The 10 Best Machine Learning Courses to Launch Your AI Career in 2022

Machine learning is eating the world. The global machine learning market is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8% [^1]. As companies across every industry race to adopt AI and big data, the demand for skilled machine learning practitioners is exploding.

[^1]: Fortune Business Insights. (2022). Machine Learning Market Size, Share & COVID-19 Impact Analysis. https://www.fortunebusinessinsights.com/machine-learning-market-102226

For software developers and technical professionals, machine learning represents a massive opportunity. According to Indeed, the average base salary for a Machine Learning Engineer in the United States is $149,801 [^2]. And globally, data scientists and machine learning specialists are among the top emerging jobs on LinkedIn [^3].

[^2]: Indeed. (2022). Machine Learning Engineer Salaries in the United States. https://www.indeed.com/career/machine-learning-engineer/salaries
[^3]: LinkedIn. (2020). 2020 Emerging Jobs Report. https://business.linkedin.com/talent-solutions/emerging-jobs-report

But breaking into machine learning can seem daunting, especially for those coming from a traditional software development background. With so many courses, books, papers, and resources out there, where do you even start?

Having worked as a software engineer, engineering manager, and now developer advocate at a machine learning platform startup, I‘ve had the chance to explore many different paths to learning ML. And over the years, I‘ve consistently seen three key factors that make certain courses stand out:

1. Taught by world-class instructors who are both subject matter experts and excellent teachers. Look for courses from established universities, reputable companies, and well-known practitioners.

2. Provide hands-on coding practice on real datasets and projects. Avoid courses that are too theoretical or math-heavy. The best way to learn ML is to implement and experiment with the algorithms yourself.

3. Balance breadth and depth in content. You want a solid foundation in core concepts like supervised vs. unsupervised learning, generalization, overfitting, cross-validation, etc. But you also want exposure to different problem domains to see how ML is applied.

With those criteria in mind, I‘ve curated this list of the 10 best machine learning courses for software developers and programmers to take in 2022. I believe these courses offer the optimal blend of rigor and flexibility to build job-ready ML skills, no matter your current level of experience.

1. Machine Learning (Stanford)

Instructor	Andrew Ng
Offered By	Stanford University on Coursera
Level	Intermediate
Ratings	4.9 / 5.0 (243k+ ratings)
Workload	11 weeks / 5-7 hours per week
Enrolled	4.9M+ students
Cost	Free to audit, $79 for certificate

The OG ML course that started it all. Initially offered in 2011, Andrew Ng‘s Machine Learning course on Coursera is still the most popular and highly-rated intro to ML. Over 4.9M students have enrolled to date, and it has a staggering 4.9/5.0 average rating from 243k+ reviews.

Taught by Andrew Ng, co-founder of Coursera and founding lead of Google Brain, the course covers all the essential machine learning concepts and techniques:

• Supervised learning (linear regression, logistic regression, neural networks, SVMs)
• Unsupervised learning (clustering, dimensionality reduction, recommender systems)
• Best practices (bias/variance, regularization, evaluation metrics, learning curves)
• Programming exercises in Octave/MATLAB

One unique aspect is the focus on the "big picture" and intuition behind the algorithms. You‘ll learn how to diagnose errors, prioritize improvement options, and build large ML systems. There‘s just enough math to understand the core concepts without getting bogged down in proofs.

The course assumes basic linear algebra and programming skills, but there are optional refresher videos for those topics as well. It‘s very well-structured and engaging, with each week building on the previous ones while introducing new concepts gradually.

Perhaps the best part is you can view all the lecture videos and complete the programming assignments for free. The only cost is for the optional certificate of completion.

While the course does use Octave/MATLAB for the code examples, the real value is in learning the underlying algorithms which can be implemented in any language. I took this course with no Octave experience and just referred to the docs to complete the assignments. The ML knowledge is fully transferable to Python, R, etc.

If you only take one machine learning course, this is the one I recommend. It provides an excellent foundation in all the essential ideas in an accessible format. From there you can dive deeper into specific areas and more advanced topics.

