Best Machine Learning Courses

Find the best online Machine Learning Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Machine Learning Courses.

Machine Learning A-Z™: Hands-On Python & R In Data Science

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

Created by Kirill Eremenko - Data Scientist

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Students: 786116, Price: $94.99

Students: 786116, Price:  Paid

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

  • Part 1 - Data Preprocessing

  • Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

  • Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

  • Part 4 - Clustering: K-Means, Hierarchical Clustering

  • Part 5 - Association Rule Learning: Apriori, Eclat

  • Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

  • Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP

  • Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

  • Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA

  • Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

Important updates (June 2020):

  • CODES ALL UP TO DATE

  • DEEP LEARNING CODED IN TENSORFLOW 2.0

  • TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!

Python for Data Science and Machine Learning Bootcamp

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

Created by Jose Portilla - Head of Data Science, Pierian Data Inc.

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Students: 463995, Price: $89.99

Students: 463995, Price:  Paid

Are you ready to start your path to becoming a Data Scientist! 

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

  • Programming with Python
  • NumPy with Python
  • Using pandas Data Frames to solve complex tasks
  • Use pandas to handle Excel Files
  • Web scraping with python
  • Connect Python to SQL
  • Use matplotlib and seaborn for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with SciKit Learn, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Natural Language Processing
  • Neural Nets and Deep Learning
  • Support Vector Machines
  • and much, much more!

Enroll in the course and become a data scientist today!

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

Created by Sundog Education by Frank Kane - Founder, Sundog Education. Machine Learning Pro

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Students: 150695, Price: $89.99

Students: 150695, Price:  Paid

New! Updated for 2021 with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's)

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!

If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including:

  • Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras

  • Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's)

  • Data Visualization in Python with MatPlotLib and Seaborn

  • Transfer Learning

  • Sentiment analysis

  • Image recognition and classification

  • Regression analysis

  • K-Means Clustering

  • Principal Component Analysis

  • Train/Test and cross validation

  • Bayesian Methods

  • Decision Trees and Random Forests

  • Multiple Regression

  • Multi-Level Models

  • Support Vector Machines

  • Reinforcement Learning

  • Collaborative Filtering

  • K-Nearest Neighbor

  • Bias/Variance Tradeoff

  • Ensemble Learning

  • Term Frequency / Inverse Document Frequency

  • Experimental Design and A/B Tests

  • Feature Engineering

  • Hyperparameter Tuning

...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster.

If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs.

If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

  • "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD

Introduction to Data Science using Python (Module 1/3)

Learn Data science / Machine Learning using Python (Scikit Learn)

Created by Rakesh Gopalakrishnan - Over 260,000 Students

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Students: 120642, Price: Free

Students: 120642, Price:  Free

Are you completely new to Data science?

Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don't know what this is?

Do you want to start or switch career to Data Science and analytics?

If yes, then I have a new course for you. In this course, I cover the absolute basics of Data Science and Machine learning. This course will not cover in-depth algorithms. I have split this course into 3 Modules. This module, takes a 500,000ft. view of what Data science is and how is it used. We will go through commonly used terms and write some code in Python. I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started. I will be using Python and Scikit-Learn Package in this course. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.

This course will the first data science course in a series of courses. Consider this course as a 101 level course, where I don't go too much deep into any particular statistical area, but rather just cover enough to raise your curiosity in the field of Data Science and Analytics.

The other modules will cover more complex concepts. 

Mathematical Foundations of Machine Learning

Essential Linear Algebra and Calculus Hands-On in NumPy, TensorFlow, and PyTorch

Created by Dr Jon Krohn - Chief Data Scientist and #1 Bestselling Author

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Students: 81284, Price: $99.99

Students: 81284, Price:  Paid

To be a good data scientist, you need to know how to use machine learning libraries such as Scikit-learn, TensorFlow, and PyTorch to solve whatever problem you have at hand.

To be an excellent data scientist, you need to know how those libraries and algorithms work under the hood. This is where our Mathematical Foundations of Machine Learning comes in.

Led by deep learning guru Dr. Jon Krohn, this course provides a firm grasp of the mathematics — namely the linear algebra and calculus — that underlies machine learning algorithms and data science models.

The course is broken down into the following sections:

  1. Linear Algebra Data Structures

  2. Tensor Operations

  3. Matrix Properties

  4. Eigenvectors and Eigenvalues

  5. Matrix Operations for Machine Learning

  6. Limits

  7. Derivatives and Differentiation

  8. Automatic Differentiation

We have finished filming additional content on calculus (Sections 9 and 10), which will be edited and uploaded in the summer of 2021. At that point, the Mathematical Foundations of Machine Learning course could be considered complete, but we will continue adding related bonus content — on probability, statistics, data structures, and optimization — as quickly as we can. Enrollment now includes free, unlimited access to all of this future course content — over 25 hours in total.

Throughout each of the sections, you'll find plenty of hands-on assignments, Python code demos, and practical exercises to get your math game in top form!

Are you ready to become an outstanding data scientist? See you in the classroom.

Course Prerequisites

Programming: All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the code examples.

Mathematics: Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information — such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics.

Data Science and Machine Learning Bootcamp with R

Learn how to use the R programming language for data science and machine learning and data visualization!

Created by Jose Portilla - Head of Data Science, Pierian Data Inc.

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Students: 71052, Price: $89.99

Students: 71052, Price:  Paid

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We'll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R! Here a just a few of the topics we will be learning:

  • Programming with R
  • Advanced R Features
  • Using R Data Frames to solve complex tasks
  • Use R to handle Excel Files
  • Web scraping with R
  • Connect R to SQL
  • Use ggplot2 for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with R, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Data Mining Twitter
  • Neural Nets and Deep Learning
  • Support Vectore Machines
  • and much, much more!

Enroll in the course and become a data scientist today!

Data Science, Machine Learning, Data Analysis, Python & R

FREE Course on Data Science, Machine Learning, Data Analysis, Data Visualization using Python and R Programming

Created by DATAhill Solutions Srinivas Reddy - Data Scientist

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Students: 65970, Price: Free

Students: 65970, Price:  Free

Interested in the field of Data Science, Machine Learning, Data Analytics, Data Visualization? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Data Science. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

Introduction to Machine Learning for Data Science

A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.

Created by David Valentine - The Backyard Data Scientist

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Students: 48571, Price: $89.99

Students: 48571, Price:  Paid

Course Most Recently Updated Nov/2018! 

Thank you all for the huge response to this emerging course!  We are delighted to have over 20,000 students in over 160 different countries.  I'm genuinely touched by the overwhelmingly positive and thoughtful reviews.  It's such a privilege to share and introduce this important topic with everyday people in a clear and understandable way. 

I'm also excited to announce that I have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL students!)... I've got you covered. 

Most importantly:

To make this course "real", we've expanded.  In November of 2018, the course went from 41 lectures and 8 sections, to 62 lectures and 15 sections!  We hope you enjoy the new content!  

Unlock the secrets of understanding Machine Learning for Data Science!

In this introductory course, the “Backyard Data Scientist” will guide you through wilderness of Machine Learning for Data Science.  Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come.

Our exotic journey will include the core concepts of:

  • The train wreck definition of computer science and one that will actually instead make sense. 

     

  • An explanation of data that will have you seeing data everywhere that you look!

     

  • One of the “greatest lies” ever sold about the future computer science.

     

  • A genuine explanation of Big Data, and how to avoid falling into the marketing hype.

     

  • What is Artificial intelligence?  Can a computer actually think?  How do computers do things like navigate like a GPS or play games anyway?

     

  • What is Machine Learning?  And if a computer can think – can it learn? 

     

  • What is Data Science, and how it relates to magical unicorns!

     

  • How Computer Science, Artificial Intelligence, Machine Learning, Big Data and Data Science interrelate to one another. 

