Machine Learning 101: The Top 6 Machine Learning Courses From Beginner to Expert

Machine learning (ML) and artificial intelligence (AI) have raised brows over the recent years as engineers continue to push boundaries on the role of ML in our everyday lives.

From simple spam recognition and fraud analysis to the creation of DeepMind – a machine that mimics human thought processes and has even successfully beaten people at games –machine learning has experienced widespread adoption across multiple industries, and the benefits aren’t too bad either.

Machine learning engineers earn on average $147,593 a year, according to Indeed, with the potential to earn up to $200k and $300k annually.

If you’re interested in learning more about this profitable career, then look no further as we break down the best online machine learning courses. We’ll cover course content, price, strengths, weaknesses, and range of classes suited for beginners and professionals.  

But First…What Is Machine Learning?

Machine learning is a branch of artificial intelligence that specifically focuses on the use of data and algorithms to imitate and implement human learning capabilities. Coined in 1959 by Arthur Samuel, it’s essentially about computers recognizing patterns, then learning tasks from those patterns, and performing them without being explicitly told to.

What makes ML so important is that it keeps unlocking the value of data. Data-based decision-making is a key component in every industry, helping jobs like risk analysis, predictive maintenance, disease identification, and supply optimization.

Machine learning is also very much a science of the future, with sights set on an ML-based personalized internet, as well as efforts of using it to combat climate change. Needless to say, now is an exciting time to get involved in this lucrative business or upskill to keep up with growing innovations.

How to Choose a Machine Learning Course

With so many online learning sites, navigating the traffic to find the right course is a bit of a headache, but here are three ways to help make it easier.

First, you want a course with interactive learning. Learning by doing not only helps you retain information better, but it makes the course more enjoyable. You should walk away from a class feeling secure in the practice it’s given you and confident applying to ML engineering roles.

Second, choose a course that suits your goals. Aside from beginner and advanced topics, you want to study subjects geared towards your career goals, whether that’s data science, deep learning, or even specialized skills like data modeling.

Finally, make sure the price is right. While machine learning itself offers a high-income future, we all have to pay our dues, so choose a course that won’t break the bank!

Best Beginner Machine Learning Courses

1. freeCodeCamp’s Machine Learning with Python: Best Free Machine Learning Course

  • Skill Level: Beginner to Intermediate
  • Pros: Free machine learning certification
  • Cons: No access to guided support

FreeCodeCamp is a non-profit online education site with one goal in mind, to provide users a free pathway to software developer jobs. So, when we say this is the best free machine learning certification available, we mean it.

freeCodeCamp's machine learning course

FreeCodeCamp’s Machine Learning with Python is a beginner’s dream as this three-part certification teaches you the fundamentals of TensorFlow, the principle open-source framework used throughout ML.

The first two parts focus on the “what” and “how” of machine learning.

Following a video lecture by Tim Ruscica, you’ll cover what algorithms, neural networks, natural language processing (NLP) with RNNS, and reinforcement learning with Q-Learning are, all inside the open-source framework.

Then, you’ll move on to the “how” with Brandon Rohrer. In this video course, Rohrer demystifies deep neural networks, recurrent neural networks (RNN), and long short-term memory (LSTM), and how they work in machine learning.

Finally, the course ends with a bit of hands-on practice.

You’ll complete 5 projects in Python that progressively get more advanced. You start with simple exercises like creating a Rock, Paper, Scissors game, then move on to crafting a book recommendation engine using KNN, linear regression health costs calculator, and neural network SMS text classifier.

Once all of this is complete, you’ll claim a certificate that’s shareable to your professional portfolios. Not all of freeCodeCamp’s courses come with a certification, so this package is quite a steal.

It’s great for beginners, and even intermediate developers, because of its incredibly thorough evaluation of TensorFlow and the moving parts of machine learning. Plus, since it’s free that also means it’s risk-free, so even if you’re still on the fence about machine learning, you can get a great sample of the environment here.

