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.
Concentration and Focus: The Principles of Deep Work
Apply Dr. Cal Newport's Deep Work methodology to Get Stuff Done
Created by Nathan Robertson - EdTech Startup Director, Bilingual, Ex-Actor, Author
Students: 52250, Price: Free
Everybody wants to get more done, but we go about it the wrong way.
We think getting more done requires more time. We will put in 80 hours of work a week, getting an abysmal return on investment for the last 20 - 30 hours. We can't elbow grease our way through problems by throwing time at them.
We don't need more time to work. We need to change how we work.
This class, based on Dr. Cal Newport's Deep Work principles, provides the concepts to do just that. Deep Work is all about cutting out the superfluous, unnecessary tasks in our professional and personal life. It will help you focus in on the things you do that provide the most value - to the market, and to your own life.
This is a short primer course with the videos you need to get the basics, and follow up resources if you are curious exploring this deeper.
Don't waste your life working. Work smarter, and provide more value to the world, with Deep Work.
Applied Deep Learning: Build a Chatbot – Theory, Application
Understand the Theory of how Chatbots work and implement them in Python and PyTorch!
Created by Fawaz Sammani - Computer Vision Researcher
Students: 36278, Price: Free
In this course, you'll learn the following:
RNNs and LSTMs
Building a Chatbot in PyTorch
We will first cover the theoretical concepts you need to know for building a Chatbot, which include RNNs, LSTMS and Sequence Models with Attention.
Then we will introduce you to PyTorch, a very powerful and advanced deep learning Library. We will show you how to install it and how to work with it and with PyTorch Tensors.
Then we will build our Chatbot in PyTorch!
Please Note an important thing: If you don't have prior knowledge on Neural Networks and how they work, you won't be able to cope well with this course. Please note that this is not a Deep Learning course, it's an Application of Deep Learning, as the course names implies (Applied Deep Learning: Build a Chatbot). The course level is Intermediate, and not Beginner. So please familiarize yourself with Neural Networks and it's concepts before taking this course. If you are already familiar, then your ready to start this journey!
Artificial Neural Network for Regression
Build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant
Created by Hadelin de Ponteves - AI Entrepreneur
Students: 26897, Price: Free
Are you ready to flex your Deep Learning skills by learning how to build and implement an Artificial Neural Network using Python from scratch?
Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.
In this free course, AI expert Hadelin de Ponteves guides you through a case study that shows you how to build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant.
The objective is to create a data model that predicts the net hourly electrical energy output (EP) of the plant using available hourly average ambient variables.
Go hands-on with Hadelin in solving this complex, real-world Deep Learning challenge that covers everything from data preprocessing to building and training an ANN, while utilizing the Machine Learning library, Tensorflow 2.0, and Google Colab, the free, browser-based notebook environment that runs completely in the cloud. It’s a game-changing interface that will supercharge your Machine Learning toolkit.
Check out what’s in store for you when you enroll:
Part 1: Data Preprocessing
Importing the dataset
Splitting the dataset into the training set and test set
Part 2: Building an ANN
Initializing the ANN
Adding the input layer and the first hidden layer
Adding the output layer
Compiling the ANN
Part 3: Training the ANN
Training the ANN model on the training set
Predicting the results of the test set
More about Combined-Cycle Power Plants
A combined-cycle power plant is an electrical power plant in which a Gas Turbine (GT) and a Steam Turbine (ST) are used in combination to produce more electrical energy from the same fuel than that would be possible from a single cycle power plant.
The gas turbine compresses air and mixes it with a fuel heated to a very high temperature. The hot air-fuel mixture moves through the blades, making them spin. The fast-spinning gas turbine drives a generator to generate electricity. The exhaust (waste) heat escaped through the exhaust stack of the gas turbine is utilized by a Heat Recovery Steam Generator (HSRG) system to produce steam that spins a steam turbine. This steam turbine drives a generator to produce additional electricity. CCCP is assumed to produce 50% more energy than a single power plant.