We’ll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science:

  • How a perfect storm of data, computer and Machine Learning algorithms have combined together to make this important right now.

     

  • We’ll actually make sense of how computer technology has changed over time while covering off a journey from 1956 to 2014.  Do you have a super computer in your home?  You might be surprised to learn the truth.

     

  • We’ll discuss the kinds of problems Machine Learning solves, and visually explain regression, clustering and classification in a way that will intuitively make sense.

     

  • Most importantly we’ll show how this is changing our lives.  Not just the lives of business leaders, but most importantly…you too!

To make sense of the Machine part of Machine Learning, we’ll explore the Machine Learning process:

  • How do you solve problems with Machine Learning and what are five things you must do to be successful?

     

  • How to ask the right question, to be solved by Machine Learning.

     

  • Identifying, obtaining and preparing the right data … and dealing with dirty data!

     

  • How every mess is “unique” but that tidy data is like families! 

     

  • How to identify and apply Machine Learning algorithms, with exotic names like “Decision Trees”, “Neural Networks” “K’s Nearest Neighbors” and “Naive Bayesian Classifiers”

     

  • And the biggest pitfalls to avoid and how to tune your Machine Learning models to help ensure a successful result for Data Science.

Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete.  We’ll explore:

  • How to start applying Machine Learning without losing your mind.

     

  • What equipment Data Scientists use, (the answer might surprise you!)

     

  • The top five tools Used for data science, including some surprising ones. 

     

  • And for each of the top five tools – we’ll explain what they are, and how to get started using them. 

     

  • And we’ll close off with some cautionary tales, so you can be the most successful you can be in applying Machine Learning to Data Science problems.

Bonus Course!  To make this “really real”, I’ve included a bonus course!

Most importantly in the bonus course I’ll include information at the end of every section titled “Further Magic to Explore” which will help you to continue your learning experience. 

In this bonus course we’ll explore:

  • Creating a real live Machine Learning Example of Titanic proportions.  That’s right – we are going to predict survivability onboard the Titanic!

  • Use Anaconda Jupyter and python 3.x

  • A crash course in python - covering all the core concepts of Python you need to make sense of code examples that follow. See the included free cheat sheet!

  • Hands on running Python! (Interactively, with scripts, and with Jupyter)

  • Basics of how to use Jupyter Notebooks

  • Reviewing and reinforcing core concepts of Machine Learning (that we’ll soon apply!)

  • Foundations of essential Machine Learning and Data Science modules:

    • NumPy – An Array Implementation

    • Pandas – The Python Data Analysis Library

    • Matplotlib – A plotting library which produces quality figures in a variety of formats

    • SciPy – The fundamental Package for scientific computing in Python

    • Scikit-Learn – Simple and efficient tools data mining, data analysis, and Machine Learning

  • In the titanic hands on example we’ll follow all the steps of the Machine Learning workflow throughout:

    • 1. Asking the right question.

    • 2. Identifying, obtaining, and preparing the right data

    • 3. Identifying and applying a Machine Learning algorithm

    • 4. Evaluating the performance of the model and adjusting

    • 5. Using and presenting the model

  • We’ll also see a real world example of problems in Machine learning, including underfit and overfit.

    The bonus course finishes with a conclusion and further resources to continue your Machine Learning journey. 

So I invite you to join me, the Backyard Data Scientist on an exquisite journey into unlocking the secrets of Machine Learning for Data Science.... for you know - everyday people... like you!

Sign up right now, and we'll see you – on the other side!

Complete Machine Learning & Data Science Bootcamp 2021

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

Created by Andrei Neagoie - Senior Software Developer / Founder of zerotomastery.io

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Students: 45901, Price: $89.99

Students: 45901, Price:  Paid

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies. You will go from zero to mastery!

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.

The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:

- Data Exploration and Visualizations

- Neural Networks and Deep Learning

- Model Evaluation and Analysis

- Python 3

- Tensorflow 2.0

- Numpy

- Scikit-Learn

- Data Science and Machine Learning Projects and Workflows

- Data Visualization in Python with MatPlotLib and Seaborn

- Transfer Learning

- Image recognition and classification

- Train/Test and cross validation

- Supervised Learning: Classification, Regression and Time Series

- Decision Trees and Random Forests

- Ensemble Learning

- Hyperparameter Tuning

- Using Pandas Data Frames to solve complex tasks

- Use Pandas to handle CSV Files

- Deep Learning / Neural Networks with TensorFlow 2.0 and Keras

- Using Kaggle and entering Machine Learning competitions

- How to present your findings and impress your boss

- How to clean and prepare your data for analysis

- K Nearest Neighbours

- Support Vector Machines

- Regression analysis (Linear Regression/Polynomial Regression)

- How Hadoop, Apache Spark, Kafka, and Apache Flink are used

- Setting up your environment with Conda, MiniConda, and Jupyter Notebooks

- Using GPUs with Google Colab

By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.

Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems.

Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.

Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.

You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!

Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!

Taught By:

Daniel Bourke:
A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.

My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen.

I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more.

Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups.

Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims.

My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?".

Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views.

I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible.

My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know.

Questions are always welcome.

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Andrei Neagoie:
Andrei is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. 

Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time.   Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. 

Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. 

Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible.  

See you inside the course!

2021 Python for Machine Learning & Data Science Masterclass

Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!

Created by Jose Portilla - Head of Data Science, Pierian Data Inc.

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Students: 31527, Price: $94.99

Students: 31527, Price:  Paid

This is the most complete course online for learning about Python, Data Science, and Machine Learning. Join Jose Portilla's over 2 million students to learn about the future today!

What is in the course?

Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I've worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python!

This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning. The typical starting salary for a data scientists can be over $150,000 dollars, and we've created this course to help guide students to learning a set of skills to make them extremely hirable in today's workplace environment.

We'll cover everything you need to know for the full data science and machine learning tech stack required at the world's top companies. Our students have gotten jobs at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech companies! We've structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms.

We cover advanced machine learning algorithms that most other courses don't! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN.

This comprehensive course is designed to be on par with Bootcamps that usually cost thousands of dollars and includes the following topics:

  • Programming with Python

  • NumPy with Python

  • Deep dive into Pandas for Data Analysis

  • Full understanding of Matplotlib Programming Library

  • Deep dive into seaborn for data visualizations

  • Machine Learning with SciKit Learn, including:

    • Linear Regression

    • Regularization

    • Lasso Regression

    • Ridge Regression

    • Elastic Net

    • K Nearest Neighbors

    • K Means Clustering

    • Decision Trees

    • Random Forests

    • Natural Language Processing

    • Support Vector Machines

    • Hierarchal Clustering

    • DBSCAN

    • PCA

    • Model Deployment

    • and much, much more!

As always, we're grateful for the chance to teach you data science, machine learning, and python and hope you will join us inside the course to boost your skillset!

-Jose and Pierian Data Inc. Team

AWS Certified Machine Learning Specialty 2021 – Hands On!

AWS machine learning certification preparation - learn SageMaker, feature engineering, data engineering, modeling & more

Created by Sundog Education by Frank Kane - Founder, Sundog Education. Machine Learning Pro

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Students: 30375, Price: $99.99

Students: 30375, Price:  Paid

[ Updated for 2021's latest SageMaker features and new AWS ML Services. Happy learning! ]

Nervous about passing the AWS Certified Machine Learning - Specialty exam (MLS-C01)? You should be! There's no doubt it's one of the most difficult and coveted AWS certifications. A deep knowledge of AWS and SageMaker isn't enough to pass this one - you also need deep knowledge of machine learning, and the nuances of feature engineering and model tuning that generally aren't taught in books or classrooms. You just can't prepare enough for this one.