2. Udemy’s Machine Learning A-Z With Python and R in Data Science

  • Skill Level: Beginner
  • Pros: Covers Python and R
  • Cons: Only covers Python and R for machine learning, does not teach the whole language

Udemy’s known as a credible site for getting access to industry-standard online learning, and their bestselling Machine Learning A-Z: Python and R in Data Science is no exception.

Udemy's Python and R machine learning course

Created by data scientists Kirill Eremenko and Hadelin de Ponteves, who have a wealth of experience in the professional and business world of machine learning, this course has helped just under 800,000 students and counting.

The class consists of over 44 hours of on-demand video and 320 lectures split into 10 parts, and when purchased for $64.99 you’ll receive lifetime access and a shareable certificate.

You’ll begin with the basics, learning about data processing, regressions, classifications, clustering, NLP, and reinforcement learning. Then, you’ll learn about more advanced topics like dimensionality reduction, how to effectively conduct model selection and boosting, as well as using the updated TensorFlow 2.0 for deep learning.

Throughout, you’ll make your own robust machine learning models and study in an environment geared towards data science and a future as a data scientist.

What makes this a unique opportunity is the practice using Python and R, the two industry-standard programming languages for machine learning and data science. You’ll even have access to downloadable Python and R templates.

This course is a perfect choice for beginners looking to work in data science or Python programmers who want to switch to data science. However, it does not teach all Python or R skills, only what’s necessary for the course.

If you’re new to Python, check out our article on beginner courses here

3. Coursera’s Machine Learning Course: Best Beginner Course

  • Skill Level: Beginner
  • Pros: University accredited course
  • Cons: Not as comprehensive as other courses

Coursera is an online site for taking university-backed courses without the stress (or cost) of university. And they just so happen to have a beginner Machine Learning Course by the top-rated instructor Andrew Ng.

Coursera's beginner machine learning course

Offered by Stanford University, this course has taught over 4 million students, and is broken down into 11 weeks. However, it is flexible learning and you work at your own pace, but overall it should take approximately 61 hours to complete.

You’ll cover typical topics like linear regressions, neural networks, and dimensionality reductions, but you’ll also cover more unique subjects like vector machines, ML system design, anomaly detections, and large-scale machine learning.

The course draws on numerous case studies for its approach and aims to shape and give advice on applying machine learning. It costs $79 for the shareable certificate but the course is free to audit, which means you can read through the material, but you won’t have access to any hands-on practice or videos.

This is the best beginner’s course because of its solid focus on fundamentals that’ll help you easily move on to more advanced techniques and specializations in machine learning. We also recommend this for anyone wanting a university-accredited course and instructor.

4. Google’s Machine Learning Crash Course

  • Skill Level: Intermediate
  • Pros: Created by Google and free!
  • Cons: Fast-paced, not for true beginners

Why not go straight to the hands of one of the industry’s top innovators with Google’s Machine Learning Crash Course.

Google's machine learning crash course

Google is the mastermind behind TensorFlow, so it makes sense it would create an ML TensorFlow crash course, using TensorFlow API’s. With 15 hours comprised of 30 exercises, 25 lessons, Google researcher-led video courses, and interactive visualizations of algorithms in action, this is the perfect way to get an inside look at the industry. And did we mention it’s free?

Using Python as the programming language, and Jupyter as an interactive notebook, you’ll study linear and logistic regression, classification, reducing loss, overfitting, model performance metrics, as well as embedding and single and multiclass neural networks.

Overall, it’s a very comprehensive crash course, providing information from the industry-leading source, designed for those interested in machine learning engineering.

We recommend this for those with some experience, as the class discusses many nuances for machine learning that may otherwise take hundreds of hours to learn on your own, and it doesn’t offer any certification.   

Best Advanced Machine Learning Courses

5. Udemy’s AI Advanced Machine Learning Course  

  • Skill level: Advanced
  • Pros: Covers advanced data science techniques
  • Cons: Doesn’t cover specialized areas of ML like deep learning

Udemy’s Artificial Intelligence: Advanced Machine Learning course is the ideal solution for data scientists looking to upskill their techniques.

