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The 9 Free Machine Learning Courses from the World-Class Educators

The 9 Free Machine Learning Courses from the World-Class Educators

Machine Learning is everywhere and all the successful companies are employing skilled engineers to apply machine learning methods to optimally improve the personalization of their technologies.

According to the piece published last year on Forbes about Machine Learning Engineer is the best Job which indicated that the Machine Learning jobs grew 344% between 2015 to 2018 and have an average salary of $146,085. Similarly, the Computer Vision Engineers earn an average salary of $158,303, the highest salaries in tech.

If you want to learn Machine Learning, then this article about Free Machine Learning Courses will shed some light on how you can intellectually bootstrap your abilities and upgrade your skills to profitability in the rewarding field of Artificial Intelligence.

First off, If you are a beginner, you will need to have a solid background in Mathematics. So, If you need to learn the basics or advanced concepts, i’ve got you covered in this post about the Maths for Machine Learning.

However, If you are a beginner and your understanding about the Machine Learning is a big Question Mark, then i’d highly recommend that you navigate to this piece about the Beginner’s Guide to Machine Learning with Python.

I know the options out there, Machine Learning offers enormous potential and in this guide, we interrogate what’s possible for you. So, without further ado, let’s get started.


The 9 Best and Free Machine Learning Courses from World-Class Educators

Hands-on learning is the best way to gain real-world experience to learn Machine Learning.

I’ve compiled these free courses according to the student reviews, course outline and experience level.


1. Data Science: Machine Learning Harvard

This is an introductory course on Machine Learning for Data Science created by Harvard University (HarvardX) and delivered via edX by Professor Rafael Irizarry.

First, This course provides an introduction to machine learning from the field ion and helps learners to understand the important concepts behind performing cross-validation to avoid overtraining.

Next, You will also learn about the popular machine learning algorithms and understand the key concepts of regularization and why it is very useful in the field of machine learning.

Data Science: Machine Learning

Is it right for you?

If you have an intermediate skills in Python and basic understanding of Data Science, then this course is suitable for you to become highly prepared for the advanced Machine Learning Topics

By the end of this course, you will have gained a solid understanding of the basic concepts necessary to build a recommendation system and also become highly prepared for the intermediate data science courses.

GO TO edX

2. Intro to Machine Learning — Udacity

This Introductory course on Machine Learning is delivered via Udacity by Sebastian Thrun, Co-Founder of Udacity and Adjunct Professor at Stanford University, along with Katie Malone, who is a Director of Data Science Research & Development at Civic Analytics.

In this course, You will start with learning about the techniques to extract and identify useful features that best represent your data and how to analyze and train data sets.

Next, You will learn about the most important Machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.

Finally, You will also learn the end-to-end process of investigating data through machine learning lens.

Intro to Machine Learning

Is it right for you?

This free Introductory courses in Machine Learning is a highly recommended prerequisite course for Udacity’s Nano-Degree program for becoming a Machine Learning Engineer.

If you already have a basic understanding of Machine Learning and have some Programming experience and some background in Statistics for Data Science, then this course is very suitable for you.

Also, Hands-on exercises and projects are central to the course syllabus, so if you prefer hands-on learning, you’ll definitely get ahead in learning through this course.

GO TO UDACITY

3. Intro to Machine Learning — KAGGLE

This introductory Machine Learning course is designed by Dan Becker on Kaggle to equip learners with the better understanding of the core ideas in Machine Learning.

First, You will learn about the basics of Machine learning models and then perform some basic data exploration to analyze and train your data.

Next, You will build your first machine learning model and then perform cross-validation techniques and learn how to experiment with different models through the means of underfitting and overfitting methods to fine tune the performance of your models.

Finally, You will learn about the Random Forest and use some  sophisticated machine learning algorithms to deepen your understanding about the decision trees.

Decision Tree

Is it right for you?

If you have some experience in Python, then this course will equip you with the basic concepts of Machine Learning.

