Best Free Machine Learning Courses

Find the best online Free 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.

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

"]

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. 

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

"]

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.

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

"]

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

"]

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.

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: 22136, 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

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

"]

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.

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

"]

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

"]

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.

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

"]

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

"]

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.

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

"]

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.

Python Crash Course for Data Science and Machine Learning

Learn the Python fundamentals from scratch and kick-off your practical data science learning path

Created by Idan Gabrieli - Presales Manager | Entrepreneur | Cloud and AI Expert

"]

Students: 7239, Price: Free

Unleash the Power of ML

Machine Learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and ML experts to develop products, features, and projects that will help them unleash the power of machine learning. As a result, a data scientist is one of the top ten wanted jobs worldwide!

Starting with Python

This course is designed for beginners looking to enter the practical side of data science. You will learn the Python fundamentals and syntax for developing data science projects by using the JupyterLab tool while creating Jupiter notebooks. The course includes a summary exercise as well as a complete solution to practice Python.

The Game just Started!

Enroll in the training program and start your journey to become a data scientist!

Basic Python/Machine Learning in Bioinformatics

Please download the kaggle for the code

Created by William Kang - Bioinformatics Researcher

"]

Students: 6961, Price: Free

This is a course intended for beginners interested in applying Python in Bioinformatics. We will go over basic Python concepts, useful Python libraries for bioinformatics/ML, and going through several mini-projects that will use these Python/ML concepts. These mini-projects include a sequence analysis (with no libraries) Python example, a Python sequence analysis example using libraries, and a basic Sklearn Machine Learning example.

Implementation of ML Algorithm Using Python

Data Science with Machine Learning Algorithm

Created by Dhanashri Kolekar - Data Science Using Python

"]

Students: 5670, Price: Free

we learn lots of dataset how to predict values using different machine learning algorithm. problem-solving oriented subject that learns to apply scientific techniques to practical problems. The course orients on practical classes and self-study during preparation of datasets and programming of data analysis tasks. This course takes you through all the important modules that you need to know about, including machine learning and programming languages.

Artificial Intelligence and Machine Learning Made Simple

A non-technical explanation of all the buzzwords around Artificial Intelligence, Machine Learning and Deep Learning.

Created by Sertac Ozker - Data Analyst / Machine Learning Engineer / Data Scientist

"]

Students: 5450, Price: Free

Are you ready for the coming AI revolution? It already started to affect us. In this non-technical course, I will try to show you how to navigate the rise of Artificial Intelligence, Machine Learning and Deep Learning.

"Artificial Intelligence and Machine Learning Made Simple" is carefully created to match the needs of business leaders, managers and CXOs. This program was built to be broadly applicable across industries and roles. So regardless if you're coming from IT or marketing, work as an engineer or manager, this program may well be suited for you. Despite its broad applicability, this program will be most useful for those who are looking to understand and make better decisions surrounding machine learning projects in a business environment. My focus will be on explaining concepts in a way that is easily understandable regardless of your technical background.

When you finish the course, you will be comfortable with the buzzwords around Artificial Intelligence, Machine Learning and Deep Learning. You will have a certain understanding of AI applications and how to apply them to your business.

Practical Machine Learning with Scikit-Learn

Learn the most powerful machine learning algorithms in under an hour

Created by Adam Eubanks - Self Taught Programmer And Learning Enthusiast

"]

Students: 5111, Price: Free

Machine learning is a rapidly growing field. However, a lot of courses on the internet today do not go over some of it's most powerful algorithms. In this course, we will learn multiple machine learning algorithms, along with data preprocessing, all in under an hour. We will go over regression, classification, component analysis and boosting all in scikit-learn, one of the most popular machine learning libraries for python.

Algorithms we'll go over (in order):

  • Linear Regression

  • Polynomial Regression

  • Multiple Linear Regression

  • Logistic Regression

  • Support Vector Machines

  • Decision Trees

  • Random Forest

  • Principle Component Analysis

  • Gradient Boosting

  • XGBoost

Data Science for Business Leaders: Machine Learning Defined

Understanding Machine Learning as a Business Capability

Created by Robert Fox - Data Scientist, CIO

"]

Students: 4686, Price: Free

This course has a simple mission: to give you a solid understanding of what Machine Learning is.  Mastering the terminology is your first step to understanding the ideas and capabilities. 

Machine learning is a capability that business leaders should grasp if they want to extract value from data.   There's a lot of hype; but there's some truth: the use of modern data science techniques could translate to a leap forward in progress or a significant competitive advantage.  Often you can buy "AI-powered" solutions; but you should consider how your organization's core competencies could benefit from machine learning. 