This certification prep course is taught by Frank Kane, who spent nine years working at Amazon itself in the field of machine learning. Frank took and passed this exam on the first try, and knows exactly what it takes for you to pass it yourself. Joining Frank in this course is Stephane Maarek, an AWS expert and popular AWS certification instructor on Udemy.

In addition to the 9-hour video course, a 30-minute quick assessment practice exam is included that consists of the same topics and style as the real exam. You'll also get four hands-on labs that allow you to practice what you've learned, and gain valuable experience in model tuning, feature engineering, and data engineering.

This course is structured into the four domains tested by this exam: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Just some of the topics we'll cover include:

  • S3 data lakes

  • AWS Glue and Glue ETL

  • Kinesis data streams, firehose, and video streams

  • DynamoDB

  • Data Pipelines, AWS Batch, and Step Functions

  • Using scikit_learn

  • Data science basics

  • Athena and Quicksight

  • Elastic MapReduce (EMR)

  • Apache Spark and MLLib

  • Feature engineering (imputation, outliers, binning, transforms, encoding, and normalization)

  • Ground Truth

  • Deep Learning basics

  • Tuning neural networks and avoiding overfitting

  • Amazon SageMaker, including SageMaker Studio, SageMaker Model Monitor, SageMaker Autopilot, and SageMaker Debugger.

  • Regularization techniques

  • Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)

  • High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more

  • Security best practices with machine learning on AWS

Machine learning is an advanced certification, and it's best tackled by students who have already obtained associate-level certification in AWS and have some real-world industry experience. This exam is not intended for AWS beginners.

If there's a more comprehensive prep course for the AWS Certified Machine Learning - Specialty exam, we haven't seen it. Enroll now, and gain confidence as you walk into that testing center.

Complete 2020 Data Science & Machine Learning Bootcamp

Learn Python, Tensorflow, Deep Learning, Regression, Classification, Neural Networks, Artificial Intelligence & more!

Created by Philipp Muellauer - Data Scientist | Android Developer | Teacher

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Students: 26104, Price: $89.99

Students: 26104, Price:  Paid

Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science.

At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:

  • The course is a taught by the lead instructor at the App Brewery, London's leading in-person programming bootcamp.

  • In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.

  • This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.

  • The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.

  • To date, we’ve taught over 200,000 students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.

  • You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.

We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.

The course includes over 35 hours of HD video tutorials and builds your programming knowledge while solving real-world problems.

In the curriculum, we cover a large number of important data science and machine learning topics, such as:

  • Data Cleaning and Pre-Processing

  • Data Exploration and Visualisation

  • Linear Regression

  • Multivariable Regression

  • Optimisation Algorithms and Gradient Descent

  • Naive Bayes Classification

  • Descriptive Statistics and Probability Theory

  • Neural Networks and Deep Learning

  • Model Evaluation and Analysis

  • Serving a Tensorflow Model

Throughout the course, we cover all the tools used by data scientists and machine learning experts, including:

  • Python 3

  • Tensorflow

  • Pandas

  • Numpy

  • Scikit Learn

  • Keras

  • Matplotlib

  • Seaborn

  • SciPy

  • SymPy

By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. We’ll be covering all of these Python programming concepts:

  • Data Types and Variables

  • String Manipulation

  • Functions

  • Objects

  • Lists, Tuples and Dictionaries

  • Loops and Iterators

  • Conditionals and Control Flow

  • Generator Functions

  • Context Managers and Name Scoping

  • Error Handling

By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer.

Sign up today, and look forward to:

  • 178+ HD Video Lectures

  • 30+ Code Challenges and Exercises

  • Fully Fledged Data Science and Machine Learning Projects

  • Programming Resources and Cheatsheets

  • Our best selling 12 Rules to Learn to Code eBook

  • $12,000+ data science & machine learning bootcamp course materials and curriculum

Don't just take my word for it, check out what existing students have to say about my courses:

“One of the best courses I have taken. Everything is explained well, concepts are not glossed over. There is reinforcement in the challenges that helps solidify understanding. I'm only half way through but I feel like it is some of the best money I've ever spent.” -Robert Vance

“I've spent £27,000 on University..... Save some money and buy any course available by Philipp! Great stuff guys.” -Terry Woodward

"This course is amazingly immersive and quite all-inclusive from end-to-end to develop an app! Also gives practicality to apply the lesson straight away and full of fun with bunch of sense of humor, so it's not boring to follow throughout the whole course. Keep up the good work guys!" - Marvin Septianus

“Great going so far. Like the idea of the quizzes to challenge us as we go along. Explanations are clear and easy to follow” -Lenox James

“Very good explained course. The tasks and challenges are fun to do learn an do! Would recommend it a thousand times.” -Andres Ariza

“I enjoy the step by step method they introduce the topics. Anyone with an interest in programming would be able to follow and program” -Isaac Barnor

“I am learning so much with this course; certainly beats reading older Android Ebooks that are so far out of date; Phillippe is so easy any understandable to learn from. Great Course have recommended to a few people.” -Dale Barnes

“This course has been amazing. Thanks for all the info. I'll definitely try to put this in use. :)” -Devanshika Ghosh

“Great Narration and explanations. Very interactive lectures which make me keep looking forward to the next tutorial” -Bimal Becks

“English is not my native language but in this video, Phillip has great pronunciation so I don't have problem even without subtitles :)” -Dreamerx85

“Clear, precise and easy to follow instructions & explanations!” -Andreea Andrei

“An incredible course in a succinct, well-thought-out, easy to understand package. I wish I had purchased this course first.” -Ian

REMEMBER… I'm so confident that you'll love this course that we're offering a FULL money back guarantee for 30 days! So it's a complete no-brainer, sign up today with ZERO risks and EVERYTHING to gain.

So what are you waiting for? Click the buy now button and join the world's best data science and machine learning course.

Bayesian Machine Learning in Python: A/B Testing

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More

Created by Lazy Programmer Inc. - Artificial intelligence and machine learning engineer

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Students: 25965, Price: $89.99

Students: 25965, Price:  Paid

This course is all about A/B testing.

A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more.

A/B testing is all about comparing things.

If you’re a data scientist, and you want to tell the rest of the company, “logo A is better than logo B”, well you can’t just say that without proving it using numbers and statistics.

Traditional A/B testing has been around for a long time, and it’s full of approximations and confusing definitions.

In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.

First, we’ll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.

You’ll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.

We’ll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.

Finally, we’ll improve on both of those by using a fully Bayesian approach.

Why is the Bayesian method interesting to us in machine learning?

It’s an entirely different way of thinking about probability.

It’s a paradigm shift.

You’ll probably need to come back to this course several times before it fully sinks in.

It’s also powerful, and many machine learning experts often make statements about how they “subscribe to the Bayesian school of thought”.

In sum - it’s going to give us a lot of powerful new tools that we can use in machine learning.

The things you’ll learn in this course are not only applicable to A/B testing, but rather, we’re using A/B testing as a concrete example of how Bayesian techniques can be applied.

You’ll learn these fundamental tools of the Bayesian method - through the example of A/B testing - and then you’ll be able to carry those Bayesian techniques to more advanced machine learning models in the future.

See you in class!

"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

  • After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

  • Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)

  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy, Scipy, Matplotlib

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Deep Learning Prerequisites: The Numpy Stack in Python V2

Numpy, Scipy, Pandas, and Matplotlib: prep for deep learning, machine learning, and artificial intelligence

Created by Lazy Programmer Team - Artificial Intelligence and Machine Learning Engineer

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Students: 25862, Price: Free

Students: 25862, Price:  Free

Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2).

The reason I made this course is because there is a huge gap for many students between machine learning "theory" and writing actual code.

As I've always said: "If you can't implement it, then you don't understand it".

Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer.

This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.

The goal is that, after you take this course, you will learn about machine learning algorithms, and implement those algorithms in code using the tools and techniques you learned in this course.