Upon the successful completion of this course and Machine Learning Completion on Kaggle, you will become highly prepared for the 3 Micro-Courses offered by Kaggle in Deep Learning, Machine Learning and Expandability.

GO TO KAGGLE


4. Machine Learning with Python: A Practical Introduction — IBM

This Machine Learning with Python course dives into the basics of Machine Learning using Python, created by IBM and delivered via edX by Saeed Aghabozorgi.

First, You will learn about the Supervised vs Unsupervised Learning, and take a deep dive into understanding how Statistical Modeling relates to Machine Learning, by doing a comparison of each.

Next, You will explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, RMSE: Root Mean Squared Error and Random Forests.

Finally, You’ll look at real-life examples of Machine Learning and how you can apply these algorithms to accomplish the desired results that solve real problems in the society today.

Machine Learning with Python: A Practical Introduction

Is it right for you?

This introductory courses in machine learning assumes knowledge of Python and familiarity with the required prerequisites of Maths for Data Science and Machine Learning.

This course will help you gain practical skills through hands-on labs exercises and projects that will help you transform your knowledge to become well versed with optimally applying Machine Learning techniques into your Data Science and Deep Learning projects.

Upon the successful completion, you will have gained a solid understanding about Supervised and Unsupervised Machine Learning.

GO TO EDX


5. CS229: Machine Learning – Spring 2019 — Stanford

This course is offered by Stanford University, aims to provide an introduction to Machine Learning and will help you to understand the key concepts of statistical pattern recognition.

CS229: Machine Learning – Spring 2019

Stanford is one of the best places to learn Machine Learning in the World. If you are familiar with the subject, you might have heard about their highly recommended – Machine Learning Course by Andrew Ng, which has over 2 million enrollments.  

Is it right for you?

This course assumes an intermediate knowledge of computer science and advanced knowledge of Python for Data Science (Numpy), and basic familiarity of topics related to Mathematics for Machine Learning.

If you want to learn Python for Machine Learning, i’ve got you covered in this piece about Python for Machine learning.

By the end of this course, you will be equipped with a solid understanding of Supervised learning, Unsupervised learning, Computational learning theory, reinforcement learning and adaptive control.

GO TO STANFORD

6. Machine Learning with R  — IBM

This Machine Learning with R course is offered by IBM and delivered via CognitiveClass.ai by Saeed Aghabozorgi.

First, this course will give an introduction to the basics of machine learning using R programming language.

Next, You’ll learn about Supervised vs Unsupervised Learning and then take a deep look into how Statistical Modeling relates to Machine Learning. You will also learn to do a comparison of each in order to optimally enhance your Machine Learning projects.

Finally, You will learn to use Jupyter notebook and take a deeper look at the real-word examples of Machine learning and how it affects the world and changes the world faster.

Machine Learning with R

Is it right for you?

This course assumes good knowledge of R programming and basic familiarity of using Jupyter notebooks.

Hands-on lab and exercise is essential to the course syllabus, and you will richly benefit from the topics about Popular models  that include lessons on Train/Test Split, Root Mean Squared Error, and Random Forests.

Upon the successful completion of this course, You will be equipped with a solid understanding about the most popular machine learning algorithms like Classification, Regression, Clustering, and Dimensional Reduction.

GO TO COGNITIVE-CLASS  


7. Intermediate Machine Learning — Kaggle

This course is designed by Alexis Cook on Kaggle, to help learners understand how to handle missing values, non-numeric values, data leakage and more.

First, You will go through an introduction to get a big picture about what you will be learning and then complete an exercise about it. After that, You will learn about missing values and also how you can prepare yourself about the common or some unwanted challenges in the real datasets.

Next, You will also learn about the Categorical Values and how to use it for machine learning, and also learn about Pipelines which is a critical skill for deploying (and even testing) complex models with pre-processing.

Finally, You will learn about the better way to test your models through Cross-Validation methods and employ XGBoost which is the most accurate modeling technique for structured data. You will also about the Data Leakage and learn how to find and fix this problem that ruin your model in unimaginable ways

Intermediate Machine Learning

Is it right for you?