Udemy's advanced AI machine learning course

Created by Eduero Academy, an online educator focused on technical training, this quick and efficient course offers nearly 4 hours of content, divided into 46 lectures. For $59.99, you’ll receive lifetime access as well as a sharable certificate upon completion.

It covers advanced topics like model complexity, pipeline, imbalanced classes and metrics, and model selection of unsupervised learning. Throughout the course, you’ll also handle real data, text data, and out of core learning.

The course focuses on advanced algorithms, data visualizations, and data analysis, taking a deep dive into data analytics. You should come out the other side building efficient models capable of carrying out advanced tasks and knowing how to predict values of continuous variables.

This class is taught with Python and is best for advanced learners who want to upskill their interaction with data and boost efficiency with model handling.

6. Coursera’s Advanced Machine Learning Specialization: Best Advanced Machine Learning Course

  • Skill Level: Advanced
  • Pros: Built for those already working in the industry
  • Cons: Large time commitment

Coursera’s Advanced Machine Learning Specialization is truthfully the complete package for advanced ML engineers.

Sponsored by HSE University, the top research university in Russia, and taught by HSE instructors, this 7-part specialization takes approximately 10 months to complete, but does offer flexible learning and a certificate for $79.

With Python as its programming language, the courses are: 

  • Course 1: Introduction to Deep Learning
  • Course 2: How to Win a Data Science Competition: Learn from Top Kagglers
  • Course 3: Bayesian Methods for Machine Learning
  • Course 4: Practical Reinforcement Learning
  • Course 5: Deep Learning in Computer Vision
  • Course 6: Natural Language Processing
  • Course 7: Addressing Large Hadron Collider Challenges by Machine Learning

Typically, Coursera specializations offer courses that are dependent on one another, but this one offers 7 completely independent advanced classes. So, bear with us as we attempt to boil down these jam-packed courses full of juicy material.

Coursera's advanced ML specialization

Course 1 is on the easier side, focused on introducing you to modern neural networks and how they’re applied in computer vision and natural language understanding. It also recaps linear models and teaches you how to define complex modern architectures in TensorFlow and Keras. It finishes off with a project where you apply deep neural networks to image captioning.

Course 2 is actually geared toward competitive data science, so you’ll compete in predictive modeling competitions, learn how to preprocess data, and advance feature engineering techniques like generating mean-encodings. You’ll also form reliable cross validation methodologies, learn advanced algorithms, and practice analyzing and interpreting data, as well as combining different ML models.

Course 3 is pretty straightforward, you’ll learn how to apply Bayesian methods and practice applying them to game development and drug discovery. Bayesian methods essentially give superpowers to ML algorithms by compressing models a hundred-fold and automating workflow.

Course 4 teaches the foundations of RL methods, teaching you deep neural networks for RL tasks and state-of-the-art RL algorithms. You’ll practice by teaching neural networks to play games.

Course 5 covers advanced deep learning and computer vision topics. You’ll learn new deep learning techniques and computer vision applications, like facial recognition, indexing, photo stylization, and machine vision in self-driving cars. You’ll also get to build your own facial recognition and manipulation system.

Course 6 is all about NLP and balancing traditional and deep learning techniques. Going from basic to advanced subjects you’ll learn sentiment analysis, summarization, and dialogue state tracking. By the end, you’ll be able to recognize NLP tasks in day-to-day work, choose the best techniques, and build your own conversational chatbot.

And last but not least, Course 7 teaches you about the Large Hadron Collider, currently the largest data generation machine. Here you learn about gigantic data and how to use sophisticated data processing techniques to handle large data. Using the main concept of physics and data flow, it shows you machine learning techniques in data processing pipelines like track pattern recognition, particle identification, and online real-time processing.

If you’re still with us, the truly formidable thing about this specialization is that you can pick and choose which courses you want to take. You are by no means obligated to take all of them, although it certainly couldn’t hurt.

Overall, my interpretation doesn’t do this specialization justice with just how much great material is laden within it. These courses are best for industry professionals looking to modernize and strengthen their current ML skills.

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