Suggested Prerequisites:

  • linear algebra

  • probability

  • Python programming

50 Must Know Concepts,Algorithms in Machine Learning

Introduction to 50 Must know Topics of Machine Learning,Data science. Understand machine Learning Syllabus.

Created by TheMachineLearning.Org . - Machine Learning Engineer

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Students: 25274, Price: Free

Students: 25274, Price:  Free

This course is designed to give you introduction to syllabus of machine learning. If you want to get started with machine learning then this course will help you. It helps you to get ready for an interview with 50 concepts covering varied range of topics. The course is intended not only for candidates with a full understanding of Machine Learning but also for recalling knowledge in data science.

DP-100: A-Z Machine Learning using Azure Machine Learning

Microsoft Azure DP-100: Designing and Implementing a Data Science Solution Exam Covered. Learn Azure Machine Learning

Created by Jitesh Khurkhuriya - Data Scientist and Digital Transformation Consultant

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Students: 24150, Price: $99.99

Students: 24150, Price:  Paid

This course will help you and your team to build skills required to pass the most in demand and challenging, Azure DP-100 Certification exam. It will earn you one of the most in-demand certificate of Microsoft Certified: Azure Data Scientist Associate.

DP-100 is designed for Data Scientists. This exam tests your knowledge of Data Science and Machine learning to implement machine learning models on Azure. So you must know right from Machine Learning fundamentals, Python, planning and creating suitable environments in Azure, creating machine learning models as well as deploying them in production.

Why should you go for DP-100 Certification?

  • One of the very few certifications in the field of Data Science and Machine Learning.

  • You can successfully demonstrate your knowledge and abilities in the field of Data Science and Machine Learning.

  • You will improve your job prospects substantially in the field of Data Science and Machine Learning.

Key points about this course

  • Covers the most current syllabus as on May, 2021.

  • 100% syllabus of DP-100 Exam is covered.

  • Very detailed and comprehensive coverage with more than 200 lectures and 25 Hours of content

  • Crash courses on Python and Azure Fundamentals for those who are new to the world of Data Science

Machine Learning is one of the hottest and top paying skills. It's also one of the most interesting field to work on.

In this course of Machine Learning using Azure Machine Learning, we will make it even more exciting and fun to learn, create and deploy machine learning models using Azure Machine Learning Service as well as the Azure Machine Learning Studio. We will go through every concept in depth. This course not only teaches basic but also the advance techniques of Data processing, Feature Selection and Parameter Tuning which an experienced and seasoned Data Science expert typically deploys. Armed with these techniques, in a very short time, you will be able to match the results that an experienced data scientist can achieve.

This course will help you prepare for the entry to this hot career path of Machine Learning as well as the Azure DP-100: Azure Data Scientist Associate exam.

----- Exam Syllabus for DP-100 Exam -----

1. Set up an Azure Machine Learning Workspace (30-35%)

Create an Azure Machine Learning workspace

  • Create an Azure Machine Learning workspaceConfigure workspace settings

  • Manage a workspace by using Azure Machine Learning studio

Manage data objects in an Azure Machine Learning workspace

  • Register and maintain datastores

  • Create and manage datasets

Manage experiment compute contexts

  • Create a compute instance

  • Determine appropriate compute specifications for a training workload

  • Create compute targets for experiments and training

Run Experiments and Train Models (25-30%)

Create models by using Azure Machine Learning Designer

  • Create a training pipeline by using Azure Machine Learning designer

  • Ingest data in a designer pipeline

  • Use designer modules to define a pipeline data flow

  • Use custom code modules in designer

Run training scripts in an Azure Machine Learning workspace

  • Create and run an experiment by using the Azure Machine Learning SDK

  • Configure run settings for a script

  • Consume data from a dataset in an experiment by using the Azure Machine Learning SDK

Generate metrics from an experiment run

  • Log metrics from an experiment run

  • Retrieve and view experiment outputs

  • Use logs to troubleshoot experiment run errors

Automate the model training process

  • Create a pipeline by using the SDK

  • Pass data between steps in a pipeline

  • Run a pipeline

  • Monitor pipeline runs

Optimize and Manage Models (20-25%)

Use Automated ML to create optimal models

  • Use the Automated ML interface in Azure Machine Learning studio

  • Use Automated ML from the Azure Machine Learning SDK

  • Select pre-processing options

  • Determine algorithms to be searched

  • Define a primary metric

  • Get data for an Automated ML run

  • Retrieve the best model

Use Hyperdrive to tune hyperparameters

  • Select a sampling method

  • Define the search space

  • Define the primary metric

  • Define early termination options

  • Find the model that has optimal hyperparameter values

Use model explainers to interpret models

  • Select a model interpreter

  • Generate feature importance data

Manage models

  • Register a trained model

  • Monitor model usage

  • Monitor data drift

Deploy and Consume Models (20-25%)

Create production compute targets

  • Consider security for deployed services

  • Evaluate compute options for deployment

Deploy a model as a service

  • Configure deployment settings

  • Consume a deployed service

  • Troubleshoot deployment container issues

Create a pipeline for batch inferencing

  • Publish a batch inferencing pipeline

  • Run a batch inferencing pipeline and obtain outputs

Publish a designer pipeline as a web service

  • Create a target compute resource

  • Configure an Inference pipeline

  • Consume a deployed endpoint

Some feedback from previous students,

  1. "The instructor explained every concept smoothly and clearly. I'm an acountant without tech background nor excellent statistical knowledge. I do really appreciate these helpful on-hand labs and lectures. Passed the DP-100 in Dec 2020. This course really help."

  2. "Cleared DP-100 today with the help of this course. I would say this is the one of the best course to get in depth knowledge about Azure machine learning and clear the DP-100 with ease. Thank you Jitesh and team for this wonderful tutorial which helped me clear the certification."

  3. "The instructor explained math concept clearly. These math concepts are necessary as fundation of machine learning, and also are very helpful for studying DP-100 exam concepts. Passed DP-100."

I am committed to and invested in your success. I have always provided answers to all the questions and not a single question remains unanswered for more than a few days. The course is also regularly updated with newer features.

Learning data science and then further deploying Machine Learning Models have been difficult in the past. To make it easier, I have explained the concepts using very simple and day-to-day examples. Azure ML is Microsoft's way of democratizing Machine Learning. We will use this revolutionary tool to implement our models. Once learnt, you will be able to create and deploy machine learning models in less than an hour using Azure Machine Learning Studio.

Azure Machine Learning Studio is a great tool to learn to build advance models without writing a single line of code using simple drag and drop functionality. Azure Machine Learning (AzureML) is considered as a game changer in the domain of Data Science and Machine Learning.

This course has been designed keeping in mind entry level Data Scientists or no background in programming. This course will also help the data scientists to learn the AzureML tool. You can skip some of the initial lectures or run them at 2x speed, if you are already familiar with the concepts or basics of Machine Learning.

The course is very hands on and you will be able to develop your own advance models while learning,

  • Advance Data Processing methods

  • Statistical Analysis of the data using Azure Machine Learning Modules

  • MICE or Multiple Imputation By Chained Equation

  • SMOTE or Synthetic Minority Oversampling Technique

  • PCA; Principal Component Analysis

  • Two class and multiclass classifications

  • Logistic Regression

  • Decision Trees

  • Linear Regression

  • Support Vector Machine (SVM)

  • Understanding how to evaluate and score models

  • Detailed Explanation of input parameters to the models

  • How to choose the best model using Hyperparameter Tuning

  • Deploy your models as a webservice using Azure Machine Learning Studio

  • Cluster Analysis

  • K-Means Clustering

  • Feature selection using Filter-based as well as Fisher LDA of AzureML Studio

  • Recommendation system using one of the most powerful recommender of Azure Machine Learning

  • All the slides and reference material for offline reading

You will learn and master, all of the above even if you do not have any prior knowledge of programming.

