Last Updated on November 3, 2019

The demand for Machine Learning Engineers and Research Scientists is exploding.

Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning.

First off, The concepts of Machine Learning aren’t very difficult to grasp when they’re explained simply.

I want to highlight a simple question that can be highly underestimated.

What is Machine Learning?

The answer is, in one word, algorithms,.

Machine Learning is simply a way for computers to learn things without being specifically programmed and this is why we heavily invest time and energy on implementing algorithms, a set of rules that a computer is able to follow.

So, Mathematics is very important for Machine Learning and this is why I’ve already got you covered in this piece about Math for Machine Learning.

And, If your understanding about the Machine Learning is a big question mark, I’d highly recommend that you give this piece “Beginners Guide to Machine Learning with Python” a read.

Now, without further ado, let’s get started.

Best Machine Learning Courses from World-Class Educators.

This compilation of Best Machine Learning Courses and Specializations is suitable for Beginners, Intermediate learners as well as Advanced learners.

Machine Learning Offered by Stanford Highly Recommended

Created by Artificial Intelligence Pioneer – This is one of the best and highly recommended Machine Learning course across the internet. Andrew Ng , Co-Founder of Coursera, Landing AI and Adjunct Professor at Stanford University

In this course, you will learn about the most effective machine learning techniques, and gain practical experience implementing them and getting them to work for yourself.

Is it right for you?

This course is the perfect place for beginners to understand the core idea of teaching a computer to learn concepts using data without being explicitly programmed.

This Machine Learning Course offered by Stanford University provides an in-depth introduction to Machine Learning, helps you understand statistical modeling and discusses best practices for applying Machine Learning.

Introduction to Machine Learning with R Highly Recommended

Introduction to Machine Learning with R from DataCamp will help you in learning the true fundamentals of Machine Learning and experiment with the techniques.

This course is Created by Vincent Vankrunkelsven, A DataCamp Instructor with Masters in AI and Gilles Inghelbrecht, A Doctoral Student with Degree in Fundamental Mathematics and a solid background in Classical Statistics.

This course will help you gain a firm understanding in training of different machine learning models and learn three of the most basic machine learning tasks: classification, regression, and clustering.

Is it right for you?

DataCamp is a time flexible, online Data Science learning platform offering tutorials and courses in Data Science, Machine Learning AI and more.

Introduction to Machine Learning with R is for anyone with a solid basis in Statistics and R Programming. Upon the completion of this course, you’ll have a basic understanding of all the main principles.

This course on Machine Learning with Python will equip you to understand the concepts of using data to predict future events.

This course is Created by Jerry Kurata, Technology Expert and best selling author of Machine Learning and Deep Learning Courses on Pluralsight and Coursera.

In this Course, you will learn to build predictive models and use Python to perform Supervised learning with scikit-learn.

Is it right for you?

This Machine Learning Course is right for you, if you’re new to Machine Learning and have some experience in Python programming.

Understanding Machine Learning with Python course will hone your Machine Learning craft using sckit-learn, the most powerful Machine Learning library used by every Machine Learning Engineer and Data Scientist.

This is one of the highly rated course on the internet, delivered via Udemy by Kirill Eremenko Hadelin de Ponteves , and SuperDataScience Team

This “Machine Learning A-Z“, provides a great introduction to Machine Learning and teaches difficult to understand algorithms and getting on grips with scikit-learn and few other packages.

Also, the theoretical explanation is exceedingly clear, so are the practical examples.

Is it right for you?

If you are a beginner with basic knowledge of Python for Data Science or R programming and some high-school level knowledge of Math, then this course is perfect place to bootstrap your motivation to dive deep in Machine Learning.

Practical Machine Learning on H2O

This course “”, is designed by H2o.a i and delivered via Coursera by Darren Cook , who has an experienced software engineer, data analyst, and technical director, working on everything from financial trading systems to NLP, data visualization tools, and more.

In this course, you will be using linear models, random forest, GBMs and of course learn deep learning, as well as some unsupervised learning algorithms.