2. Machine Learning Specialization (University of Washington)

Instructor	Emily Fox, Carlos Guestrin
Offered By	University of Washington on Coursera
Level	Intermediate
Ratings	4.6 / 5.0 (15k+ ratings)
Workload	4 courses / 8-12 hours per course
Enrolled	422k+ students
Cost	Free to audit, $49/month for certificate

If you‘re looking for a more hands-on, project-based approach to learning ML, the University of Washington‘s Machine Learning Specialization on Coursera is an excellent option. Spread across four courses, the specialization covers the same key concepts as Andrew Ng‘s course but with an emphasis on real-world applications in Python.

Taught by Emily Fox and Carlos Guestrin, both Amazon Professors of Machine Learning at UW, the specialization takes a unique "case study" approach. Each course focuses on a different real-world problem and guides you through the ML workflow:

1. Machine Learning Foundations – Sentiment analysis of reviews
2. Regression – Predicting house prices
3. Classification – Classifying loan applications, digit recognition
4. Clustering & Retrieval – Document retrieval, image compression, grouping

Within each case study, you‘ll go through every step of the process: data exploration, feature engineering, model selection, evaluation, and optimization. You‘ll implement a variety of algorithms from scratch in Python, using libraries like NumPy, SciPy, and matplotlib. It‘s very hands-on and practical.

Another unique aspect is the focus on communication and interpretation of results. In addition to the code, you‘ll also create visualizations and written reports to share your findings. This is great practice for real data science projects and portfolios.

The specialization assumes an intermediate programming background in Python, as well as some prior coursework in calculus, linear algebra, probability, and statistics. It‘s more advanced than Andrew Ng‘s course, but still very accessible for those with the prereqs.

Each course takes about 6-8 weeks to complete at 4-6 hours per week, so the full specialization is a significant time investment. But it‘s well worth it for the practical experience and deeper understanding of the ML workflow. You can also choose to take just the individual courses that interest you most.

Even though the courses use an older library (GraphLab) in some examples, all the assignments can be completed using open-source Python tools. And the case study format means the skills are highly transferable to other domains and datasets.

Overall, the UW Machine Learning Specialization is the perfect blend of theory and practice, balancing clear explanations with hands-on coding experience. If you want to go beyond the basics and see how ML is really applied, I highly recommend it.

3. Deep Learning Specialization (deeplearning.ai)

Instructor	Andrew Ng
Offered By	deeplearning.ai on Coursera
Level	Intermediate
Ratings	4.8 / 5.0 (422k+ ratings)
Workload	5 courses / 5-10 hours per course
Enrolled	1.2M+ students
Cost	Free to audit, $49/month for certificate

Ready to dive into the hottest area of machine learning? Look no further than the Deep Learning Specialization from Andrew Ng‘s education company deeplearning.ai. Building on the foundation laid in his original ML course, this 5-course specialization provides a comprehensive introduction to neural networks and deep learning.

The specialization covers both the theory and practice of deep learning, with a focus on real-world applications in computer vision, natural language processing, speech recognition, and more. You‘ll learn about:

• Neural network architectures (CNNs, RNNs, LSTMs, etc.)
• Training techniques (initialization, regularization, optimization algorithms)
• Practical advice (setting up ML projects, debugging, innovation)
• Programming assignments in Python with TensorFlow and Keras

Each course includes a mix of video lectures, quizzes, and coding exercises to reinforce the concepts. The assignments are particularly well-designed, guiding you through the process of building and training deep learning models on real data.

One unique aspect of the specialization is the "Heroes of Deep Learning" interview series, where Andrew chats with leading experts like Geoffrey Hinton, Yoshua Bengio, and Ian Goodfellow. It‘s a great way to learn about the history and cutting-edge advancements directly from the pioneers.

While the specialization is more advanced than the original ML course, it‘s still very accessible for those with some prior exposure to ML and intermediate Python skills. The first course also includes an optional refresher on the math (linear algebra, probability) used in DL.