This course is a complete Machine Learning course with basics covered. We will not only build the models but also explain various parameters of all those models and where we can apply them.

We would also look at

  • Steps for building an ML model.

  • Supervised and Unsupervised learning

  • Understanding the data and pre-processing

  • Different model types

  • The AzureML Cheat Sheet.

  • How to use Classification and Regression

  • What is clustering or cluster analysis

KDNuggets one of the leading forums on Data Science calls Azure Machine Learning as the next big thing in Machine Learning. It further goes on to say, "people without data science background can also build data models through drag-and-drop gestures and simple data flow diagrams."

Azure Machine Learning's library has many pre-built models that you can re-use as well as deploy them.

So, hit the enroll button and I will see you inside the course.

Best-

Machine Learning with Javascript

Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects.

Created by Stephen Grider - Engineering Architect

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Students: 23770, Price: $124.99

Students: 23770, Price:  Paid

If you're here, you already know the truth: Machine Learning is the future of everything.

In the coming years, there won't be a single industry in the world untouched by Machine Learning.  A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change.  You probably already use apps many times each day that rely upon Machine Learning techniques.  So why stay in the dark any longer?

There are many courses on Machine Learning already available.  I built this course to be the best introduction to the topic.  No subject is left untouched, and we never leave any area in the dark.  If you take this course, you will be prepared to enter and understand any sub-discipline in the world of Machine Learning.

A common question - Why Javascript?  I thought ML was all about Python and R?

The answer is simple - ML with Javascript is just plain easier to learn than with Python.  Although it is immensely popular, Python is an 'expressive' language, which is a code-word that means 'a confusing language'.  A single line of Python can contain a tremendous amount of functionality; this is great when you understand the language and the subject matter, but not so much when you're trying to learn a brand new topic.

Besides Javascript making ML easier to understand, it also opens new horizons for apps that you can build.  Rather than being limited to deploying Python code on the server for running your ML code, you can build single-page apps, or even browser extensions that run interesting algorithms, which can give you the possibility of developing a completely novel use case!

Does this course focus on algorithms, or math, or Tensorflow, or what?!?!

Let's be honest - the vast majority of ML courses available online dance around the confusing topics.  They encourage you to use pre-build algorithms and functions that do all the heavy lifting for you.  Although this can lead you to quick successes, in the end it will hamper your ability to understand ML.  You can only understand how to apply ML techniques if you understand the underlying algorithms.

That's the goal of this course - I want you to understand the exact math and programming techniques that are used in the most common ML algorithms.  Once you have this knowledge, you can easily pick up new algorithms on the fly, and build far more interesting projects and applications than other engineers who only understand how to hand data to a magic library.

Don't have a background in math?  That's OK! I take special care to make sure that no lecture gets too far into 'mathy' topics without giving a proper introduction to what is going on.

A short list of what you will learn:

  • Advanced memory profiling to enhance the performance of your algorithms

  • Build apps powered by the powerful Tensorflow JS library

  • Develop programs that work either in the browser or with Node JS

  • Write clean, easy to understand ML code, no one-name variables or confusing functions

  • Pick up the basics of Linear Algebra so you can dramatically speed up your code with matrix-based operations. (Don't worry, I'll make the math easy!)

  • Comprehend how to twist common algorithms to fit your unique use cases

  • Plot the results of your analysis using a custom-build graphing library

  • Learn performance-enhancing strategies that can be applied to any type of Javascript code

  • Data loading techniques, both in the browser and Node JS environments

Cluster Analysis and Unsupervised Machine Learning in Python

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.

Created by Lazy Programmer Team - Artificial Intelligence and Machine Learning Engineer

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Students: 21880, Price: $29.99

Students: 21880, Price:  Paid

Cluster analysis is a staple of unsupervised machine learning and data science.

It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning.

In a real-world environment, you can imagine that a robot or an artificial intelligence won’t always have access to the optimal answer, or maybe there isn’t an optimal correct answer. You’d want that robot to be able to explore the world on its own, and learn things just by looking for patterns.

Do you ever wonder how we get the data that we use in our supervised machine learning algorithms?

We always seem to have a nice CSV or a table, complete with Xs and corresponding Ys.

If you haven’t been involved in acquiring data yourself, you might not have thought about this, but someone has to make this data!

Those “Y”s have to come from somewhere, and a lot of the time that involves manual labor.

Sometimes, you don’t have access to this kind of information or it is infeasible or costly to acquire.

But you still want to have some idea of the structure of the data. If you're doing data analytics automating pattern recognition in your data would be invaluable.

This is where unsupervised machine learning comes into play.

In this course we are first going to talk about clustering. This is where instead of training on labels, we try to create our own labels! We’ll do this by grouping together data that looks alike.

There are 2 methods of clustering we’ll talk about: k-means clustering and hierarchical clustering.

Next, because in machine learning we like to talk about probability distributions, we’ll go into Gaussian mixture models and kernel density estimation, where we talk about how to "learn" the probability distribution of a set of data.

One interesting fact is that under certain conditions, Gaussian mixture models and k-means clustering are exactly the same! We’ll prove how this is the case.

All the algorithms we’ll talk about in this course are staples in machine learning and data science, so if you want to know how to automatically find patterns in your data with data mining and pattern extraction, without needing someone to put in manual work to label that data, then this course is for you.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

  • After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

  • matrix addition, multiplication

  • probability

  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy coding: matrix and vector operations, loading a CSV file

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Python AI and Machine Learning for Production & Development

Learn AI & ML using demos

Created by Techlatest .Net - Training videos on latest technologies and trends

"]

Students: 21030, Price: Free

Students: 21030, Price:  Free

When you want to learn a new technology for professional use, there are two mutually exclusive options, either you learn it yourself or you go for instructor based training.

Self learning is least expensive but lot of time results in wasting time in finding right contents, setting up the environment , troubleshooting issues and may make you give up in the middle.

Instructor based training can be expensive at times and need your time commitment.

This course combines the best of both these options. The course is based on one of the most famous books in the field "Python Machine Learning (2nd Ed.)" by Sebastian Raschka and Vahid Mirjalili and provides you video tutorials on how to understand the AI/ML concepts from the books by providing out of box virtual machine with demo examples for each chapter in the book and complete preinstalled setup to execute the code.

You learn the concepts by self learning and get hands on executing the sample code in the virtual machine.

The demo covers following concepts:

  1. Machine Learning - Giving Computers the Ability to Learn from Data

  2. Training Machine Learning Algorithms for Classification

  3. A Tour of Machine Learning Classifiers Using Scikit-Learn

  4. Building Good Training Sets – Data Pre-Processing

  5. Compressing Data via Dimensionality Reduction

  6. Learning Best Practices for Model Evaluation & Hyperparameter Optimization

  7. Combining Different Models for Ensemble Learning

  8. Applying Machine Learning to Sentiment Analysis

  9. Embedding a Machine Learning Model into a Web Application

  10. Predicting Continuous Target Variables with Regression Analysis

  11. Working with Unlabeled Data – Clustering Analysis

  12. Implementing a Multi-layer Artificial Neural Network from Scratch

  13. Parallelizing Neural Network Training with TensorFlow

  14. Going Deeper: The Mechanics of TensorFlow

  15. Classifying Images with Deep Convolutional Neural Networks

  16. Modeling Sequential Data Using Recurrent Neural Networks

In addition to the preinstalled setup and demos, the VM also comes with:

  1. Jupyter notebook for web based interactive development

  2. JupyterHub for multiuser notebook environment to allow multiple users to simultaneously do development

  3. Remote desktop

  4. Visual studio code IDE

  5. Fish Shell

The VM is available on :

  1. Google Cloud Platform

  2. AWS

  3. Microsoft Azure

AWS Certified Machine Learning Specialty (MLS-C01)

