7 Best Machine Learning Courses Online (Updated 2021)

Here are the best machine learning courses online to guide you on an exploration of machine learning, through a practical, hands-on approach.

7 Best Machine Learning Courses Online (Updated 2021)

There's never been a more exciting time to learn about machine learning.


It's a science that powers a wide range of processes that we interact with everyday - everything from speech recognition on our cellphones to the medical diagnosis of diseases and treatment planning.


Here is a breakdown of the best machine learning courses online to guide you in your study of machine learning, whether you're a beginner, or you already have some experience in the field. 


Not only will these courses teach you machine learning theory, they'll also give you an opportunity to sharpen your skills by working with real world datasets.


You'll discover why machine learning models are important, how to build them, and how to put everything together to make useful recommendations and predictions about future data.


This post may contain affiliate links. Please read my disclosure for more information.


What are the Best Machine Learning Courses in 2021?

Here are the best machine learning courses to enroll in online this year:

1. Machine Learning Course by Stanford University


Over 3 million students have already enrolled in this highly-rated course on machine learning by Stanford University. 


This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. It is taught by Andrew Ng, the founder of DeepLearning.AI and one of the co-founders of Coursera himself!


You'll learn about the theoretical foundations of learning, effective machine learning techniques, and some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. 


Course Syllabus: 

  • Week 1: Introduction, Linear Regression with One Variable, Linear Algebra Review
  • Week 2: Linear Regression with Multiple Variables, Octave/Matlab Tutorial
  • Week 3: Logistic Regression, Regularization
  • Week 4: Neural Networks: Representation 
  • Week 5: Neural Networks: Learning
  • Week 6: Advice for Applying Machine Learning, Machine Learning System Design
  • Week 7: Support Vector Machines 
  • Week 8: Unsupervised Learning, Dimensionality Reduction
  • Week 9: Anomaly Detection, Recommender Systems
  • Week 10: Large Scale Machine Learning
  • Week 11: Application Example: Photo OCR

As you progress through the course, you'll learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.


Key course features:

Shareable Certificate

  • Earn a Certificate upon completion

100% online

  • Start instantly and learn at your own schedule.

Flexible deadlines

  • Reset deadlines in accordance with your schedule.

Approx. 60 hours to complete


=> Enroll in the Stanford Machine Learning online course here.


2. Machine Learning: Algorithms in the Real World by Amii


Machine Learning: Algorithms in the Real World is a four-part specialization course offered by the Alberta Machine Intelligence Institute (Amii). 


Whether you're a professional in finance, medicine, engineering, or another domain, this course will show you how to apply machine learning to data analysis and automation, with the end goal of creating a successful machine learning application. 


You will learn to: 

  • Clearly define an ML problem  
  • Survey available data resources and identify potential ML applications 
  • Prepare data for effective ML applications
  • Take a business need and turn it into a machine learning application

Program Syllabus:

  • Course 1: Introduction to Applied Machine Learning
  • Course 2: Machine Learning Algorithms: Supervised Learning Tip to Tail
  • Course 3: Data for Machine Learning
  • Course 4: Optimizing Machine Learning Performance
     

Before taking this machine learning course, it's recommended that you have a background in analytics, math (linear algebra, matrix multiplication), statistics and beginner level python programming.


Overall, this course will enhance your ability to clearly define a machine learning problem, train a classification algorithm, mitigate common machine learning pitfalls, and deploy your project in the real world.


Key course features: 

Shareable Certificate

  • Earn a Certificate upon completion.

100% online courses

  • Start instantly and learn at your own schedule.

Flexible Schedule

  • Set and maintain flexible deadlines.

Intermediate Level

Approximately 4 months to complete

  • Suggested pace of 3 hours/week

=> Enroll in the Machine Learning: Algorithms in the Real World course.


3. Machine Learning A-Z™: Hands-On Python & R In Data Science Udemy Course 


Machine Learning A-Z™ is the most popular machine learning course on Udemy, taught by Kirill Eremenko and Hadelin de Ponteves, two data science experts. 


This course is designed to help you learn complex machine learning theory, algorithms, and coding libraries in a simple way.


You will learn to: 

  • Master machine learning in Python & R
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Course 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

Key course features: 

  • 44 hours on-demand video
  • 75 articles
  • 38 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion

=> Enroll in the Machine Learning A-Z™ online course here.


4. Machine Learning for All by The University of London


Machine Learning has a reputation for being one of the most complex areas of computer science, requiring advanced mathematics and engineering skills to understand it.


However, given its prevalence and the power it has to revolutionize virtually every area of human life and work, machine learning is an area that everyone should understand, at least at a basic level. 


This machine learning course by the University of London presents machine learning concepts in a format that is easily digestible, even if you don't have any background in math or programming.


