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Top 10 courses to learn Machine and Deep Learning (2020)

Sergios Karagiannakoson2020-02-10·7 mins

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 that’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 don’t look any further. Ok, let’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 don’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: Free to audit, $49/month for a Certificate

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

Rating: 4.8/5


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

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

Rating: 4.6/10


  • 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 haven’t seen a more complete curriculum than this. From supervised learning to unsupervised and reinforcement, it has everything you can think of.

It won’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, it’s a perfect match for you.

Cost: Free

Time to complete: 4 months



  • 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 Udacity’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


  • Welcome to Machine Learning

  • Naïve 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 you’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 €

Time to complete: 4 months

Rating 4.6/5


  • 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 wouldn’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: Free to audit, $227 for Certificate

Time to complete: 12 weeks


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

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

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

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

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

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

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

  • Week 8: mixtures of Gaussians, matrix factorization

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

  • Week 10: Markov models, hidden Markov models

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

  • Week 12: 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)


  • 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 created by Kirill Eremenko, Data Scientist & Forex Systems Expert and Hadelin de Ponteves, Data Scientist. Here you can expect an analysis of the most important ML algorithms with code templates in Python and R. With 41 hours of learning + 31 articles, it is certainly worth a second look.

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

Time to complete: 41 hours


  • 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

CS234 – 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


  • 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 doesn’t really matter which one you’ll choose. All of them are top-notch. The important thing is to pick one and just start learning.

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