Hands on AWS SageMaker Course with Practice Test

Created by Chandra Lingam - Cloud Wave LLC

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Students: 20205, Price: $129.99

Students: 20205, Price:  Paid

Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep

*** JUL-2021 New Endpoint focused section on how to make SageMaker Endpoint Changes with Zero Downtime ***

*** JUN-2021 Lab notebook now use spot-training as the default option. Save over 60% in training costs ***

*** NOV-2020 NEW: Nuts and Bolts of Optimization, quizzes ***

*** NOV-2020 All code examples and Labs were updated to use version 2.x of the SageMaker Python SDK ***

*** SEP-2020 Anomaly Detection with Random Cut Forest - Learn the intuition behind anomaly detection using Random Cut Forest.  With labs. ***

*** APR-2020 Bring Your Own Algorithm - We take a behind the scene look at the SageMaker Training and Hosting Infrastructure for your own algorithms. With Labs ***

*** JAN-2020 Timed Practice Test and additional lectures for Exam Preparation added

For  Practice Test, look for the section: 2020 Practice Exam - AWS Certified Machine Learning Specialty

For exam overview, gap analysis and preparation strategy, look for 2020 - Overview - AWS Machine Learning Specialty Exam

***

Benefits

There are several courses on Machine Learning and AI. What is unique about this course?

Here are the top reasons:

1. Cloud-based machine learning keeps you focused on the current best practices.

2. In this course, you will learn the most useful algorithms.  Don’t waste your time sifting through mountains of techniques that are in the wild

4. Cloud-based service is straightforward to integrate with your application and has support for a wide variety of programming languages.

5. Whether you have small data or big data, the elastic nature of the AWS cloud allows you to handle them all.

6. There is also No upfront cost or commitment – Pay only for what you need and use

Hands-on Labs

In this course, you will learn with hands-on labs and work on exciting and challenging problems

What exactly will you learn in this course?

Here are the things that you will learn in this course:

AWS SageMaker

* You will learn how to deploy a Notebook instance on the AWS Cloud.

* You will gain insight into algorithms provided by SageMaker service

* Learn how to train, optimize and deploy your models

AI Services

In the AI Services section of this course,

* You will learn about a set of pre-trained services that you can directly integrate with your application.

* Within a few minutes, you can build image and video analysis applications – like face recognition

* You can develop solutions for natural language processing, like finding sentiment, text translation, and conversational chatbots.

Integration

* Learning algorithms is one part of the story - You need to know how to integrate the trained models in your application.

* You will learn how to host your models, scale on-demand, handle failures

* Provide a clean interface for the applications using Lambda and API Gateway

Data Lake

* Data management is one of the most complex and time-consuming activities when working on machine learning projects.

* With AWS, you have a variety of powerful tools for ingesting, cataloging, transforming, securing, visualization of your data assets.

* We will build a data lake solution in this course.

Machine Learning Certification

* If you are planning to get AWS Machine Learning Specialty Certification, you will find all the resources that you need to pass the exam in this course.

Timed Practice Exam and Quizzes

Source Code

* The source code for this course available on Git and that ensures you always get the latest code

Ideal Student

* The ideal student for this course is willing to learn, participate in the course Q&A forum when you need help, and you need to be comfortable coding in Python.

Author

My name is Chandra Lingam, and I am the instructor for this course.

I have over 50,000 thousand students

I spend a considerable amount of time keeping myself up-to-date and teach cloud technologies from the basics.

I have the following AWS Certifications: Solutions Architect, Developer, SysOps, Solutions Architect Professional, Machine Learning Specialty.

I am looking forward to meeting you.

Thank you!

Learn Python with Google Colab – A Step to Machine Learning

Hands on course in python basics with Google Colab, a step towards Machine Learning

Created by Rahul Jha - IT Professional, Blogger and Research Scholar

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Students: 16814, Price: Free

Students: 16814, Price:  Free

This course is completely practical based and is per-requisite for our upcoming Machine Learning course. With around 25 lectures, this course is designed in such a way that you can take spark of Google Colab enabling Jupiter notebook , the best platform to practice Machine Learning  and is enriched with all the basic concepts that is required to start with python programming. After completing this course, you should have basic python understanding.

Machine Learning Practical: 6 Real-World Applications

Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python

Created by Dr. Ryan Ahmed, Ph.D., MBA - Professor & Best-selling Udemy Instructor, 200K+ students

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Students: 16428, Price: $89.99

Students: 16428, Price:  Paid

So you know the theory of Machine Learning and know how to create your first algorithms. Now what? 

There are tons of courses out there about the underlying theory of Machine Learning which don’t go any deeper – into the applications.

This course is not one of them.

Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges?  

Then welcome to “Machine Learning Practical”.

We gathered best industry professionals with tons of completed projects behind.

Each presenter has a unique style, which is determined by his experience, and like in a real world, you will need adjust to it if you want successfully complete this course. We will leave no one behind!

This course will demystify how real Data Science project looks like. Time to move away from these polished examples which are only introducing you to the matter, but not giving any real experience.

If you are still dreaming where to learn Machine Learning through practice, where to take real-life projects for your CV, how to not look like a noob in the recruiter's eyes, then you came to the right place!

This course provides a hands-on approach to real-life challenges and covers exactly what you need to succeed in the real world of Data Science.

 

There are most exciting case studies including:

●      diagnosing diabetes in the early stages

●      directing customers to subscription products with app usage analysis

●      minimizing churn rate in finance

●      predicting customer location with GPS data

●      forecasting future currency exchange rates

●      classifying fashion

●      predicting breast cancer

●      and much more!

 

All real.

All true.

All helpful and applicable.

And as a final bonus:

 

In this course we will also cover Deep Learning Techniques and their practical applications.

So as you can see, our goal here is to really build the World’s leading practical machine learning course.

If your goal is to become a Machine Learning expert, you know how valuable these real-life examples really are. 

They will determine the difference between Data Scientists who just know the theory and Machine Learning experts who have gotten their hands dirty.

So if you want to get hands-on experience which you can add to your portfolio, then this course is for you.

Enroll now and we’ll see you inside.

Augmented Data Visualization with Machine Learning

Automated Analytics from Oracle Analytics Cloud and Machine Learning with Data Visualizations! Hands-on with Quizzes!

Created by Subrata Dutta - EPM and Analytics Enthusiast

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Students: 14977, Price: Free

Students: 14977, Price:  Free

Data Visualization is new Analytics and, Augmented Analytics is new Data Visualization! In this course you will work on machine learning models for predictive analytics and advanced data flow features through hands on training with Oracle Analytics. This course is designed to provide you with many hands-on activities to learn building modern data visualization projects. This is new business intelligence!

Are you a business analyst curious about what Oracle Analytics can do? Then this is the course for you. We’re assuming that you know the basics of using analytics in your business. So we designed this course for you to jump right in to a technical, hands-on product experience. Every section is packed with both video and screencast to show you each analytics capability, plus demo files and scripts to download, so you can try it yourself!

Since we can’t guess which use case you’re itching to try out, we’ve packed the course with different projects like sales analysis, school donation analysis, HR attrition analysis as well as advanced projects such as Machine Learning models for Predictive Analysis. Curious about some other application for analytics? Try it with your own data too!

As a day-to-day analyst and data visualization user, you will find this course fun and informative, try it out with your own data set! We hope that you have great time learning this exciting new data visualization capabilities.

Analytically Yours,
Your Instructors

New in Big Data: Apache HiveMall – Machine Learning with SQL

HiveMall SQL on Spark, MapReduce and Tez. Leverage your knowledge of SQL to enter Machine Learning and Big Data space.

Created by Elena Akhmatova - Data Scientist

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Students: 14129, Price: Free

Students: 14129, Price:  Free

It is widely accepted that applying Machine Learning techniques to data is a complex task that requires knowledge of a variety of programming languages and means hours of coding, compiling and debugging.  

