10 best Machine Learning Courses to Take (2023 Guide)

Machine learning is one of the most interesting areas of computer science to work in. It applies to tons of industries, applications, and projects — which means you can likely find a job opportunity that fits your passions and interests while working in a super cutting-edge field.

Arthur Samuel coined the term “Machine Learning” in 1959 and defined it as a “Field of study that gives computers the capability to learn without being explicitly programmed”.

And that was the beginning of Machine Learning! In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According to a survey, Machine Learning Engineer Is The Best Job of the decade and is expected to grow YoY by 22% (between 2020-2030) and an average base salary of $122,000 per year in the USA and INR 8.5 LPA in India.

What is Machine Learning?

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand-holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data we have and what kind of task we are trying to automate.

Why Do We use Machine Learning?

As we’re moving forward in the digital world, a massive amount of data is being generated every single minute along with the accessibility of high-speed internet. This is the major factor to develop automated systems that can handle data at such a threshold by accurately using different algorithms for complex data sets. Today, companies of all scales are using this method to handle cost management, lower risk, and also help in improving the quality of their product and services. This technology has been widely accepted in many industries today and soon it’s going to be a major part of our lives. (which has already begun)

1. Machine Learning A-Z™: Python & R in Data Science [2022]

True to its name, this Udemy course is a comprehensive but practical introduction to machine learning. It slowly works its way up from data preprocessing to model validation, but glosses over some of the underlying math.

If you want to jump straight into “doing”, this course might be a good fit.

The course starts by covering various types of regression, classification, and clustering models. It discusses reinforcement learning as well as natural language processing, and it covers the fundamentals of artificial neural networks.

The course uses the Python and R programming languages, and the TensorFlow machine learning library.

This course includes:

• 42.5 hours of on-demand video

• 38 articles

• 9 downloadable resources

• Certificate of completion

• 908,457 students already enrolled

• 4.5 (164002) ratings

Interested to Enroll?

If yes, then check it out here– Machine Learning A-Z™: Python & R in Data Science [2022]

2. Machine Learning Foundations: A Case Study Approach (University of Washington)

My second pick for the best machine learning online course is Machine Learning Foundations: A Case Study Approach, offered by the University of Washington on Coursera.

The course starts by contextualizing machine learning: explaining what machine learning is, going over some of its applications, and making a case for its importance in the future.

The course introduction also takes the time to cover Python fundamentals as well as the rudiments of tools like Jupyter Notebooks.

The course then moves from case study to case study, using each one to illustrate a particular facet of machine learning: you use regressions to predict house prices, you use classification to evaluate sentiments in user reviews, you use clustering for grouping related articles, you use deep learning to identify objects in images, and so on.

If you’re someone that likes to learn through examples, the clear mapping between tasks and concepts in this course might help make the subject more palatable to you.

By the end of this course, you’ll understand fundamental machine-learning tasks like regression, classification, and clustering, and you’ll know when to use each technique.

You’ll know how to extract features from data and use these as inputs for your models. You’ll be able to evaluate your model's correctness using well-defined error metrics. And you’ll be able to implement machine learning applications end-to-end in Python.

This course includes:

• Certificate of completion

• Shareable Certificate

• 4.6 (13,191) ratings

• Approx. 18 hours to complete

• 367,015 students already enrolled

Interested to Enroll?

If yes, then check it out here– Machine Learning Foundations: A Case Study Approach (University of Washington)

3. Machine Learning for All (University of London)

My third pick for the best machine learning online course is Machine Learning for All, offered by the University of London on Coursera.

This course starts by explaining what artificial intelligence and machine learning are and how these disciplines are connected.

It discusses various real-world applications of machine learning, including AlphaGo, a machine-learning program capable of beating the best Go players in the world. It explains data representation, how to set up a machine learning project, and some of the opportunities and ethical considerations of machine learning.

Finally, the course invites you to implement a machine-learning project by collecting data, training a model, and putting it to the test.

By the end of the course, you’ll be equipped with a broad understanding of machine learning, its various uses, and its significance for the future.

You’ll be familiar with the most important technical concepts that underpin machine learning. You’ll have a high-level grasp of the process of building a machine-learning model, from data collection to model evaluation.

And you’ll be prepared to tackle more advanced, theoretical courses on machine learning.

This course includes:

• Certificate of completion

• Shareable Certificate

• 4.7 (3,007) ratings

• Approx. 22 hours to complete

• 132,342 students already enrolled

Interested to Enroll?

If yes, then check it out here– Machine Learning for All (University of London)

4. Machine Learning with Python (IBM)

This course offered by IBM on Coursera teaches machine learning through a hands-on approach using Python, which is nowadays the de facto programming language of artificial intelligence.

Beware, this course will throw math at you. If your calculus is rusty, you might want to brush up on that before taking this course.

The course starts by covering machine learning fundamentals and applications in fields such as healthcare, banking, and telecommunications. And it explains the difference between supervised and unsupervised learning and goes over which type of learning is suitable for which type of task.

Each week is dedicated to one of the broad machine learning tasks — regression, clustering, and classification — and the various methods that can be used to implement them, such as decision trees, support vector machines, and k-means.

By the end of the course, you’ll have covered a lot of ground in terms of the mathematical underpinnings of machine learning. You’ll be familiar with a large number of applications of machine learning in fields ranging from healthcare to high-performance computing.

You’ll be able to implement a tapestry of machine learning algorithms using Python. And you’ll have practiced using machine learning libraries such as scikit-learn and SciPy.