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.
Blockchain and Deep Learning: Future of AI
Jobs Training for the Future
Created by Melanie Swan - Blockchain Theorist, New School University, NYC
Students: 19814, Price: Free
This course provides a conceptual overview and technical summary of the two top job growth areas worldwide: blockchain technology and deep learning. The course discusses how these technologies may be used together in deep learning chains. Some of the important application areas are autonomous driving, health care, energy, and finance.
Basics of Deep Learning
Fundamentals of Neural Network
Created by Sunil Kumar Mishra - AI Enthusiast | Startup Mentor I Author
Students: 19053, Price: Free
Have you ever wondered what is Deep Learning and how it is helping today in powering Artificial Intelligence?
This basic course in Deep Learning may unravel some of them. You dont need any technical or coding background to know the basic fundamentals of Neural Network. This course is designed for functional consultants, product managers as well as developers and architects.
Contents of the course:
1. Inspiration for Deep Learning
2. Key Concepts of Deep Learning
3. Improving the model
4. Convolutional network
5. Recurrent network
6. Word representation
Amazing AI: Reverse Image Search
Apply your Deep Learning skills and create your own end-to-end Image Search engine!
Created by Luka Anicin - AI Engineer and Entrepreneur
Students: 12007, Price: Free
Artificial intelligence is one of the fastest growing fields of computer science today and the demand for excellent AI Engineers is increasing day in and day out. This course will help you stay competitive in the AI job market by teaching you how to create a Deep Learning End-to-End product on your own.
Most courses focus on the basics of Deep Learning and teach you about the very basics of different models. In this course, however, you will learn how to write a whole End-to-End pipeline, from data preprocessing across choosing the right hyper-parameters, to showing your users results in a browser.
The case that we will tackle in this course is an engine for Image to Image Search.
Why should you take this course?
This course is not focused on teaching you Neural Networks (ANNs, CNNs, RNNs…), but teaching you how to apply them in real world cases.
If you haven’t worked on a product that uses Deep Learning before, this is the perfect course for you! Throughout the course we will work together on the Image to Image Search engine, starting from ground zero - image preprocessing, creating a model, training it, then testing. After that we will create a simple web application and use it to serve our model in production.
Another cool thing about this course is that we will use multiple programming languages to create the whole application around the model itself. This will make you not only a better AI Engineer but also get you on the path towards becoming a Full stack AI Engineer.
After taking this course you will guarantee yourself to be one step closer to landing your dream job as an AI/ML Engineer by having your own AI product/project in your portfolio.
Libraries/Tools used in the course:
The whole Deep learning back-end of our pipeline will be built using Tensorflow 1.10.0. For some image preprocessing task we will use some basic functionality from OpenCV, the most important Python library for image processing tasks!
For the app's back-end (model handling, image uploading, page navigation, etc.) we will use the Flask python framework.
Who is this course for?
As you can see the course is meant to teach you how to create your own Deep Learning product from scratch.
If you are just starting out with Deep Learning, this course might be too hard for you. But if you like challenges, I do recommend following it. Although I will not be explaining the meat of Neural Networks (ANNs, CNNs), I will explain most concepts in great detail, so even if you are a total beginner you should be able to follow with the help of your peers or my help through the comments section.
If you have Deep Learning experience and want to move it to the next level you will find this course very useful! You can consider it as a level up for your skills by putting your already great skills to new use. At the end of the course you will not only have learned how to create a working End-to-End pipeline, but also hold proof of your skills for potential employers!
The conclusion is this - this is very rare opportunity, not only to learn Deep Learning concepts, but also how to apply that knowledge and create your own web application (as a complete product) from scratch.
I hope to see you in class!
