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: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 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: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 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
Syllabus:
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
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 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: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 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
Syllabus:
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
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 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.
* Disclosure: Please note that some of the links above might be affiliate links, and at no additional cost to you, we will earn a commission if you decide to make a purchase after clicking through.