11 Best Machine Learning Courses

Machine learning is a process of teaching computers to learn from data, without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make predictions.

There are different types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning is where the machine is given labeled data (examples with a known outcome) and told to learn from these examples in order to produce accurate predictions when given new unlabeled data. This is the most common type of machine learning and can be used for tasks such as regression (predicting a continuous value) or classification (determining which category something belongs to).

Unsupervised learning is where the machine is given unlabeled data and must learn from this data by itself to find patterns or relationships. This can be used for tasks such as clustering (grouping data) or dimensionality reduction (reducing the number of features in a dataset).

Reinforcement learning is where the machine interacts with its environment and learns from the rewards or punishments it receives. This can be used for tasks such as playing a game or controlling a robotic arm.

Machine learning is becoming increasingly important as we move towards a future where more and more decisions are made by computers. Machine learning algorithms are already being used in many different areas, such as finance (algorithmic trading), medicine (diagnosis and treatment recommendations), fraud detection (credit card fraud, insurance fraud), general business decision-making (targeted advertising, customer segmentation), and much more.

There are many different applications for machine learning, and it is one of the most exciting fields in artificial intelligence today. If you are interested in data science, then learning machine learning is a great way to make your skills more valuable.

What are the 11 best machine learning courses to consider in 2022?

Now, we will be discussing the eleven best machine learning courses that we recommend for data science enthusiasts. These courses are excellent for beginners and experienced data scientists alike.

Machine Learning A-Z™ by Kirill Eremenko and Hadelin de Ponteves on Udemy

Kirill Eremenko comes from the Data Science consulting space and has experience in different fields including finance, retail, transport, and others. On Udemy he has over 565k+ reviews across his different courses and more than 2M students have enrolled in his courses. Hadelin is an online entrepreneur and has created 30+ top-rated educational e-courses in AI, ML, Blockchain, and Crypto.

Kirill and Hadelin created this course to show you everything you need to know about Machine Learning, from the very basics (like what is it?) to more advanced concepts (like Neural Networks and Reinforcement Learning). This course is perfect for both absolute beginners with no prior experience in coding or data science, as well as experienced coders and data scientists who want to add Machine Learning to their skill set.

The course is also structured in a way that is easy to follow, with each lecture being followed by a practical exercise so that you can immediately apply what you have learned.

By the end of the course, you'll have a strong understanding of various machine-learning models and how to choose the right one for each type of problem.

In addition, the course comes with a lot of bonus materials, including suggested readings and a list of resources for further learning. Overall, this is the most popular machine learning course on Udemy that will provide you with a solid basis in machine learning while also providing excellent value for your financial and time investment.

• Instructor: Kirill Eremenko and Hadelin de Ponteves
• Level: Beginner
• Duration: 44 hours
• Prerequisites: Just some high school mathematics level
• Type: Paid
• Rating: 4.5 (158k+ ratings; 867k+ students enrolled)

Machine Learning, Data Science and Deep Learning with Python by Frank Kane on Udemy

Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. He spent nine years at Amazon and IMDb before starting his own company, Sundog Software, in 2012.

In this course, he teaches you everything you need to know about machine learning, data science, and deep learning with Python. You'll use Python to classify data using K-means clustering, Support Vector Machines (SVM), KNN, Decision Trees, and other algorithms. You'll learn how to classify data using different methods, make predictions using linear regression, and build artificial neural networks with TensorFlow and Keras. By the end of the course, you'll be able to learn how to use Matplotlib and Seaborn for data visualization and implement machine learning with Apache Spark's MLLib.

This course is perfect for anyone who wants to get a strong foundation in machine learning and a basic understanding of advanced concepts such as deep learning. The explanations are clear and concise, and the examples make complex topics easier to understand. The course is also very practically oriented, with plenty of coding examples to illustrate the concepts being taught.

• Instructor: Frank Kane and Sundog Education Team
• Level: Beginner
• Duration: 15.5 hours
• Prerequisites: high school-level math skills and some prior coding experience
• Type: Paid
• Rating: 4.6 (27k+ ratings, 163k+ students enrolled)

Machine Learning Specialization by DeepLearning.ai and Stanford Online

In June 2022, Andrew Ng launched an updated Machine Learning Specialization course that teaches learners the fundamentals of machine learning and how to use these techniques to build AI applications. In this course, the graded assignments and lectures have been rebuilt to teach in Python instead of Octave/MATLAB. The original course i.e., Machine Learning on Coursera by Andrew NG launched in 2012, which has had over four million learners enrolled.