Self-driving go-kart with Unity-ML
Deep learning applied to a self-driving car simulation
Created by Fabrizio Frigeni - Engineer
Students: 10780, Price: Free
WARNING: take this class as a gentle introduction to machine learning, with particular focus on machine vision and reinforcement learning. The Unity project provided in this course is now obsolete because the Unity ML agents library is still in its beta version and the interface keeps changing all the time! Some of the implementation details you will find in this course will look different if you are using the latest release, but the key concepts and the background theory are still valid. Please refer to the official migrating documentation on the ml-agents github for the latest updates.
Learn how to combine the beauty of Unity with the power of Tensorflow to solve physical problems in a simulated environment with state-of-the-art machine learning techniques.
We study the problem of a go-kart racing around a simple track and try three different approaches to control it: a simple PID controller; a neural network trained via imitation (supervised) learning; and a neural network trained via deep reinforcement learning.
Each technique has its strengths and weaknesses, which we first show in a theoretical way at simple conceptual level, and then apply in a practical way. In all three cases the go-kart will be able to complete a lap without crashing.
We provide the Unity template and the files for all three solutions. Then see if you can build on it and improve performance further more.
Buckle up and have fun!
Practical Transfer Learning ( Deep Learning )in Python
Don't Be Hero - Next Frontier in Deep Learning Image Classification and Object Detection Problems solution - Keras
Created by Mosin hasan - Engineer - Computer Science
Students: 10563, Price: Free
Don't be Hero . as It is well said..
Let;s Enroll and utilize works of Hero for our problems.
Everyone can not do research like Yann Lecun or Andrew Ng. They are focused on improving machine learning algorithms for better world.
But as an individual and for industry, we are more concern with specific application and its accuracy.
Transfer Learning is the solution for many existing problems. Transfer learning uses existing knowledge of previously learned model to new frontier.
I will demonstrate code to do Transfer Learning in Image Classification.
Knowledge gain to recognize cycle and bike can be used to recognize car.
There are various ways we can achieve transfer learning. I will discuss Pre trained model, Fine tunning and feature extraction techniques.
Once again. Let's not be Hero . and enroll in this course.
Learn Keras: Build 4 Deep Learning Applications
Get up and running with deep learning with keras, a high level deep learning API
Created by Adam Eubanks - Self Taught Programmer And Learning Enthusiast
Students: 10135, Price: Free
When I started learning deep learning, I had a hard time figuring out how everything worked. What library was the best for me? Which algorithms worked best for which data set? How could I know my model was accurate? I spent a lot of time on tutorials, courses and reading to try and answer these questions. In the end, I felt like the process I took to learn deep learning was too inefficient. That is why I created this course.
Learn Keras: Build 4 Deep Learning Applications is a course that I designed to solve the problems my past self had. This course is designed to get you up and running with deep learning as quickly as possible. We use keras in this course because it is one of the easiest libraries to learn for deep learning. Each video, we go over a different machine learning algorithm and its use cases. The four algorithms we focus on the most are:
1. Linear Regression
2. Dense Neural Networks
3. Convolutional Neural Networks
4. Recurrent Neural Networks
In conclusion, if you are looking at a quick intro into deep learning, this course is for you.
So what are you waiting for? Let's get started!
Data Science with Analogies, Algorithms and Solved Problems
Machine learning, Data Mining, Data Science, Deep Learning, Data analysis, Data analytics, Python, Visualization
Created by Ajay Dhruv - Assistant Professor at VIT Mumbai, India
Students: 9523, Price: Free
Interested to know about the field of Machine Learning?
Then this course is for you! This course has been designed such that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.
We will walk you into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this field. While preparing this course special care is taken that the concepts are presented in fun and exciting way but at the same time, we dive deep into machine learning.
