You know what I was hoping to have when I started learning Machine Learning. An all in one Machine Learning course. At the time, it was really tricky to find a good course with all the necessary concepts and algorithms. So we were forced to search all over the web, read research papers, and buy books.

Luckily thatâ€™s not the case any more. Now we are in the exact opposite situation. There are so many courses out there. How I am supposed to know which one is good, which includes all the things I need to learn. So here I compiled a list of the most popular and well- taught courses.

I have personal experience with most of them and I highly recommend all of them. Every Machine Learning Engineer or Data Scientist I know suggests one or many of them. So donâ€™t look any further. Ok, letâ€™s get started.

## Machine Learning by Stanford (Coursera)

This course by Stanford is considered by many the best Machine Learning course around. It is taught by Andrew Ng himself ( for those of you who donâ€™t know him, he is a Stanford Professor, co-founder of Coursera, co-founder of Google Brain and VP of Baidu) and it covers all the basics you need to know. Plus, it has a rating of a whopping 4.9 out of 5.

The material is completely self-contained and is suitable for beginners as it teaches you basic principles of linear algebra and calculus alongside with supervised learning. The one drawback I can think of, is that it uses Octave ( an open-source version of Matlab) instead of Python and R because it really wants you to focus on the algorithms and not on programming.

Cost: Free to audit, $79 if you want a Certificate

Time to complete: 76 hours

Rating: 4.9/5

Syllabus: Linear Regression with One Variable

Linear Algebra Review

Linear Regression with Multiple Variables

Octave/Matlab Tutorial

Logistic Regression

Regularization

Neural Networks: Representation

Neural Networks: Learning

Advice for Applying Machine Learning

Machine Learning System Design

Support Vector Machines

Dimensionality Reduction

Anomaly Detection

Recommender Systems

Large Scale Machine Learning

Application Example: Photo OCR

## Deep Learning Specialization by deeplearning.ai (Coursera)

Again, a course taught by Andrew Ng and again it is considered on the best in the field of Deep Learning. You see a pattern here? It actually consists of 5 different courses and it will give you a clear understanding of the most important Neural Network Architectures. Seriously if you are interested in DL, look no more.

It utilizes Python and the TensorFlow library ( some background is probably necessary to follow along) and it gives you the opportunity to work in real-life problems around natural language processing, computer vision, healthcare.

Cost:Â Free to audit, $49/month for a Certificate

Time to complete: 3 months (11 hours/week)

Rating: 4.8/5

Syllabus:

Neural Networks and Deep Learning

Improving Neural Networks: Hyperparameter Tuning, Regularization, and Optimization

Structuring Machine Learning Projects

Convolutional Neural Networks

Sequence Models

## Advanced Machine Learning Specialization (Coursera)

The advanced Machine Learning specialization is offered by National Research University Higher School of Economics and is structured and taught by Top Kaggle machine learning practitioners and CERN scientists It includes 7 different courses and covers more advanced topics such as Reinforcement Learning and Natural Language Processing. You will probably need more math and a good understanding of basic ML ideas, but the excellent instruction and the fun environment will make up to you. It surely comes with my highest recommendation.

Cost:Â Free to audit, $49/month for a Certificate

Time to complete: 8-10 months (6-10 hours/week)

Rating: 4.6/10

Syllabus:

Introduction to Deep Learning

How to Win Data Science Competitions: Learn from Top Kagglers

Bayesian Methods for Machine Learning

Practical Reinforcement Learning

Deep Learning in Computer Vision

Natural Language Processing

Addressing the Large Hadron Collider Challenges by Machine Learning

## Machine Learning by Georgia Tech (Udacity)

If you need a holistic approach on the field and an interactive environment, this is your course. I have to admit that I havenâ€™t seen a more complete curriculum than this. From supervised learning to unsupervised and reinforcement, it has everything you can think of.

It wonâ€™t teach you Deep neural networks, but it will give you a clear understanding of all the different ML algorithms, their strengths, their weaknesses and how they can be used in real-world applications. Also, if you are a fan of very short videos and interactive quizzes throughout the course, itâ€™s a perfect match for you.

Cost:Â Free

Time to complete: 4 months

Rating:

Syllabus:

Supervised Learning

Unsupervised Learning

Reinforcement Learning

## Introduction to Machine Learning (Udacity)

This introductory class is designed and taught the co-founder of Udacity Sebastian Thrun and the Director of Data Science Research and Development Katie Malone. Its primary audience is beginners who are looking for a course to get started with ML. Again if you like Udacityâ€™s environment (which I personally do), it is an amazing alternative to get your foot in the door.

Cost: Free

Time to complete: 10 weeks

Syllabus:

Welcome to Machine Learning

NaÃ¯ve Bayes

Support Vector Machines

Decision Trees

Choose your own Algorithm

Datasets and Questions

Regressions

Outliers

Clustering

Feature Scaling

## Deep Learning Nanodegree (Udacity)

The Deep Learning Nanodegree by Udacity will teach you all the cutting-edge DL algorithms from convolutional networks to generative adversarial networks. It is quite expensive but is the only course with 5 different hands-on projects. You will build a dog breed classifier, a face generation system a sentiment analysis model and youâ€™ll also learn how to deploy them in production. And the best part is that it is taught by real authorities such as Ian Goodfellow, Jun-Yan Zhuand, Sebastian Thrun and Andrew Trask.