This course will help you to understand the techniques and key concepts to evaluate your models and choose the best model to suit not just your data but the other business restraints you may be under.

Is it right for you?

If you have no prior experience of machine learning, even if you dont have a solid background in math, this course is suitable for you.

Upon the successful completion of this course, you will have gained some practical experience to make machine learning models using a variety of algorithms.

This course ““, is designed by Duke university and delivered via Coursera by Lawrence Carin , who is a distinguished Professor of Electrical and Computer Engineering at Duke and one of the most prolific authors in the world in the fields of Machine Learning and Artificial Intelligence.

This course is designed to provide you a foundational understanding of machine learning models including but not limited to logistic regression, multilayer perceptrons, convolutional neural networks, and natural language processing.

With the help of demonstrations and hands-on exercises, you will also learn how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.

Is it right for you?

This course is for learners with background in Mathematics and some experience in programming.

Upon the successful completion of this course, you will have gained good understanding of how to implement machine learning algorithms with TensorFlow, open source libraries used by leading tech giants in the machine learning field like Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more.

This course is designed by Google cloud and delivered via Coursera by Google Cloud Technical Instructors who teach about Google Cloud Platform all over the world.

This is a one week accelerated on-demand course that introduces learners to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP).

Is it right for you?

If you have some experience in Python and SQL, then this course is suitable for you to learn about the data processing and machine learning capabilities on Google Cloud Platform.

This course is part of a Data Engineering Specialization offered by Google Cloud on Coursera.

Upon the successful completion of this course, you will be highly prepared for Data Engineering or Machine Learning Specialization.

Google Cloud also offers a specialization on Advanced Machine Learning with TensorFlow. However if you only want to Learn TensorFlow, i’ve got you covered in this piece about the Best TensorFlow Courses on the internet

Machine Learning Engineering Career Track Job Guarantee

This intensive AI/machine learning course—the first with a job guarantee—will equip you to transition into a role as a machine learning engineer.

The extensive curriculum was curated by machine learning experts with experience at companies like Airbnb and Intel.

While you’ll learn the advanced concepts machine learning engineers are expected to understand, you’ll also develop production engineering skills.

By the end of the program, you’ll have designed a machine learning system, built a prototype, and deployed an application that can be accessed via API or web service.

Is it right for you?

AI/Machine Learning Career Track was designed for people with a background in software engineering. It’s a self-guided, mentor-led, career-focused bootcamp complete with a job guarantee. If you don’t find a job within six months of graduating, you will get a refund for your tuition.

You’ll learn to apply machine learning and deep learning algorithms through case studies and projects.

You’ll have one-on-one guidance from your mentor, an experienced machine learning engineer, as you complete your coursework, build projects, and search for a job.

Machine Learning Specialization — Highly Recommended

Machine Learning Specialization

This ““, is offered by the University of Washington and delivered via Coursera to introduce learners to the exciting, high-demand field of Machine Learning.

This specialization comprises of 4 courses designed to equip and edify you with the skills to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

Is it right for you?

This specialization assumes background in programming and maths.

By the end of this specialization, you will have gained an applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval.

Advanced Machine Learning Specialization — Highly Recommended

This Specialization “Advanced Machine Learning”, is offered by National Research University Higher School of Economics via Coursera.

You will get an in-depth introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods.

You will also learn Top Kaggle machine learning practitioners and CERN scientists, who will share their experience of solving real-world problems and help you to fill the gaps between theory and practice.

Is it right for you?

If you have solid understanding of mathematics and experience programming, then this specialization is for you.

Upon completion of 7 courses, you will be better equipped with solid understanding of applying modern machine learning methods in enterprise and understand the caveats of real-world data and settings.

Thanks for making it to the end : -)

If you liked this article, I’ve got a few very practical reads for you. One about A Beginner’s Guide to Machine Learning with Python and one about Math for Machine Learning and Data Science Courses from the World-Class Educators.