Coursera machine learning courses + specializations with pink green and yellow

6 Best Machine Learning Courses and Specializations [Includes Andrew Ng Stanford Course!]

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TLDR: 6 Best Coursera
Machine Learning Courses

and Specializations

Sign up for courses and specializations using the links below. Check out the Table of Contents to learn more.

Beginner
💥 Machine Learning for All
💥 Mathematics for Machine Learning Specialization

Advanced Beginner
💥 Machine Learning with Andrew Ng

Intermediate
💥 Machine Learning with Python
💥 Machine Learning Specialization

Advanced
💥 Advanced Machine Learning Specialization

Sign up for Coursera here.

Coursera offers dozens machine learning courses and Specializations.

And we picked out the best ones.

Coursera Machine Learning Courses and Specialization for Beginners

Let’s dig into some beginner courses and Specializations (a Specialization on Coursera is a combination of courses in a specific discipline).

Machine Learning for All

⚠️ Level: Beginner
👨‍🏫 Modules: 4
📂 Projects: 1
📚 Learning style: Interactive text and video
⏲️ Estimated completion time: 21 hours
⌨️ Embedded code editor: Yes

Machine Learning for All is unique in that there is no programming in this course.

So it’s great for newbies.

You don’t need any previous programming experience.

Instead, you’ll learn the basics on how machine learning technologies work.

In addition, you’ll learn about the ethical dangers of machine learning.

Course Layout

Using a series of videos, readings and quizzes, you’ll learn about machine learning.

Video introduction to datasets in Machine Learning for All

Machine Learning for All covers 3 key concepts:

Machine Learning – learn about artificial intelligence and machine learning techniques

Data Features – learn how data representation affects machine learning

Machine Learning in Progress – learn how to test machine learning projects

Then you’ll work on your machine learning project.

Projects

In this Coursera machine learning course final project, you’ll collect a dataset, train a model and test it.

Establishing parameters for machine learning project in Machine Learning for All

Takeaway

Because you don’t need any programming experience, Machine Learning for All is a fantastic way to enter the world of machine learning.

Sign up for Machine Learning for All
👉 here. 👈

Mathematics for Machine Learning Specialization

⚠️ Level: Beginner
👨‍🏫 Courses: 3
📂 Projects: Mini projects
📚 Learning style: Interactive text and video
⏲️ Estimated completion time: 4 months
⌨️ Embedded code editor: Yes

Falling behind in math is a recipe for failure in machine learning.

Luckily, Mathematics for Machine Learning is a little different from the other Coursera courses and Specializations we feature here.

This Specialization covers nothing but prerequisite math for apps in data science and machine learning.

So if you’re not good at math, take this Specialization.

Specialization Layout

There are 3 courses in this machine learning Specialization:

Mathematics for Machine Learning: Linear Algebra – learn how linear algebra relates to vectors and matrices, implement ideas in code

Mathematics for Machine Learning: Multivariate Calculus – learn how to calculate vectors and build approximations for functions

Mathematics for Machine Learning: PCA – learn how to derive Principal Component Analysis (PCA)

Linear algebra video explanation in the Coursera machine learning specialization Machine Learning Specialization

Projects

You’ll work on mini projects with Python throughout Mathematics for Machine Learning.

Takeaway

If math is not your strong suit, we highly recommend you consider this course.

With its beginner and intermediate modules, you’ll work directly with math that you’ll be using throughout your machine learning career.

Sign up for Coursera’s Machine Learning Specialization
👉 here. 👈

Coursera Machine Learning Course for Advanced Beginners

Machine Learning

⚠️ Level: Advanced Beginner
👨‍🏫 Modules: 11
📂 Projects: See below
📚 Learning style: Interactive text and video
⏲️ Estimated completion time: 60 hours
⌨️ Embedded code editor: Yes

Ok, so we’ve all heard of this Andrew Ng Coursera course. It’s famous!

But what makes it so great?

Andrew Ng is a fantastic instructor.