Cost: 1316 â‚¬

Time to complete: 4 months

Rating 4.6/5

Syllabus:

Project 1: Predicting Bike-Sharing Patterns (Gradient Descent and Neural Networks)

Project 2: Dog Breed Classifier( CNN, AutoEncoders and PyTorch)

Project 3: Generate TV Scripts (RNN, LSTM and Embeddings)

Project 4: Generate Faces (GAN)

Project 5: Deploy a Sentiment Analysis Model

## Machine Learning by Columbia (edX)

The next in our list is hosted in edX and is offered by the Columbia University. It requires substantial knowledge in mathematics (linear algebra and calculus) and Programming( Python or Octave) so if I were a beginner I wouldnâ€™t start here. Nevertheless, it can be ideal for more advanced students if they want to develop a mathematical understanding of the algorithms.

One thing that makes this course unique is the fact that it focuses on the probabilistic area of Machine Learning covering topics such as Bayesian linear regression and Hidden Markov Models.

Cost:Â Free to audit, $227 for Certificate

Time to complete: 12 weeks

Syllabus:

Week 1:Â maximum likelihood estimation, linear regression, least squares

Week 2:Â ridge regression, bias-variance, Bayes rule, maximum a posteriori inference

Week 3:Â Bayesian linear regression, sparsity, subset selection for linear regression

Week 4:Â nearest neighbor classification, Bayes classifiers, linear classifiers, perceptron

Week 5:Â logistic regression, Laplace approximation, kernel methods, Gaussian processes

Week 6:Â maximum margin, support vector machines, trees, random forests, boosting

Week 7:Â clustering, k-means, EM algorithm, missing data

Week 8:Â mixtures of Gaussians, matrix factorization

Week 9:Â non-negative matrix factorization, latent factor models, PCA and variations

Week 10:Â Markov models, hidden Markov models

Week 11:Â continuous state-space models, association analysis

Week 12:Â model selection, next steps

## Practical Deep Learning for Coders, v3 ( by fast.ai)

Practical Deep Learning for Coders is an amazing free resource for people with some coding background (but not too much) and includes a variety of notes, assignments and videos. It is built around the idea to give students practical experience in the field so expect to code your way through. You can even learn how to use a GPU server on the cloud to train your models. Pretty cool.

Cost: Free

Time to complete: 12 weeks (8 hours/week)

Syllabus:

Introduction to Random Forests

Random Forest Deep Dive

Performance, Validation, and Model Interpretation

Feature Importance. Tree Interpreter

Extrapolation and RF from Scratch

Data Products and Live Coding

RF From Scratch and Gradient Descent

Gradient Descent and Logistic Regression

Regularization, Learning Rates, and NLP

More NLP and Columnar Data

Embeddings

Complete Rossmann. Ethical Issues

## Machine Learning A-Zâ„¢: Hands-On Python & R In Data Science

Definitely, the most popular AI course on Udemy with half a million students enrolled. It is createdÂ byÂ Kirill Eremenko,Â Data Scientist & Forex Systems ExpertÂ andÂ Hadelin de Ponteves,Â Data Scientist. Here you can expect an analysis of the most important ML algorithms with code templates in Python and R. WithÂ 41 hours of learningÂ + 31 articles, it is certainly worth a second look.

Cost: 199 â‚¬ (but with discounts. At the time of writing the cost was 13.99â‚¬)

Time to complete: 41 hours

Syllabus:

Part 1 - Data Preprocessing

Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression,Â PolynomialÂ Regression,Â SVR, Decision Tree Regression,Â Random Forest Regression

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

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

Part 5 - Association Rule Learning: Apriori,Â Eclat

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

Part 7 - Natural Language Processing: Bag-of-words modelÂ andÂ algorithms for NLP

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

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

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

## CS234 â€“ Reinforcement Learning by Stanford

The most difficult course on the list for sure because arguably Reinforcement Learning is much more difficult. But if you want to dive into it, there is no better way to do it. It is in fact actual recorded lectures from Stanford University. So be prepared to become a Stanford student yourself. The professor Emma Brunskill makes it very easy to understand all these complex topics and gives you amazing introduction to the RL systems and algorithms. Of course, you will find many mathematical equations and proofs, but there is no way around it when it comes to Reinforcement Learning.

You can find the course website here and the video lectures in this Youtube playlist

Cost: Free

Time to complete: 19 hours

Syllabus:

Introduction

Given a model of the world

Model-Free Policy Evaluation

Model-Free Control

Value Function Approximation

CNNs and Deep Q Learning

Imitation Learning

Policy Gradient I

Policy Gradient II

Policy Gradient III and Review

Fast Reinforcement Learning

Fast Reinforcement Learning II

Fast Reinforcement Learning III

Batch Reinforcement Learning

Monte Carlo Tree Search

Here you have it. The ultimate list of Machine and Deep Learning Courses. Some of them may be too advanced, some may contain too much math, some may be too expensive but each one of them is guaranteed to teach all you need to succeed in the AI field.

And to be honest, it doesnâ€™t really matter which one youâ€™ll choose. All of them are top-notch. The important thing is to pick one and just start learning.

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