The workload is significant, with each course taking 3-5 weeks to complete at 4-6 hours per week. But the pacing is good, with each course building on the previous ones while introducing new concepts gradually. It‘s a challenging but rewarding journey.

Deeplearning.ai also offers several other excellent AI-related specializations, including the new Machine Learning Engineering for Production (MLOps) Specialization for those interested in deploying and managing ML systems at scale. Andrew Ng is an incredible teacher, and his courses never disappoint.

Other Excellent Machine Learning Courses

While the three specializations above are my top recommendations, there are many other great options depending on your background, goals, and interests. Here are a few more to consider:

Course	Offered By	Level	Focus
Machine Learning	Columbia University on edX	Intermediate	Advanced ML methods (probabilistic graphical models, RL, etc.)
Machine Learning with Python	IBM on Coursera	Beginner	Hands-on ML in Python with scikit-learn
Machine Learning for All	University of London on Coursera	Beginner	Accessible ML without programming required
Introduction to ML for Coders	fast.ai	Intermediate	Top-down, code-first approach to ML using PyTorch
ML Crash Course	Google	Beginner	Quick practical intro to core ML concepts in TensorFlow
Machine Learning A-Z	Udemy	Beginner	Comprehensive ML intro with Python and R code
Mathematics for Machine Learning	Imperial College London on Coursera	Intermediate	In-depth coverage of the math used in ML
ML Engineering for Production	deeplearning.ai on Coursera	Advanced	Building & deploying scalable, reliable ML pipelines

The key is to just get started and not get paralyzed trying to find the "perfect" course. You‘ll learn the most by doing, so pick a course that matches your current skills and dive in. Once you have the fundamentals down, you can explore more specialized topics based on your interests.

How to Keep Learning After Taking These Courses

These courses will give you an excellent foundation in machine learning, but the field is rapidly evolving. To stay current and deepen your knowledge, I recommend:

1. Work on projects – Apply what you‘ve learned to real-world datasets and problems. Participate in Kaggle competitions, contribute to open-source projects, or create your own MVPs. Build a public portfolio on GitHub to demonstrate your skills.

2. Read papers & blogs – Follow ML researchers and practitioners to see what new techniques and architectures are being developed. The Papers With Code site is a great resource for finding state-of-the-art models with code implementations.

3. Attend conferences & meetups – Engage with the ML community at events like NeurIPS, ICML, and local gatherings. Learn from experts, network with peers, and stay motivated. Many conferences now have virtual options as well.

4. Take advanced courses – Branch out into specific domains like computer vision, NLP, or reinforcement learning. The fast.ai and deeplearning.ai catalogs offer great in-depth courses in these areas.

5. Teach others – One of the best ways to solidify your knowledge is to explain it to someone else. Write blog posts, create tutorials, give talks, or mentor others. You‘ll identify gaps in your understanding and learn even more in the process.

Remember, machine learning is a vast field and you‘ll never stop learning. The key is to stay curious, keep experimenting, and have fun! With the right foundation and mindset, you‘ll be well on your way to becoming a proficient ML practitioner.

Conclusion

I hope this guide has given you a clearer idea of the best machine learning courses to take in 2022 and beyond. Whether you‘re a complete beginner, an experienced developer looking to add ML to your toolkit, or a data scientist seeking to level up your skills, there‘s a course on this list for you.

Machine learning is an incredibly powerful technology that‘s transforming industries and creating new opportunities for innovation. By investing in your ML education, you‘re positioning yourself for a successful and impactful career at the forefront of this exciting field.

So what are you waiting for? Pick a course, carve out some dedicated learning time, and start your machine learning journey today. Trust me, it‘ll be one of the best investments you ever make in your professional and personal growth. I can‘t wait to see what you‘ll build!

This post was written by Manoel Horta Ribeiro, a former software engineer and current developer advocate at Neptune.ai. Manoel is passionate about democratizing AI and empowering developers to build ML-powered applications. Follow him on Twitter and GitHub for more ML content and updates.