This course is one of the best courses for first-time learners who want to learn machine learning. The complex topics are explained in simple and understandable language. The course content is well-structured and easy to follow.

In the first specialization course, you will build ML models in Python using sci-kit-learn, a popular machine-learning library. You will learn about supervised and unsupervised learning algorithms and how to apply them to practical problems. In the second course, you will learn to build and train a neural network with TensorFlow, a popular open-source machine-learning library. You will also learn about decision trees and tree ensemble methods, and how to use them to build predictive models. In the third course, you will learn unsupervised learning techniques including clustering and anomaly detection. You will also create a recommender system and a deep reinforcement learning model.

• Instructor: Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig
• Level: Beginner
• Duration: Approximately 2 months to complete
• Prerequisites: high school-level math skills and some prior coding experience
• Type: Free to audit, but if you want to access graded assignments or earn a Course Certificate, you will need to pay
• Rating: 4.9 (150+ ratings, 8k+ students enrolled)

Machine Learning Crash Course with TensorFlow APIs by Google AI

If you are a data science enthusiast, ML Crash Courses by Google AI is the perfect platform for you. The course offers recorded sessions by Google ML experts that can be accessed at your convenience. In addition to this, there are quiz-based and coding-based assignments to help you learn better. The platform is regularly updated with new content so that you can keep up with the latest developments in machine learning.

The course covers the basics to the intermediate application level of machine learning with Python. It also provides introductory knowledge of Tensor flow. The best part is that it is a completely free platform that combines articles, videos, and interactive content. With over 30+ exercises, 25 lessons, and 15 hours of lectures, you are sure to get insights into the world of machine learning from Google researchers. You can also learn about real-world case studies and how to apply machine learning in practical scenarios.

So, what are you waiting for? Start your journey towards becoming a machine learning expert today with ML Crash Courses by Google AI!

• Instructor: Google experts
• Level: Intermediate
• Duration: 15 hours
• Prerequisites: This does not require any prior machine learning skills, but to understand the concepts presented and complete the exercises, students must be comfortable with variables, linear algebra basics, graphs of functions, histograms, and statistical means. Some experience programming in Python is also expected.
• Type: Free
• Rating: NA

2022 Python for Machine Learning & Data Science Masterclass by Jose Portilla on Udemy

Jose Portilla currently works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and Python programming training courses to employees working at top companies. He has 49 courses on Udemy with 889k+ ratings and over 2 million students have enrolled in his various programs. In this course, Jose deep dives into Pandas for data analysis before teaching machine learning with SciKit Learn.

You'll learn supervised machine learning algorithms to predict classes, and also create regression machine learning algorithms to predict continuous values. There are also coding exercises for each machine learning algorithm taught in the course.

I would highly recommend this course to beginners in data science and machine learning using Python. It provides exposure to several machine-learning models and algorithms and is best for Python developers interested in Machine Learning and Data Science. If you want to understand machine learning using Python, then this is an excellent machine learning course.

There's another similar course i.e., Python for Data Science and Machine Learning Bootcamp by the instructor which isn't as updated as this one, but it's still a great course.

• Instructor: Jose Portilla
• Level: Beginner
• Duration: 44 hours
• Prerequisites: Basic Python knowledge
• Type: Paid
• Rating: 4.7 (7k+ ratings, 60k students enrolled)

Machine Learning for All by the University of London on Coursera

Prof. Gillies is a renowned researcher in the field of machine learning and virtual reality. He has been instrumental in developing online degrees such as the Machine Learning for All course offered on Coursera. In addition to his teaching commitments, Prof. Gillies is also involved in researching cutting-edge technologies such as virtual reality and machine learning.

The course is designed for data science enthusiasts with little or no prior experience in machine learning. You'll learn the fundamental machine learning concepts, how data affects the results of models, and how to test a machine learning project to get the results you want. The course also covers ethics in machine learning.