And Machine Learning has over 3.7 million students to show for it.

Andrew will walk you through the absolute basics and best practices in machine learning all the way up to database mining and applying algorithms.

Note: Machine Learning is listed as Beginner on Coursera. But because it’s math-heavy and covers concepts in depth, we decided to categorize it as Advanced Beginner.

Course Layout

Similar to other Coursera machine learning courses, you’ll learn concepts with videos, articles and interactive quizzes.

Linear regression quiz in the Andrew Ng Coursera course Machine Learning

Machine Learning contains 11 modules including:

Linear Regression – one and multiple variables best practices and implementation

Neural Networks – representation and learning parameters using the backpropagation algorithm

Unsupervised Learning – learn about the k-Means algorithm for clustering

Large Scale Machine Learning – apply machine learning algorithms with large datasets

And more.

Lesson on linear regression with one variable in the Andrew Ng Coursera course Machine Learning

In addition, you’ll gain valuable insight into applying machine learning techniques.

Projects

You’ll work on mini projects throughout the modules in the course.

Takeaway

By the end of this course, you’ll gain skills in machine learning, logistic regression, algorithms and more.

🔥 Geena’s Hot Take:

Machine Learning is the machine learning course to take on Coursera. It’s thorough and concepts are expertly explained.

According to countless students, an Andrew Ng education can’t be beat!

Sign up for the Andrew Ng Coursera Machine Learning course
👉 here. 👈

Coursera Machine Learning Course and Specialization for Intermediate Students

Machine Learning with Python

⚠️ Level: Intermediate
👨‍🏫 Modules: 6
📂 Projects: 1
📚 Learning style: Interactive text and video
⏲️ Estimated completion time: 22 hours
⌨️ Embedded code editor: Yes

Machine Learning with Python has two key takeaways:

1. Learn how machine learning applies to the real world

2. Get an overview of supervised vs unsupervised learning, model evaluation and algorithms

Course Layout

As with all courses, you’ll watch a series of videos, read, and take interactive quizzes.

5 modules cover:

Introduction to Machine Learning – learn about machine learning applications in various career fields

Regression – learn about regression types and their applications

Classification – learn about classification technique using different classification algorithms

Clustering – learn how to use clustering for segmentation and grouping

Recommender Systems – learn about recommendation engine types

And then you’ll work on a final project.

Projects

There’s one project which combines all concepts you’ve learned in the course. And it’s fun because you’ll submit a report of your project for a peer evaluation.

Takeaway

Because you’re working with real-world scenarios, Machine Learning with Python makes more sense than practicing concepts without context.

Sign up for Machine Learning with Python
👉 here. 👈

Machine Learning Specialization

⚠️ Level: Intermediate
👨‍🏫 Courses: 4
📂 Projects: 1
📚 Learning style: Interactive text and video
⏲️ Estimated completion time: 7 months
⌨️ Embedded code editor: Yes

This Coursera Machine Learning Specialization uses a series of case studies where you can apply your machine learning skills.

Video explanation of logistic regression in the Coursera machine learning Specialization Machine Learning

You’ll learn to analyze complex datasets, create systems and build applications that can make predictions from data.

Specialization Layout

There are 4 courses in the Machine Learning Specialization. And each of them uses a real-world case study approach:

Machine Learning Foundations – Multiple

Machine Learning: Regression – Predicting House Prices

Machine Learning: Classification – Analyzing Sentiment & Loan Default Prediction

Machine Learning: Clustering & Retrieval – Finding Similar Documents

Regression quiz in the Coursera machine learning Specialization Machine Learning

Projects

In this Coursera machine learning Specialization, you’ll implement predictive, classification and clustering machine learning algorithms. And you’ll use real datasets to do so.

Takeaway

You’ll walk away from this Specialization with experience in applied machine learning and Python programming.