By the end of the course, you'll be able to:  

  • Understand the basics of how modern machine learning technologies work
  • Explain and predict how data affects the results of machine learning
  • Use a non-programming based platform to train a machine learning module using a dataset
  • Form an informed opinion on the benefits and dangers of machine learning to society

Course Syllabus: 

  • Week 1: Machine learning
  • Week 2: Data Features
  • Week 3: Machine Learning in Practice
  • Week 4: Your Machine Learning Project

In this course, you will have the opportunity to complete an exciting project where you train a computer to recognize images, using user friendly tools developed at Goldsmiths, University of London.


Overall, this is one of the best machine learning courses for beginners. It is suitable for people who want to start a technical career in machine learning, as well as individuals who just want to learn more about one of the most interesting areas of technology at the moment. 


Key course features:

Shareable Certificate

  • Earn a Certificate upon completion.

100% online

  • Start instantly and learn at your own schedule.

Flexible deadlines

  • Reset deadlines in accordance with your schedule.

Beginner Level

  • Approx. 22 hours to complete. 

=> Enroll in the Machine Learning for All online course here.


5. Machine Learning Specialization by The University of Washington


In this course by The University of Washington, learners will gain applied experience in major areas of machine learning including Prediction, Classification, Clustering, and Information Retrieval.


Through a series of practical case studies, you will learn to analyze large and complex datasets, create systems that improve over time, and build intelligent applications that can make predictions from data.


Program Syllabus: 

  • Course 1: Machine Learning Foundations: A Case Study Approach
  • Course 2: Machine Learning: Regression
  • Course 3: Machine Learning: Classification
  • Course 4: Machine Learning: Clustering & Retrieval

Key course features:

Shareable Certificate

  • Earn a Certificate upon completion.

100% online courses

  • Start instantly and learn at your own schedule.

Flexible Schedule

  • Set and maintain flexible deadlines.

Intermediate Level

  • Some related experience required.
Approximately 7 months to complete
  • Suggested pace of 3 hours/week

=> Enroll in this Machine Learning Specialization course here.


6. Bayesian Machine Learning in Python: A/B Testing Udemy Course 


This online course on machine learning focuses on the Bayesian framework and A/B testing in Python. 


A/B testing is all about comparing things.


If you’re a data scientist in a company and you want to make a claim that logo A is better than logo B, you have to back it up using numbers and statistics.


This course will walk you through traditional A/B testing in order to help you appreciate its complexity, and then you will explore an entirely different way of thinking about probability via Bayesian Methods.


In the course, you will: 

  • Use adaptive algorithms to improve A/B testing performance and solve the explore-exploit dilemma
  • Learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning

  • Improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1
  • Understand the difference between Bayesian and frequentist statistics
  • Apply Bayesian methods to A/B testing

Here are the suggested prerequisites for this machine learning course: 

  • Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy, Scipy, Matplotlib

Key course features: 

  • 10.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion


=> Enroll in the Bayesian Machine Learning in Python course here.


7. IBM Machine Learning Professional Certificate


If you'd like to go even further in your study of machine learning and earn a widely-recognized credential to boost your career, this professional certificate offered by IBM is for you. 


This online certificate program will help you develop the skills and experience to pursue a career in machine learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning.


Through a series of 6 courses, you'll be provided with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to machine learning.


You'll also get to explore special topics that complement your learning, including Time Series Analysis and Survival Analysis.


Program Syllabus: 

  • Course 1: Exploratory Data Analysis for Machine Learning
  • Course 2: Supervised Learning: Regression
  • Course 3: Supervised Learning: Classification
  • Course 4: Unsupervised Learning
  • Course 5: Deep Learning and Reinforcement Learning
  • Course 6: Specialized Models: Time Series and Survival Analysis

The machine learning courses included in this program will provide you with an interactive, hands-on experience. You will follow along and code your own projects using some of the most relevant open source frameworks and libraries.


Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this program is suitable for anyone who has basic computer skills and the willingness to learn!


Key course features: 

Shareable Certificate

  • Earn a Certificate upon completion.

100% online courses

  • Start instantly and learn at your own schedule.

Flexible Schedule

  • Set and maintain flexible deadlines.

Intermediate Level

  • Some related experience required.

Approximately 6 months to complete

  • Suggested pace of 3 hours/week.


=> Enroll in the IBM Machine Learning Professional Certificate here.


Final Thoughts


Thanks for checking out this post on the best machine learning courses currently available online. I hope you were able to find a course that suits your particular learning objectives and interests. 


These courses will take you out of your comfort zone. Not only will you get more confident with machine learning theory, you'll also gain experience working with the exact techniques and frameworks that data scientists use to solve problems everyday. 


You can then use this knowledge for your personal projects, professional endeavours, or to enhance your business strategy.


The options are endless.  


Happy learning!


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