Overall, the course is a great introduction to machine learning for those with no prior experience. Though the course doesn't cover hands-on training in Python, it gives both a high-level and detailed overview of the concept. If you're looking to start your machine learning journey, this is one of the best courses out there. It's a great introduction to the subject matter and will give you a strong foundation to build upon.

• Instructor: Prof Marco Gillies
• Level: Beginner
• Duration: 22 hours
• Prerequisites: None
• Type: Free to audit, but if you want to access graded assignments or earn a Course Certificate, you will need to pay
• Rating: 4.7 (2K+ ratings, 113k+ students enrolled)

Stanford CS229 Machine Learning Course (Autumn 2018) by Andrew NG on YouTube

Andrew Ng is a computer scientist who co-founded Google Brain, led AI research at Baidu, and has impacted millions of AI learners. He is known for his work on deep learning and machine learning, which has helped to make these fields more accessible to the public. In addition to his online courses through Coursera, Andrew has also released a series of lectures on YouTube, which cover a variety of topics related to machine learning. These lectures are a great resource for anyone looking to learn more about this field.

The CS229 Machine Learning Course by Stanford University is math-heavy, but it covers a larger set of topics that will give you a strong foundation in data science. The lecture notes and cheat sheets are great resources to have on hand while watching the lectures.

In this course, you'll learn about linear regression, support vector machines, decision trees, ensemble methods, neural networks, and reinforcement learning. These are all important topics that will give you a well-rounded understanding of machine learning. The lectures are engaging and informative, and Andrew NG does a great job of explaining the concepts in detail.

If you're serious about learning machine learning, then the Stanford CS229 Machine Learning Course is a great resource to check out. With its comprehensive coverage of important topics and engaging lectures, you'll be sure to come away with a strong understanding of the subject matter. So, what are you waiting for? Go check it out!

• Instructor: Andrew Ng
• Level: Intermediate
• Duration: 26 hours
• Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy, familiarity with probability theory or familiarity with multivariable calculus and linear algebra
• Type: Free
• Rating: NA (has 1M+ views on YouTube)

Cornell CS 5787 Applied Machine Learning (Fall 2020) by Volodymyr Kuleshov on YouTube

Volodymyr Kuleshov is an Assistant Professor at Cornell Tech's Jacobs Technion-Cornell Institute and in the Computer Science Department at Cornell University. On YouTube, he offers a series of lectures on applied machine learning. In his class, he covers a range of topics including linear regression, classification, Naive Bayes, SVMs, decision trees, clustering, and dimensionality reduction. You'll also learn about feature selection and model improvements. The course materials are available online, so you can work through them at your own pace.

I found this course to understand the fundamental machine learning concepts. Volodymyr does a great job of explaining things clearly and providing examples to illustrate key advanced machine-learning techniques. I would recommend this course to anyone who wants to learn more about machine learning.

• Instructor: Volodymyr Kuleshov
• Level: Intermediate
• Duration: 24 hours (content is spread across 80 videos)
• Prerequisites: Students should be comfortable with multivariable calculus, primarily integration, and differentiation in multiple dimensions. The course will use Python and related data science libraries, including NumPy, SciPy, Scikit-Learn, and TensorFlow, or PyTorch.
• Type: Free
• Rating: NA (61k+ views on YouTube)

Learning From Data by Caltech's Professor Yaser Abu-Mostafa on YouTube

If you're a data scientist or aspiring to be one, then you need to check out this video by Professor Yaser Abu-Mostafa from Caltech. In it, he covers the machine learning fundamentals, algorithms, and applications of machine learning in an introductory course that is perfect for beginners. He explains key concepts such as linear models, the difference in training and testing, overfitting, regularization, and SVMs in a way that is easy to understand. I highly recommend this video to anyone looking to learn more about machine learning.

• Instructor: Yaser Abu-Mostafa
• Level: Intermediate
• Duration: 18 lectures about 60 minutes each plus Q&As
• Prerequisites: Understanding of basic probability, matrices, and calculus is expected
• Type: Free
• Rating: NA (985k+ views on YouTube)

Introduction to Machine Learning Course on Udacity

If you're a data science enthusiast, the Introduction to Machine Learning Course on Udacity is a great way to start learning about machine learning algorithms. The course discusses the most important algorithms (Linear Regressions, SVMs, Naive Bayes, Decision Trees, etc.), and teaches how to evaluate the performance of ML algorithms.