No coding or complex math. This is not a hands-on course. We set out to explain all of the fundamental concepts you'll need in plain English.

Simplified: Intro to Machine Learning

Learn what machine learning is all about in this beginner-level entry course

Created by Jayanth Peetla - High School Student at the Academies of Loudoun

"]

Students: 4413, Price: Free

This is the first of a series of courses dedicated to teaching students with an understanding of basic computer science concepts and little to no pre-existing knowledge of machine learning.  Specifically, "Machine Learning Simplified" targets individuals who can't afford an expensive machine learning course and do not have the extensive pre-requisites the majority of courses require.  Why learn machine learning?  Artificial intelligence has already established itself as the future of modern society.  Experts predict that up to 20 million jobs will be lost to AI by 2030.  Therefore, to stay competitive in the constantly changing labor force, it's critical to keep up with new technology.  Machine learning, one of the biggest sectors of artificial intelligence, has shown to be a promising, newly emerging field in the tech industry.  After completing this course and the rest of the courses in the series, you will have an in-depth understanding of machine learning.

Mastering Machine Learning: Course-1

Taking First Step Towards Machine Learning

Created by Parteek Bhatia - Professor, CSED, TIET, Patiala, India

"]

Students: 4196, Price: Free

This course will be a part of series of Free ML Courses to become an expert of ML. Presenting here its First Course on Machine Learning for becoming expert of ML.

This course presents the concepts of Supervised Machine Learning, Unsupervised Machine Learning, Regression and Classification.

It covers implementation of Simple Linear Regression.

What is Machine Learning?

An overview of Supervised, Unsupervised, and Reinforcement Learning with Python Demos

Created by Satish Reddy - Machine Learning Consultant

"]

Students: 3759, Price: Free

Course gives a big picture, (mostly) non-technical overview of Supervised, Unsupervised, and Reinforcement Learning

  • Ideas are presented using lots of examples with animations and plots

  • A demo of course Python codes is presented

  • A list of resources for further study of machine learning is provided

Students who would like to run and experiment with the demo codes will need to have a Google (or gmail) account or have Python on their machine (can install the Anaconda platform). Knowledge of Python is not required to experiment with codes.

Find Actionable Insights using Machine Learning and XGBoost

Let's Build a Student Retention Model with Python and Create a Report of Actionable Insights

Created by Manuel Amunategui - Data Scientist & Quantitative Developer

"]

Students: 3273, Price: Free

Applied data science is about everything that goes before and after your model. Extracting actionable insights is probably the most important aspect of any modeling project! if you want to step up your data science game then this is a great area to study. Let's do it hands-on, applied a science project together and walk through a student retention model to extract actionable insights and help out struggling students.

  • Explore student data

  • Model student behavior using XGBoost

  • Predict struggling/at-risk students

  • Identify what makes a struggling student different than successful students

  • Build a report of actionable insights

  • And help teachers help students

In the case of a student retention model, looking at the full picture means doing a lot of work before doing any modeling. For example, talking to teachers. We need to better understand the business domain. In this case, finding out what are the problems they face. What are the uncertainties they'd like help with? It is critical to also leverage all their knowledge, like how and when do they determine that a student is at-risk. What data points and triggers do they use to identify someone that could be failing a class and/or their studies. How early can they identify this? Obviously the earlier the better, you don't want to wait till have too many bad grades and can't dig themselves out of the hole.

After you've distilled all that information in the model, we dig down into the observation level. This is an important point to understand. A model may return feature importance, coefficients, or weights depending on what type of model you use and how it learns. So, imagine a model that predicts heart attacks and finds that older age is the most important feature for the model, and if your patient is young, that's not going to tell them anything, worse, may lead them to misdiagnose.

Instead, we let the model give us a prediction of the likelihood of something happening, then we dig down to the observation level (i.e. each specific patient or student level) where each case is different and unique and analyze what makes this particular patient/student different from the rest. This may yield some useful information that may allow the professional to better assist - that is actionable insight.

Introduction to Data Science for Complete Beginners

Start your journey in the field of Data Science and learn about machine learning and Deep learning & more!

Created by Fahad Masood Reda - Data Science & MIS Mentor | Founder of Fahad Academy

"]

Students: 2062, Price: Free

Data science and machine learning is one of the hottest fields in the market and has a bright future

In the past ten years, many courses have appeared that explains the field in a more practical way than in theory

During my experience in counseling and mentoring, I faced many obstacles, the most important of which was the existence of educational gaps for the learner, and most of the gaps were in the theoretical field.