This course is suitable for learners with experience in Python programming and a solid background in Mathematics.

Upon the successful completion of this course, you will be equipped to create Machine Learning models that are more accurate and useful.

GO TO KAGGLE


8. Advanced Machine Learning  — FutureLearn

This course Advanced Machine Learning is designed by The Open University and Persontyle and delivered via FutureLearn by Michael Ashcroft.

In this course, you will first improve your understanding of the machine learning methods applied. You will do this by exploring supervised learning statistical modeling algorithms for classification and regression problems.

Next, you will examine how Machine Learning algorithms are related, and how models generated by them can be tuned and evaluated so that you can optimally use them in your data science projects.

Finally, with hands-on exercise, you will explore the advanced techniques of Machine Learning and you will also look at feature engineering and how to analyse sufficiency of the data.

Advanced Machine Learning

Is it right for you?

If your understanding of Mathematics is solid, then this course will help you to obtain additional insights into the Machine Learning methods used to solve real-world problems.

You also need to have a good understanding of R Programming to be able to achieve the learning outcomes of the course.

If you want to learn R, I have written a piece on R for Data Science Courses, designed to equip you with the better understanding of key topics required to learn Machine Learning with R Programming.

Upon the successful completion, you will be able to apply a range of advanced machine learning techniques from all major areas of machine learning including but not limited to supervised, unsupervised, semi-supervised ]and reinforcement learning and also tuning and regularizing these models.

GO TO FUTURE LEARN


9. Machine Learning Crash Course — Google AI

Machine Learning crash course offered by Google AI team aims to introduce learners to Google’s best practices in machine learning.

This course is designed by Peter Norvig, who is a Computer Scientist and Director of Research at Google INC.

First, You will learn to  apply fundamental machine learning concepts, gain real-world experience with the companion Kaggle competition.

Next, You can also Learn with Google AI to explore the full library of Machine Learning training resources to become a practitioner of the art.

Finally, You will learn the best practices from Google experts on key machine learning concepts and building effective recommendation systems.

Machine Learning Crash Course

Is it right for you?

If you have more than 2 years of experience in programming and solid background in Mathematics, this course will definitely get ahead.

You will richly benefit from this course and come to a better understanding of recognizing the practical benefits of mastering machine learning and understand the philosophy behind machine learning.

This course also features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.

Also, Google Cloud is offering 2 Specializations  Machine Learning with TensorFlow in their Google Cloud Certification Training Program. However, if you specifically want to learn Machine Learning and Deep Learning with TensorFlow, i’ve got you covered in this article about the Best TensorFlow Courses.

GO TO GOOGLE-AI


Thanks for making it to the end 😉

If you liked this article, I’ve got a few practical reads for you. One about the Machine Learning Cheat Sheets and one about the Best Machine Learning Courses (with a Specialization Certificate) on the Internet.

Also, If you want us to audit a course that you feel should be listed in this article, please leave a comment below and we will be happy to do so.  

Cheers !


Image Credits: Kaggle, IBM, CognitiveClass, Stanford, edXBanner Image Credits: sinxLoud

Sanjay Kumar

Mr. Sanjay is no new to the marketing world where his work speaks it all. As a certified inbound marketer, his contributions shine on web pages helping startups and established firm to acquire their motives and gains. Business wisdom put into practice is what is the personality is known about, making him the first pick of many.

This Post Has 7 Comments

  1. Thank you for the lidrññst. But how can you acces Stanford ml course? I only see available the Coursera version, which is more simple and Octave is used..

  2. Thank you for the list. But how can you acces Stanford ml course? I only see available the Coursera version, which is more simple and Octave is used..

      1. Thank you so much for the reply. I found that link too, the explanations are gold. However, it is an old version (2007 I think), and thus the content is not updated. In addition the code with code in Octave. The one you listed is the 2019 version and code is in Python, but I can’t find it open, I think it’s only available for Stanford students enrolled in the course.

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