The course is well-structured and easy to follow. The lectures are concise and informative, and the exercises are challenging but not too difficult. Overall, I highly recommend this course to anyone interested in learning more about machine learning.

Udacity offers a lot of great courses, but this one is one of the best. If you're interested in learning more about machine learning, I highly recommend this course. It's a great introduction to the subject, and it's taught by experienced instructors. You'll learn a lot, and you'll have a lot of fun too. Thanks for reading, and I hope you enjoy the course!

• Instructor: Sebastian Thrun and Katie Malone
• Level: Intermediate
• Duration: Approx. 10 weeks
• Prerequisites: Python programming, inferential statistics, and descriptive statistics
• Type: Free
• Rating: NA

Data Science: Machine Learning by Harvard University on edX

If you're passionate about data science or aspire to be, then Harvard's Data Science: Machine Learning course on edX is a must-take. This course is taught by Rafael Irizarry who is a Professor of Applied Statistics at Harvard and the Dana-Farber Cancer Institute, as well as a Biostatistics Professor at Harvard T.H. Chan School of Public Health.

In this intensive course, you'll learn the basics of machine learning and some of the most popular algorithms used in the field. You'll also learn Principal component analysis and regularization techniques, as well as how to build a recommendation system.

Cross-validation is a key concept that is covered in the course, so you'll also spend some time on that. All in all, it's a comprehensive course that will leave you with a solid foundation in machine learning principles. This course is part of the Professional Certificate in Data Science and is taught by instructors from Harvard's renowned Data Science Initiative. So, if you're looking to take your data science skills to the next level, this is the course for you.

• Instructor: Rafael Irizarry
• Level: Beginner
• Duration: Estimated 8 weeks (2-4 hours per week)
• Prerequisites: Concepts of probability, statistical inference, data modeling, data wrangling, and linear regression. Additionally, to complete the assignments, experience in R programming language is necessary.
• Type: Free (Audit track) and $99 as part of the Verified track
• Rating: NA (380k+ students enrolled)

Tips for getting the most out of machine- learning course

Before diving into any machine learning course, it is important to have the right mindset and understand what you want to get out of the experience. Here are a few tips to help you get the most out of your machine learning course:

• Understand the basics of machine learning. Before you start taking a machine learning course, it is important to have a basic understanding of the concepts. This will help you get the most out of the course and avoid getting lost.
• Learn how to use Python. Python is a programming language that is commonly used in machine learning. If you don’t know how to use Python, you will likely struggle with some of the concepts. Make sure that you take the time to learn this important skill before taking a machine learning course.
• Set realistic expectations. A lot of people start taking machine learning courses with inflated expectations and end up being disappointed. It is important to be realistic about what you can expect to learn and how long it will take you to learn it. If you set unrealistic expectations, you are more likely to get frustrated and give up.
• Choose the right course. There are a lot of machine learning courses out there. Make sure that you choose one that is right for your level of experience and your goals.
• Have a clear goal in mind. When you start taking a machine learning course, make sure that you have a clear idea of what your goals are. What do you want to learn? What are you hoping to achieve? Having a clear goal will help you stay focused and motivated.
• Keep practicing! The best way to learn machine learning is to practice, practice, practice. Make sure that you find time to experiment and play around with the concepts that you are learning. The more you practice, the better you will become at using machine learning.
• Participate in data science competitions. Many data science competitions offer a real-world challenge that can help data scientists get the most out of any machine learning course. These competitions allow you to apply the concepts that you are learning in a practical setting and see how they work in practice. They also provide an opportunity to network with other data scientists and learn from their experiences.
• Use online resources to learn more about the topic that's taught in the class. There are plenty of free resources on the internet that can help teach and guide you along the way.
• Don’t be afraid to ask for help. If you are having trouble understanding something, don’t be afraid to reach out to your instructor, classmates, or communities for help.

Conclusion

I hope that this article has helped you in your search for the perfect course to help you learn machine learning and guide you through the process of choosing the best one for you. Thanks for reading and I hope you find the perfect course for you!

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