To fill this gap, I made this course, Thank God, this course helped many students to properly understand the field of data science.

If you have no idea what the field of data science is and are looking for a very quick introduction to data science, this course will help you become familiar with and understand some of the main concepts underlying data science.

If you are an expert in the field of data science, then attending this course will give you a general overview of the field

This short course will lay a strong foundation for understanding the most important concepts taught in advanced data science courses, and this course will be very suitable if you do not have any idea about the field of data science and want to start learning data science from scratch

Association Mining for Machine Learning

Simplified Way to Learn

Created by Parteek Bhatia - Professor, CSED, TIET, Patiala, India

"]

Students: 1294, Price: Free

This course covers the working Principle of Association Mining and its various concepts like Support, Confidence, and Life in a very simplified manner. This course discusses about Naive Algorithm and Apriori Algorithm for finding Association Mining rules by taking lot of examples. All of these algorithms has been explained by taking working examples.

Genetic Algorithm for Machine Learning

Simplified Way to Learn

Created by Parteek Bhatia - Professor, CSED, TIET, Patiala, India

"]

Students: 1291, Price: Free

This course covers the working Principle of Genetics Algorithms and its various components like Natural Selection, Crossover or Recombination, Mutation and Elitism in a a very simplified way.

GA are inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.

Intro to Embedded Machine Learning

Embedded Systems, Machine Learning, and Tiny ML

Created by Ashvin Roharia - Software Engineer at Silicon Labs

"]

Students: 1022, Price: Free

In this course, you will learn more about the field of embedded machine learning. In recent years, technological advances in embedded systems have enabled microcontrollers to run complicated machine learning models. Embedded devices for machine learning applications can fulfill many tasks in the industry. One typical example: sensor devices that detect acoustic or optical anomalies and discrepancies and, in this way, support quality assurance in production or system condition monitoring. In addition to cameras for monitoring visual parameters and microphones for recording soundwaves, these devices also use sensors for, for instance, vibration, contact, voltage, current, speed, pressure, and temperature.

Even though there is plenty of educational content on embedded systems and machine learning individually, educational content on embedded ML has yet to catch up. This course attempts to fill that void by providing fundamentals of embedded systems, machine learning, and Tiny ML. This course will conclude with an interactive project where the learner will get to create their own specialized embedded ML project. This project will be based on acoustic event detection using a microcontroller or your own mobile device. By the end of the course, you will be able to pick your own classifications and audio and train and deploy a machine learning model yourself. This is a great way to introduce yourself to and gain valuable experience in the field of embedded machine learning.

Machine Learning with Python

Machine Lerning with Python,Supervised,Unsupervised and Regression learning

Created by Vijay A - Computer Science Engineer and Software developer

"]

Students: 963, Price: Free

Machine Learning tutorial provides basic and advanced concepts of machine learning. Our machine learning tutorial is designed for students and working professionals.

Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more.

This machine learning tutorial gives you an introduction to machine learning along with the wide range of machine learning techniques such as Supervised, Unsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, hidden Markov models, and various sequential models.

When you tag a face in a Facebook photo, it is AI that is running behind the scenes and identifying faces in a picture. Face tagging is now omnipresent in several applications that display pictures with human faces. Why just human faces? There are several applications that detect objects such as cats, dogs, bottles, cars, etc. We have autonomous cars running on our roads that detect objects in real time to steer the car. When you travel, you use Google Directions to learn the real-time traffic situations and follow the best path suggested by Google at that point of time. This is yet another implementation of object detection technique in real time.

Let us consider the example of Google Translate application that we typically use while visiting foreign countries. Google’s online translator app on your mobile helps you communicate with the local people speaking a language that is foreign to you.

There are several applications of AI that we use practically today. In fact, each one of us use AI in many parts of our lives, even without our knowledge. Today’s AI can perform extremely complex jobs with a great accuracy and speed. Let us discuss an example of complex task to understand what capabilities are expected in an AI application that you would be developing today for your clients.

Example

We all use Google Directions during our trip anywhere in the city for a daily commute or even for inter-city travels. Google Directions application suggests the fastest path to our destination at that time instance. When we follow this path, we have observed that Google is almost 100% right in its suggestions and we save our valuable time on the trip.

You can imagine the complexity involved in developing this kind of application considering that there are multiple paths to your destination and the application has to judge the traffic situation in every possible path to give you a travel time estimate for each such path. Besides, consider the fact that Google Directions covers the entire globe. Undoubtedly, lots of AI and Machine Learning techniques are in-use under the hoods of such applications.