Not any longer!

Apache HiveMall is a Machine Learning library that allows anyone with basic knowledge of SQL to run Machine Learning algorithms. 

  • No coding
  • No compiling
  • No debugging

Apache HiveMall algorithms are hidden behind Hive UDFs. This allows end user to use SQL and only SQL to apply Machine Learning algorithms to a very large volume of training data.

Apache HiveMall Machine Learning Library makes training, testing, and model evaluation easy and accessible to a much wider community of business experts than ever before.

Feature Engineering for Machine Learning

Transform the variables in your data and build better performing machine learning models

Created by Soledad Galli - Lead Data Scientist

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Students: 12960, Price: $89.99

Students: 12960, Price:  Paid

Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online.

In this course, you will learn how to engineer features and build more powerful machine learning models.

Who is this course for?

So, you’ve made your first steps into data science, you know the most commonly used prediction models, you probably built a linear regression or a classification tree model. At this stage you’re probably starting to encounter some challenges - you realize that your data set is dirty, there are lots of values missing, some variables contain labels instead of numbers, others do not meet the assumptions of the models, and on top of everything you wonder whether this is the right way to code things up. And to make things more complicated, you can’t find many consolidated resources about feature engineering. Maybe only blogs? So you may start to wonder: how are things really done in tech companies?

This course will help you! This is the most comprehensive online course in variable engineering. You will learn a huge variety of engineering techniques used worldwide in different organizations and in data science competitions, to clean and transform your data and variables.

What will you learn?

I have put together a fantastic collection of feature engineering techniques, based on scientific articles, white papers, data science competitions, and of course my own experience as a data scientist.

Specifically, you will learn:

  • How to impute your missing data

  • How to encode your categorical variables

  • How to transform your numerical variables so they meet ML model assumptions

  • How to convert your numerical variables into discrete intervals

  • How to remove outliers

  • How to handle date and time variables

  • How to work with different time zones

  • How to handle mixed variables which contain strings and numbers

Throughout the course, you are going to learn multiple techniques for each of the mentioned tasks, and you will learn to implement these techniques in an elegant, efficient, and professional manner, using Python, NumPy, Scikit-learn, pandas and a special open-source package that I created especially for this course: Feature- engine.

At the end of the course, you will be able to implement all your feature engineering steps in a single and elegant pipeline, which will allow you to put your predictive models into production with maximum efficiency.

Want to know more? Read on...

In this course, you will initially become acquainted with the most widely used techniques for variable engineering, followed by more advanced and tailored techniques, which capture information while encoding or transforming your variables. You will also find detailed explanations of the various techniques, their advantages, limitations and underlying assumptions and the best programming practices to implement them in Python.

This comprehensive feature engineering course includes over 100 lectures spanning about 10 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.

In addition, the code is updated regularly to keep up with new trends and new Python library releases.

So what are you waiting for? Enroll today, embrace the power of feature engineering and build better machine learning models.

Data Science 2021 : Complete Data Science & Machine Learning

Machine Learning A-Z, Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science

Created by Jitesh Khurkhuriya - Data Scientist and Digital Transformation Consultant

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Students: 11464, Price: $89.99

Students: 11464, Price:  Paid

Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more?

Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes.

We are going to execute following real-life projects,

  • Kaggle Bike Demand Prediction from Kaggle competition

  • Automation of the Loan Approval process

  • The famous IRIS Classification

  • Adult Income Predictions from US Census Dataset

  • Bank Telemarketing Predictions

  • Breast Cancer Predictions

  • Predict Diabetes using Prima Indians Diabetes Dataset

Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others.

As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning?

Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,

  • Understanding of the overall landscape of Data Science and Machine Learning

  • Different types of Data Analytics, Data Architecture, Deployment characteristics of Data Science and Machine Learning projects

  • Python Programming skills which is the most popular language for Data Science and Machine Learning

  • Mathematics for Machine Learning including Linear Algebra, Calculus and how it is applied in Machine Learning Algorithms as well as Data Science

  • Statistics and Statistical Analysis for Data Science

  • Data Visualization for Data Science

  • Data processing and manipulation before applying Machine Learning

  • Machine Learning

  • Ridge (L2), Lasso (L1) and Elasticnet Regression/ Regularization for Machine Learning

  • Feature Selection and Dimensionality Reduction for Machine Learning models

  • Machine Learning Model Selection using Cross Validation and Hyperparameter Tuning

  • Cluster Analysis for unsupervised Machine Learning

  • Deep Learning using most popular tools and technologies of today.

This Data Science and Machine Learning course has been designed considering all of the above aspects, the true Data Science and Machine Learning A-Z Course. In many Data Science and Machine Learning courses, algorithms are taught without teaching Python or such programming language. However, it is very important to understand the construct of the language in order to implement any discipline including Data Science and Machine Learning.

Also, without understanding the Mathematics and Statistics it's impossible to understand how some of the Data Science and Machine Learning algorithms and techniques work.

Data Science and Machine Learning is a complex set of topics which are interlinked. However, we firmly believe in what Einstein once said,

"If you can not explain it simply, you have not understood it enough."

As an instructor, I always try my level best to live up to this principle. This is one comprehensive course on Data Science and Machine Learning that teaches you everything required to learn Data Science and Machine Learning using the simplest examples with great depth.

As you will see from the preview lectures, some of the most complex topics are explained in a simple language.

Some of the key skills you will learn,

  • Python Programming

    Python has been ranked as the #1 language for Data Science and Machine Learning. It is easy to use and is rich with various libraries and functions required for performing various tasks for Data Science and Machine Learning. Moreover, it is the most preferred and default language of use for many Deep Learning frameworks including Tensorflow and Keras.

  • Advance Mathematics for Machine Learning

    Mathematics is the very basis for Data Science in general and Machine Learning in particular. Without understanding the meanings of Vectors, Matrices, their operations as well as understanding Calculus, it is not possible to understand the foundation of the Data Science and Machine Learning. Gradient Descent which forms the very basis of Neural Network and Machine Learning is built upon the basics of Calculus and Derivatives.

  • Advance Statistics for Data Science

    It is not enough to know only mean, median, mode etc. The advance techniques of Data Science and Machine Learning such as Feature Selection, Dimensionality Reduction using PCA are all based on advance inferential statistics of Distributions and Statistical Significance. It also helps us understanding the data behavior and then apply an appropriate machine learning technique to get the best result from various techniques of Data Science and Machine Learning.

  • Data Visualization

    As they say, picture is worth a thousand words. Data Visualization is one of the key techniques of Data Science and Machine Learning and is used for Exploratory Data Analysis. In that, we visually analyse the data to identify the patterns and trends. We are going to learn how to create various plots and charts as well as how to analyse them for all the practical purposes. Feature Selection plays a key role in Machine Learning and Data Visualisation is key for it.

  • Data Processing

    Data Science require extensive data processing. Data Science and Machine Learning practitioners spend more than 2/3rd of the time processing and analysing the data. Data can be noisy and is never in the best shape and form. Data Processing is one of the key disciplines of Data Science and Machine Learning to get the best results. We will be using Pandas which is the most popular library for data processing in Python and various other libraries to read, analyse, process and clean the data.

  • Machine Learning

    The heart and soul of Data Science is the predictive ability provided by the algorithms from Machine Learning and Deep Learning. Machine Learning takes the overall discipline of Data Science ahead of others. We will combine everything we would learn from the previous sections and build various machine learning models. The key aspects of the Machine Learning is not just about the algorithms but also understanding various parameters used by Machine Learning algorithms. We will understand all the key parameters and how their values impact the outcome so that you can build the best machine learning models.