This course includes:

• Certificate of completion

• Shareable Certificate

• 4.7 (13,458) ratings

• Approx. 13 hours to complete

• 300,199 students already enrolled

Interested to Enroll?

If yes, then check it out here– Machine Learning with Python (IBM)

5. Intro to Machine Learning

This is a beginner-level free machine learning course on Udacity. In this course, you will get a complete understanding of machine learning basics. There is no prior experience is required to enroll in this Free course. Anyone who is a beginner can enroll in this course.

This course includes:

• Certificate of completion

• Course Cost Free

• Approx. 1 Week

• Skill level Beginner

Interested to Enroll?

If yes, then check it out here– Intro to Machine Learning

6. Machine Learning (Georgia Tech)

This course is offered by the Georgia Institute of Technology on Udacity, and it’s also offered as part of Georgia Tech’s Online Master of Computer Science (OMSCS).

This course is divided into three broad machine-learning tasks.

First, it covers supervised learning, discussing decision trees, regression and classification, and neural networks. Then, it covers unsupervised learning, discussing clustering, feature selection, and randomized optimization. Finally, it covers reinforcement learning, discussing Markov decision processes, game theory, and decision-making.

By the end of the course, you’ll have a comprehensive understanding of supervised, unsupervised, and reinforcement learning, and the differences between them.

You’ll learn methods tailored to each of these problems. And you’ll be able to implement methods to solve them, interpret the results of these methods, and evaluate their correctness.

This course includes:

• Certificate of completion

• Course Cost Free

• Approx. 4 Months

• Skill level Intermediate

• Taught by Industry Pros

• Self-Paced Learning

Interested to Enroll?

If yes, then check it out here– Machine Learning (Georgia Tech)

7. Machine Learning Crash Course with TensorFlow APIs (Google)

This course is offered by Google on their developer platform. While most of the courses in this ranking are academic and rather long, this one fits squarely into the category of hands-on introductions to machine learning.

The crash course begins by asking you about your background in machine learning. Depending on your answer, it will orient you toward the appropriate resources, so you can make the best use of your time.

Assuming you’re a complete beginner, you’ll start from square one. So your learning path will cover fundamental machine learning concepts, including regressions, loss functions, and gradient descent.

The course uses TensorFlow, Google’s popular machine-learning library. So rapidly the low-level details will be abstracted away by leveraging the library functions.

Some learners could see this as a negative since you can get away with not understanding how it all works under the hood. But if you're interested in quickly applying machine learning, this crash course should be right up your alley.

A thing to note is that the course also introduces neural networks, a topic many other short machine learning courses prefer to skip or barely touch since it's a topic worthy of its separate course.

Google’s crash course, however, is condensed enough to comfortably fit neural nets. But remember, it abstracts away lots of details, so if what you’re after is deep comprehension, you might be better served by another course.

This course includes:

• Interactive visualizations

• Real-world case studies

• 15 hours

• Course Cost Free

• Lectures from Google researchers

• 30+ exercises

• 25 lessons

Interested to Enroll?

If yes, then check it out here– Machine Learning Crash Course with TensorFlow APIs (Google)

8. Introduction to Machine Learning in Production (DeepLearning.AI)

After launching the machine learning course that tops this ranking and co-founding Coursera, Andrew Ng went on to create another company, DeepLearning.AI.

This course starts by discussing the lifecycle of a machine learning project and how to deploy production-ready machine learning systems.

Then, the course explains strategies to pick adequate models and train them, as well as some of the pitfalls to avoid when dealing with skewed data sets.

Finally, the course covers how to handle classification problems and how to establish a baseline to assess your model's performance.

This course includes:

• Advanced Level

• Approx. 12 hours to complete

• 67,641 Students already enrolled

• Certificate of completion

• 4.8 (2172) Ratings

Interested to Enroll?

If yes, then check it out here– Introduction to Machine Learning in Production (DeepLearning.AI)

9. Intro to Machine Learning with TensorFlow

In this Nano-Degree program, you will learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then this program will cover deep and unsupervised learning.

The best part of this program is that at each step, you will get practical experience by applying your skills to code exercises and projects.

There are 3 courses in this Nanodegree program, where you will learn about supervised machine learning algorithms such as Regression, Perceptron Algorithms, Decision Trees, Naive Bayes, Support Vector Machines, Evaluation Metrics, etc.

You will also learn Deep learning and learn how to build an image classifier. Then you will learn about unsupervised learning algorithms such as Clustering, Hierarchical, and Density-Based Clustering, Gaussian Mixture Models, and Dimensionality Reduction.

This course includes:

• Advanced Level

• 3 months At 10 hrs/week

• 4.7 (529) ratings

• Content co-created with Kaggle

• Real-world projects

Interested to Enroll?

If yes, then check it out here- Intro to Machine Learning with TensorFlow

10. Machine Learning Scientist with Python– Datacamp

This is a career track offered by Datacamp. There are 23 courses in this career track and begin with supervised learning with scikit learn. In this course, you will learn supervised, unsupervised, and deep learning.

Along with this, you will learn natural language processing, image processing, and libraries such as Spark and Keras.

In this career track, you will also learn how to approach and win Kaggle competitions.

This course includes:

• Type- Career Track

• Time to Complete- 93 hours

Interested to Enroll?

If yes, then check out the course details here- Machine Learning Scientist with Python

Conclusion

I hope these 10 Best Machine Learning Courses Online for Beginners will help you to enhance your machine learning skills.

All the Best!

Enjoy Learning!

Thanks for reading and I will see you in my next blog. Also, you can follow me here on Twitter and Instagram.