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400 Best + Free Machine Learning Courses & Certification [2020][UPDATED]

As featured on Harvard EDU, Stackify and Inc - CourseDuck identifies and rates the Best Machine Learning Courses, Tutorials, Providers and Certifications, based on 12,000+ student reviews, public mentions, recommendations, ratings and polling 5,000+ highly active StackOverFlow members. Learn more

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400 Filtered Courses
Machine Learning by Stanford
Best Course Overall

2 )

Machine Learning by Stanford (2011)

Created by the co-founder of Coursera, this course will provide you with a broad introduction to Machine Learning. It is the #1 highest rated Machine Learning course on Coursera and an excellent choice for beginners with no programming experience.
    • Highly recommended as your first course to dive into Machine Learning.
    • Although it requires hard work, the course is very accessible for beginners.
    • Presented by an expert in the field of Machine Learning and online teaching.
    • Well designed with simple explanations and comprehensive content.
    • Focused on the logic behind Machine Learning rather than programming and maths.
    • Experienced developers may consider lectures and assignments to be too basic.
    • Taught in Matlab/Octave, not Python.
    • Lacks practical examples.
Best YouTube Tutorial

3 )

Machine Learning with Python by Sentdex (2016)

Comprehensive Machine Learning series covering everything from linear regression to neural networks provided by a famous YouTube instructor, Sentdex. This tutorial features 72 videos, and it's ideal for learners that have a basic understanding of Python.
    • In-depth tutorial covering many major topics of Machine Learning.
    • Great for beginners as well as intermediate level learners.
    • Interesting and knowledgeable instructor with practical approach to learning.
    • Requires foundational knowledge in data science and Python.
Best Short Course

4 )

Google's Machine Learning Crash Course (2018)

Taught by Google experts, this free, concise, and highly interactive course will give you a basic understanding of Machine Learning concepts. Learn and practice at your own pace, using TensorFlow APIs.
    • The course is taught by Google engineers and researchers, experts in the field of Machine Learning.
    • Short and sweet course but with relevant curriculum for complete beginners.
    • Interactive quizzes, programming and playground exercises.
    • The only framework for building ML models presented in the course is TensorFlow.
    • Vague explanations of Machine Learning concepts make some of the exercises too difficult for students.
Best NEW Course

5 )

MIT Deep Learning for Self-Driving Cars (2019)

Learn Deep Learning from a research scientist at MIT, one the world's most reputable universities. Great collection of courses and lectures, providing informative content and real-world examples.
    • The instructor is a researcher from one of the most prestigious universities in the world.
    • The concepts are presented in a clear and straight-forward manner.
    • Real-world examples to help you understand how to apply the theory behind Deep Learning.
    • Too many topics covered in one tutorial, only scratches the surface of each.
    • Lacks interactivity which can be inconvenient for learners to easily comprehend key concepts.
Best Practical Course

6 )

Practical Deep Learning for Coders, v3 (2019)

Text-based and video-based introductory Machine Learning course taught by an experienced instructor and Kaggle's #1 competitor. Using PyTorch and fastai library, this tutorial is focused on practical results rather than theory.
    • Experienced instructor that provides easy to understand explanations and teaches you "how-to" instead of "why".
    • Top-down learning approach perfect for students that want to apply Machine Learning fast.
    • Great community of fellow-learners to help you along the course.
    • This course uses fastai library that can be too difficult for beginners.
    • To understand the theory befind the course, further readings and additional information are necessary.
Neural Networks and Deep Learning
Best Advanced Course

7 )

Neural Networks and Deep Learning (2017)

Learn how to build and implement your own deep neural networks in just 7 hours. Taught by an experienced instructor, this is the first course in the Deep Learning Specialization.
    • Offered by, a well known provider of a world-class AI education.
    • Taught in Python and Jupyter Notebook.
    • Good introduction to how to build and implement neural networks.
    • Easy to understand lectures with a mix of theory and practical application.
    • Useful tips and insights into Deep Learning.
    • Pre-written code in assignments.
    • Repetitive content.
Machine Learning & Data Science Masterclass in Python and R

8 )

Machine Learning & Data Science Masterclass in Python and R (2019)

This course contains over 200 lessons, quizzes, practical examples - the easiest way if you want to learn Machine Learning. Step by step I teach you machine learning. In each section you will learn a new topic - first the idea / intuition behind it, and then the code in both Python and R. Machine Learning is only really fun when you evaluate real data. That's why you analyze a lot of practical examples in this course:
  • Estimate the value of used cars
  • Write a spam filter
  • Diagnose breast cancer
All code examples are shown in both programming languages - so you can choose whether you want to see the course in Python, R, or in both languages! After the course you can apply Machine Learning to your own data and make informed decisions: You know when which models might come into question and how to compare them. You can analyze which columns are needed, whether additional data is needed, and know which data needs to be prepared in advance. This course covers the important topics:
  • Regression
  • Classification
On all these topics you will learn about different algorithms. The ideas behind them are simply explained - not dry mathematical formulas, but vivid graphical explanations. We use common tools (Sklearn, NLTK, caret, data.table, ...), which are also used for real machine learning projects.
What do you learn?
  • Regression:
  • Linear Regression
  • Polynomial Regression
  • Classification:
  • Logistic Regression
  • Naive Bayes
  • Decision trees
  • Random Forest
You will also learn how to use Machine Learning:
  • Read in data and prepare it for your model
  • With complete practical example, explained step by step
  • Find the best hyper parameters for your model
  • "Parameter Tuning"
  • Compare models with each other:
  • How the accuracy value of a model can mislead you and what you can do about it
  • K-Fold Cross Validation
  • Coefficient of determination
My goal with this course is to offer you the ideal entry into the world of machine learning.

Who this course is for:
  • Developers interested in Machine Learning

9 )

Machine Learning (2015)

Learn Supervised, Unsupervised and Reinforcement Learning approaches from entertaining and competent instructors. Offered at Georgia Tech, this free and interactive course covers an interesting area of Artificial Intelligence.
    • The course is a part of the Online Masters Degree at one of the best universities for computer science.
    • Charming and entertaining instructors.
    • Broad survey of the Machine Learning field.
    • Unique style of teaching that will not suit everyone.
    • Long and Time-consuming.
Machine Learning by Columbia University and edX

10 )

Machine Learning by Columbia University and edX (2016)

Advanced Machine Learning course offered by the Columbia University. Focused on maths and theory, it delivers in-depth knowledge for students with a strong foundation within a reasonable amount of time.
    • Offered by an Ivy League research university.
    • In-depth learning experience in Machine Learning.
    • Talented instructor competent in transmitting knowledge.
    • Good explanations of advanced topics such as Gaussian processes.
    • Requires strong mathematics background.
    • Lacks interactivity and practical examples.
    • Does not cover neural networks.
Machine Learning with Python by IBM

11 )

Machine Learning with Python by IBM (2018)

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components:First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!By just putting in a few hours a week for the next few weeks, this is what you'll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

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