Considering the continuous demand for the development of such applications, you will now appreciate why there is a sudden demand for IT professionals with AI skills.

Interview Puzzles for Data Science

Goal oriented course to prepare for Puzzle rounds in Data Science Interviews

Created by Machine Learning Express - Delivers Data Science Skills

"]

Students: 781, Price: Free

Welcome to the course on one of the most important aspects of Data Science Interviews. Problem Solving. Companies across the globe are increasingly relying on problem solving skills to decide on the right talent for their teams. Most companies find training a new member if the new comer has a strong logical bent of mind. In this course, we have collected some of the challenging problems that are asked in Data Science Interviews and have presented solutions for them as well.

This course will prepare you better for Data Science Interviews in the following ways

Enable development of strong foundation in Problem Solving

Build a step by step approach mindset 

How to engage the interviewer by articulating a step by step solution

It is the best course to start developing problem solving skills and be better prepared to handle the brainteaser rounds. Wish you all the best and we hope you see you inside the course

Learn Python NumPy for Machine Learning

Why Learn NumPy for Machine Learning?

Created by Saima Aziz - Instructor

"]

Students: 745, Price: Free

Welcome to Learn NumPy for Machine Learning course. My name is Saima Aziz and I will be the instructor for this course.

In this course we will learn how to create Numpy arrays, learn some built-in functions, access values, broadcasting and manipulating arrays etc.

Python is a general purpose and high level programming language. You can use Python for developing desktop GUI applications, websites and web applications. We will learn Numpy from scratch, which is one of the most popular Python programming language library.

Numpy stands for ‘Numerical Python’. It is an open-source Python library used to perform various mathematical and scientific tasks. It contains multi-dimensional arrays and matrices, along with many high-level mathematical functions that operate on these arrays and matrices. Moreover, NumPy forms the foundation of Machine Learning.

NumPy helps to calculate large quantities and common descriptive statistics. It is very useful for handling linear algebra, fourier transforms, and random numbers. It's high speed coupled with easy to use functions make it a favorite among Data Science and Machine Learning practitioners. Many of its functions are very useful for performing any mathematical or scientific calculation.

I encourage you to take the course from beginning to end to get the full learning experience. Some topics may be very easy for you and others will be challenging, but each topic should offer something of value.

Hope you will enjoy the course!

Machine Learning through Case Studies for Beginners

Gentle introduction to Machine Learning

Created by Arun Panayappan - Data Scientist and Trainer

"]

Students: 736, Price: Free

Are you a beginner looking to get started with Machine Learning? This course offers a gentle introduction to Machine Learning through real world case studies as you invent your first Machine Learning algorithm.

This course gives you a broad overview of the variety of Machine Learning models and provides a learning ladder to continue learning. It also presents applications that use Machine Learning and details a plethora of techniques that are used to evolve Machine Learning models from data.

This course presents data for a simple case study of classifying emails automatically. It provides the data set, identifies features ans labels and presents the intuition behind any Machine Learning algorithm. The course goes on to talk about both the (i) Supervised and (ii) Unsupervised learning models. It presents an analysis of over fitting and under fitting in models.

The course aims to motivate a beginner to get started with their Machine Learning journey. This course will be further supplemented with focused sessions on various regression, classification and clustering algorithms.

The subsequent sessions will get in to the Math behind the algorithm while solving a real world case study. Students who continue this course through the recommended ladder will eventually have the skills to build and deploy Machine Learning models to production.

Machine Learning Linear Regression Case Study

Predicting Boston house price with Linear Regression using scikit-learn !!

Created by Goeduhub Technologies - Technical Training Provider Company.

"]

Students: 680, Price: Free

We have covered-

What is Machine Learning and how does it works?

Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

Linear Regression Concept with simple regression model using Scikit Learn Library.

What are the types of Regressions?

Case Study-Boston house price prediction-predicts the price of houses in Boston using a machine learning algorithm called Linear Regression. To train our machine learning model ,we will be using scikit-learn’s boston dataset.

Analyse and visualize data using Linear Regression.

Plot the graph of results of Linear Regression to visually analyze the results.

Linear regression is starting point for a data science this course focus is on making your foundation strong for deep learning and machine learning algorithms.

End of the course you will be able to code your own regression algorithm from scratch.

After completing this course you will be able to:

  • Interpret and Explain machine learning models which are treated as a black-box

  • Create an accurate Linear Regression model in python and visually analyze it

  • Select the best features for a business problem

  • Remove outliers and variable transformations for better performance

  • Confidently solve and explain regression problems

    This course will give you a very solid foundation in machine learning. You will be able to use the concepts of this course in other machine learning models.