Here is a list of few of the topics we will be learning:
• Difference between Data Mining and Deep Learning
• Data and 5 Vs of Big Data
• Types of Attributes
• Supervised learning, Unsupervised learning, Reinforcement learning
• Python Libraries
• CNN, RNN, LSTM
• K - means Clustering Algorithm
• Bayesian Algorithm, ID3 Algorithm
• Simple Linear Regression
Deep Learning with PyTorch for Beginners – Part 1
PyTorch Basics & Linear Regression
Created by Aakash N S - Software Consultant & Entrepreneur
Students: 7873, Price: Free
“Deep Learning with PyTorch for Beginners is a series of courses covering various topics like the basics of Deep Learning, building neural networks with PyTorch, CNNs, RNNs, NLP, GANs, etc. This course is Part 1 of 5.
1. Introduction to Machine Learning & Deep Learning
2. Introduction on how to use Jovian platform
3. Introduction to PyTorch: Tensors & Gradients
4. Interoperability with Numpy
5. Linear Regression with PyTorch
- System setup
- Training data
- Linear Regression from scratch
- Loss function
- Compute gradients
- Adjust weights and biases using gradient descent
- Train for multiple epochs
- Linear Regression using PyTorch built-ins
- Dataset and DataLoader
- Using nn.Linear
- Loss Function
- Train the model
- Commit and update the notebook
7. Sharing Jupyter notebooks online with Jovian
Amazing AI: Music Editing with Deep Learning
Using Deep Learning to split any songs to vocals, drums, bass and other instruments
Created by Luka Anicin - AI Engineer and Entrepreneur
Students: 6343, Price: Free
Deep Learning is used more and more across a variety of industries, and it threatens to disrupt yet another one - the music industry.
So far, there are a lot of different applications on how people use AI in music to get the best possible results - from generating music, lyrics to creating the whole Eurovision songs using this fantastic technology!
In this course, you will learn about another example of how to leverage the full potential of Artificial Intelligence and Deep Learning in the music industry with a few libraries and Python programming language.
You and I will jump in the Google Collab, and in just under 30 minutes, from zero to playable songs go, we will use AI to split any song to its different parts (vocals, drums, bass, and other instruments)!
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.
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
AI foundations for business professionals
A code-free intro to artificial intelligence, ML, & data science for professionals, marketers, managers, & executives
Created by Marshall Lincoln - Chief Data Scientist, FluentInAI.com | Teaching AI literacy
Students: 1145, Price: Free
Full course outline:
Module 1: Demystifying AI
A term with any definitions
An objective and a field
Excitement and disappointment
Introducing prediction engines
Introducing machine learning
Don't expect 'intelligence' (It's not magic)
Module 2: Building a prediction engine
What characterizes AI? Inputs, model, outputs
Two approaches compared: a gentle introduction
Building a jacket prediction engine
Human-crafted rules or machine learning?
Module 3: New capabilities... and limitations
Expanding the number of tasks that can be automated
New insights --> more informed decisions
Personalization: when predictions are granular... and cheap
What can't AI applications do well?
Module 4: From data to 'intelligence
What is data?
Machine learning unlocks new insights from more types of data
What do AI applications do?
Predictions and automated instructions
When is a machine 'decision' appropriate?
Module 5: Machine learning approaches
Machine learning basics
What's an algorithm?
Traditional vs machine learning algorithms
What's a machine learning model?
Machine learning approaches
Artificial neural networks and deep learning
Module 6: Risks and trade-offs
Beware the hype
Three drivers of new risks
What could go wrong? Potential consequences
Module 7: How it's built
It's all about data
Oil and data: two similar transformations
The anatomy of an AI project
The data scientist's mission
Module 8: The importance of domain expertise
The skills gap
A talent gap and a knowledge gap
Marrying technical sills and domain expertise
Lecture 20: What do you know that data scientists might not?
Applying your skills to AI projects
What might you know that data scientists' not?
How can you leverage your expertise?