  • Feature Selection and Dimensionality Reduction

    In case you wonder, what makes a good data scientists, then this section is the answer. A good Data Science and Machine Learning practitioner does not just use libraries and code few lines. She will analyse every feature of the data objectively and choose the most relevant ones based on statistical analysis. We will learn how to reduce the number of features as well as how we can retain the value in the data when we practice and build various machine learning models after applying the principles of Feature Selection and Dimensionality Reduction using PCA.

  • Deep Learning

    You can not become a good Data Science and Machine Learning practitioner, if you do not know how to build powerful neural network. Deep Learning can be said to be another kind of Machine Learning with great power and flexibility. After Learning Machine Learning, we are going to learn some key fundamentals of Deep Learning and build a solid foundation first. We will then use Keras and Tensorflow which are the most popular Deep Learning frameworks in the world.

  • Kaggle Project

    As an aspiring Data Scientists, we always wish to work on Kaggle project for Machine Learning and achieve good results. I have spent huge effort and time in making sure you understand the overall process of performing a real Data Science and Machine Learning project. This is going to be a good Machine Learning challenge for you.

Your takeaway from this course,

  1. Complete hands-on experience with huge number of Data Science and Machine Learning projects and exercises

  2. Learn the advance techniques used in the Data Science and Machine Learning

  3. Certificate of Completion for the most in demand skill of Data Science and Machine Learning

  4. All the queries answered in shortest possible time.

  5. All future updates based on updates to libraries, packages

  6. Continuous enhancements and addition of future Machine Learning course material

  7. All the knowledge of Data Science and Machine Learning at fraction of cost

This Data Science and Machine Learning course comes with the Udemy's 30-Day-Money-Back Guarantee with no questions asked.

So what you are waiting for? Hit the "Buy Now" button and get started on your Data Science and Machine Learning journey without spending much time.

I am so eager to see you inside the course.

Disclaimer: All the images used in this course are either created or purchased/downloaded under the license from the provider, mostly from Shutterstock or Pixabay.

Linear Regression: Absolute Fundamentals

Ideas on Machine Learning & Linear Regression using scikit-learn in Python and predicting the positive cases for COVID19

Created by Sujithkumar MA - Engineer | Course Instructor

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Students: 9650, Price: Free

Students: 9650, Price:  Free

Hey everyone! I welcome you all to my course Machine Learning Absolute Fundamentals for Linear Regression. This course is targeted for Beginner Python Developers who want to kickstart their journey in Machine Learning. In this course, we are going to use a linear regression model from scikit-learn library in Python to predict the total no. of positive cases for COVID19 in a particular state in India.

After completing this course, you'll  be able to:

1. Define Machine Learning

2. Define what is a dataset

3. Explain what does Machine Learning do?

4. Explain the concept of linear regression

5. Explain what is the line of best fit and cost function (MSE)

6. Use pandas library functions to read the dataset and to preprocess it

7. Splitting data for training and testing

8. Create a linear regression model using sklearn and train it

9. Evaluate the model and predict the values

10. Visualising data using matplotlib

n linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis.

Getting Started with Machine Learning

Machine Learning for dummies

Created by Narayan Jha - Engineer at Udemy

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Students: 9009, Price: Free

Students: 9009, Price:  Free

This course is especially for beginners who want to get started their journey in the field of machine learning.  This course provides the hands-on experience with the python and scikit learn. So if you are new to the machine learning Get started with this course will be a good choice.

Master statistics & machine learning: intuition, math, code

A rigorous and engaging deep-dive into statistics and machine-learning, with hands-on applications in Python and MATLAB.

Created by Mike X Cohen - Neuroscientist, writer, professor

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Students: 8243, Price: $19.99

Students: 8243, Price:  Paid

Statistics and probability control your life. I don't just mean What YouTube's algorithm recommends you to watch next, and I don't just mean the chance of meeting your future significant other in class or at a bar. Human behavior, single-cell organisms, Earthquakes, the stock market, whether it will snow in the first week of December, and countless other phenomena are probabilistic and statistical. Even the very nature of the most fundamental deep structure of the universe is governed by probability and statistics.

You need to understand statistics.

Nearly all areas of human civilization are incorporating code and numerical computations. This means that many jobs and areas of study are based on applications of statistical and machine-learning techniques in programming languages like Python and MATLAB. This is often called 'data science' and is an increasingly important topic. Statistics and machine learning are also fundamental to artificial intelligence (AI) and business intelligence.

If you want to make yourself a future-proof employee, employer, data scientist, or researcher in any technical field -- ranging from data scientist to engineering to research scientist to deep learning modeler -- you'll need to know statistics and machine-learning. And you'll need to know how to implement concepts like probability theory and confidence intervals, k-means clustering and PCA, Spearman correlation and logistic regression, in computer languages like Python or MATLAB.

There are six reasons why you should take this course:

  • This course covers everything you need to understand the fundamentals of statistics, machine learning, and data science, from bar plots to ANOVAs, regression to k-means, t-test to non-parametric permutation testing.

  • After completing this course, you will be able to understand a wide range of statistical and machine-learning analyses, even specific advanced methods that aren't taught here. That's because you will learn the foundations upon which advanced methods are build.

  • This course balances mathematical rigor with intuitive explanations, and hands-on explorations in code.

  • Enrolling in the course gives you access to the Q&A, in which I actively participate every day.

  • I've been studying, developing, and teaching statistics for 20 years, and I'm, like, really great at math.

What you need to know before taking this course:

  • High-school level maths. This is an applications-oriented course, so I don't go into a lot of detail about proofs, derivations, or calculus.

  • Basic coding skills in Python or MATLAB. This is necessary only if you want to follow along with the code. You can successfully complete this course without writing a single line of code! But participating in the coding exercises will help you learn the material. The MATLAB code relies on the Statistics and Machine Learning toolbox (you can use Octave if you don't have MATLAB or the statistics toolbox). Python code is written in Jupyter notebooks.

  • I recommend taking my free course called "Statistics literacy for non-statisticians". It's 90 minutes long and will give you a bird's-eye-view of the main topics in statistics that I go into much much much more detail about here in this course. Note that the free short course is not required for this course, but complements this course nicely. And you can get through the whole thing in less than an hour if you watch if on 1.5x speed!

  • You do not need any previous experience with statistics, machine learning, deep learning, or data science. That's why you're here!

Is this course up to date?

Yes, I maintain all of my courses regularly. I add new lectures to keep the course "alive," and I add new lectures (or sometimes re-film existing lectures) to explain maths concepts better if students find a topic confusing or if I made a mistake in the lecture (rare, but it happens!).

You can check the "Last updated" text at the top of this page to see when I last worked on improving this course!

What if you have questions about the material?

This course has a Q&A (question and answer) section where you can post your questions about the course material (about the maths, statistics, coding, or machine learning aspects). I try to answer all questions within a day. You can also see all other questions and answers, which really improves how much you can learn! And you can contribute to the Q&A by posting to ongoing discussions.

And, you can also post your code for feedback or just to show off -- I love it when students actually write better code than mine! (Ahem, doesn't happen so often.)

What should you do now?

First of all, congrats on reading this far; that means you are seriously interested in learning statistics and machine learning. Watch the preview videos, check out the reviews, and, when you're ready, invest in your brain by learning from this course!

Learn Azure Machine Learning from scratch

This course starts from scratch with Azure Machine Learning and lands in decision trees.

Created by Khaled Jemni - Intelligent Cloud Teacher

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Students: 7413, Price: Free

Students: 7413, Price:  Free

Are you passionate about Machine Learning and AI? Are you looking to find your first steps into Data Science. This course starts from scratch with Azure Machine Learning and lands in decision trees.

I will walk you through the Azure ML Studio, how to create expirements, how to add datasets, how to add algorithms and predict values.

This course does not cover any coding with R or Python, this will be published in a different course.