Sign up for the Coursera Machine Learning Specialization
👉 here. 👈

Coursera Machine Learning Specialization for Advanced Students

Advanced Machine Learning Specialization

⚠️ Level: Advanced
👨‍🏫 Courses: 7
📂 Projects: 3
📚 Learning style: Interactive text and video
⏲️ Estimated completion time: 10 months
⌨️ Embedded code editor: Yes

In Coursera’s Advanced Machine Learning Specialiation, you’ll learn about introductory concepts in deep learning, computer vision and Bayesian methods.

Explanation of Bayesian methods in the Coursera machine learning specialization Advanced Machine Learning

Then you’ll learn how to solve real-world problems and merge the gap between theory and practice.

Course Layout

You’ll watch videos, read articles and take interactive quizzes.

7 courses cover:

✅ Introduction to Deep Learning

✅ Bayesian Methods for Machine Learning

✅ Deep Learning in Computer Vision

✅ Natural Language Processing

And more.

Kaggle video explanations in the Coursera machine learning Specialization Advanced Machine Learning

Plus, you’ll learn how to win a data science competition from top Kagglers. Learn about the Kaggle community here.

And if that weren’t enough, you’ll also address Large Hadron Collider challenges with machine learning.

Machine Learning Course Projects

With 3 projects, you’ll master your skills by solving various real-world problems. And with hands-on experience, you’ll apply advanced machine learning techniques.

Takeaway

At the completion of this Specialization, you’ll be able to apply machine learning methods to enterprise. And you’ll understand real-world data and settings.

🔥 Geena’s Hot Take:

Advanced Machine Learning is kind of a big deal. It’s taught by over 20 instructors. And this includes CERN scientists and Kaggle machine learning practitioners.

So you’ve got industry experts sharing their hands-on experiences with you.

Sign up for the Coursera Advanced Machine Learning Specialization
👉 here. 👈

🤝 Community and Support

Coursera has a robust community where you can ask questions, search for jobs, get a study buddy, and beyond.

Coursera community page

🎖️ Certificate

There is a certificate for every completed Coursera machine learning course and Specialization.

💰 Cost

You can purchase Specializations for $49 per month.

But for $59 per month, you can access multiple Specializations and over 3000 courses.

Note: These prices are subject to change. There are multiple pricing structures within the Coursera ecosystem.

For more information on Coursera pricing, check here.

Coursera Machine Learning Courses: Conclusion

Today we showed you 6 Coursera machine learning courses and Specializations:

Beginner
Machine Learning for All

Mathematics for Machine Learning Specialization

Advanced Beginner
Machine Learning with Andrew Ng

Intermediate
Machine Learning with Python

Machine Learning Specialization

Advanced
Advanced Machine Learning Specialization

So whether you’re a newbie, need to refresh your math skills, are middle of the line or a pro, we’ve got something for every machine learning level.

Want to see some machine learning courses from other platforms?

We’re always reviewing courses on RealToughCandy.com. For example, we reviewed 10 machine learning courses from beginner to advanced. Some of the platforms include Educative, Zero to Mastery and DataCamp. Read our full review here.

  1. What's the best Coursera machine learning course?

    It all depends. If you're new to machine learning, you'll want to take a beginner course like Coursera's Machine Learning for All. If you're ready for something a little faster paced in the beginner-level range, check out Machine Learning by Andrew Ng. And if you're ready for an intermediate course, Machine Learning with Python is one of the highest-rated courses on Coursera.

  2. What's the best Coursera machine learning Specialization?

    If you need to brush up on your math skills, the beginner Coursera machine learning Specialization Mathmatics for Machine Learning might be a good place to start. But if you have the foundations of machine learning down, the Machine Learning Specialization is intermediate level. And if you're ready to hit the big time, sign up for the Advanced Machine Learning Specialization. It takes 10 months to complete.

  3. Where can I find the Andrew Ng Stanford course on machine learning?

    There is a beginner Coursera machine learning course by Andrew Ng called Machine Learning. This is a top-rated course with over 150,000 reviews to back up its stellar reputation. Whereas it's listed as a beginner course, we recommend taking Machine Learning for All as a prerequisite.