Module 9: Bonus module: Go from observer to contributor
Go from observer to contributor
Introduction to NLP
Fundamentals of Natural Language Processing
Created by Sunil Kumar Mishra - AI Enthusiast | Startup Mentor I Author
Students: 766, Price: Free
Natural Language processing is today a fast-emerging technology area. It has also been one of the most difficult topics to handle for the computers. Thanks to the advancement in artificial intelligence, we can process natural language more easily today. Many business applications today leverage the power of NLP. With the perfection on speech to text and text to speech conversions, NLP tools are used today as personal assistants and robo advisors. The chat bots are primary interface for many business applications. The NLP engine can process vast amounts of texts and classify them as well as translate them to another language. NLP programs are working in conjunction with the image recognition techniques to automatically generate captions from the images and the vice-versa.
This course is tries to demystify some aspects of NLP and address some of the challenges and approaches to handle the same.
Expectations from the course
1. Why NLP is important
2. Complexity in handling NLP
3. Business use cases of NLP
4. Different types of NLP problems
5. Approach for solving NLP problems
6. Applying machine learning concepts
7. Word embedding
This course uses python programming for basic hands-on. Some of the practice sessions in this course include: -
1. Standard text handling using nltk
2. Pre-processing text (normalization)
3. Sentiment analysis of the review comments
4. Spam detection using machine learning algorithms
Learn Basic of Emerging Trends in Computer
Emerging Trends in Computer & Information Technology
Created by Abdul Aziz Patel Khan - Lecturer
Students: 592, Price: Free
The aim of this course is to help students to attain the industry identified competency through various teaching learning experience: acquire knowledge of emerging trends. Advancements and applications of Computer Engineering and Information Technology are ever changing. Emerging trends aims at creating awareness about major trends that will define technological disruption in the upcoming years in the field of Computer Engineering and Information Technology. These are some emerging areas expected to generated revenue, increasing demand as IT professionals and open avenues of entrepreneurship. The Objectives of the course are Differentiate between Machine Learning & Deep Learning, State IoT issues & Challenges in deployment, Describe the given model of Digital Forensics Investigation, Describe the given evidence handling, Describe the need to hack your own systems, Describe Database Vulnerabilities. The outcomes of the course are Describe Artificial Intelligence, Machine Learning & Deep Learning: Describe the concept of AI, State the components of AI, Differentiate between Machine Learning & Deep Learning, Interpret IoT Concepts: Describe IoT Systems in which information and knowledge are inferred from data, State IoT issues and challenges in deployment, Compare Model of Digital Forensic Investigation: Describe the given model of Digital Forensics Investigation, State the ethical and unethical issues in Digital Forensics, Describe Evidence Handling Procedures: List the rules of digital evidence, Describe the given evidence handling procedures, Describe Ethical Hacking Process: Describe the need to hack your own system, Detect Network, Operating System & Application vulnerabilities: Network Infrastructure vulnerabilities (Wired/Wireless),Describe Messaging Systems vulnerabilities.
The Journey of Deep Learning (Artificial Intelligence)
The Journey of Deep Learning: Past and present
Created by Umesh K. Gaikwad - AI Content Creator and Instructor
Students: 540, Price: Free
Deep learning is a sub domain of Artificial Intelligence which enables computers to carry out the tasks without the human intervention. It is inspired by the biological element of the human brain i.e. Neuron. Popular deep learning algorithms includes Multi-layer perceptron (MLP), Deep Convolution Neural Networks (CNN), Recurrent Neural Networks, Long Short Term Memory (LSTM) Networks, Deep Autoencoders and Boltzmann Machines (BM).Due to their flexibility and high accuracy these models gave record breaking results in the field of image classification, text processing and speech recognition. Every year since, deep learning has continued to get more powerful and improved models for solving problems in many different domains.
There is famous quote by Albert Einstein “if you want to know the future look at the past”. The main foundation of deep learning not new, it is about 70-80 years old. With the same thought this course is designed. Deep learning has its start way back to the 1940 when the first neural network was introduced. From that day to the present day Deep learning has faced many up and downs. But from every down it came back with the better model. In this course students will experience the colorful journey of Deep Learning.