Today, Machine Learning courses and skills are in great demand. According to Microsoft’s career page, 21% of the open developer positions presently mention “Machine Learning.” On Amazon’s career page, the percentage has shot up to 63%. Recently, the World Economic Forum published a report titled “The Future of Jobs” that expects Machine Learning to become one of the world’s most in-demand skills through 2025.
All these show the importance and the future of Machine Learning and related courses. If you are new to this, Machine learning is a rapidly growing field with many applications in business, science, and medicine.
Mainly, it is an important subfield of artificial intelligence dedicated to designing algorithms that can learn from data.
With time, Machine Learning tools have gained much importance in nearly every industry. As a result, they have numerous applications – from business analytics and financial forecasting to health informatics and even self-driving cars.
Since many companies in different sectors have embraced Machine Learning, there is now a wide range of career opportunities across the industry. If you have a background in Machine Learning, you can apply for various jobs such as:
- Business intelligence developer
- Data scientist
- Natural Language Processing (NLP) scientist
- Machine learning engineer
- Human-centered Machine Learning designer
If you are interested in building a career in this niche, you can take a practical Machine Learning course to acquire deep knowledge of the field and related concepts. In addition, students who enroll in these courses gain the information and skills needed to deal with real-life challenges. So, whether you want to get a glimpse of Machine Learning or build a career in the field, you will gain the required exposure through these courses.
New and updated Machine Learning training courses are constantly emerging to help budding professionals gain the expertise they need to become Machine Learning experts. In this article, I have listed the ten best Machine Learning courses and programs to upskill yourself and secure a rewarding Machine Learning job in 2023.
Best Machine Learning Courses for 2023
Let us now review my picks for the top ten Machine Learning courses for you to join today.
Machine Learning Course by Coursera
Coursera’s Machine Learning course has raised the bar and set a new standard for all other such courses to be judged by. It is infact the best machine learning online course out there.
It is a beginner-friendly machine learning course by Andrew Ng, a Stanford professor and co-founder of Google Brain and Coursera.
Instead of sticking to Python or R for the assignments, it uses the open-source programming language Octave. Although that might turn some people off, Octave is a simple yet effective way for complete beginners to learn the fundamentals of Machine Learning.
Overall, the course material is amazingly well-rounded and meticulously articulated. Ng comprehensively explains the necessary mathematics to help you properly understand each algorithm. Although it is a largely self-contained course, it would help if you had some knowledge of linear algebra beforehand.
By completing the entire course, you will have a solid knowledge of Machine Learning in about eleven weeks. After that, you can easily take up a more advanced or specialized topic, such as deep learning or Machine Learning engineering.
Machine Learning Crash Course by Google AI
This is an excellent ML course from Google AI Education – a completely free platform offering high-quality educational articles, videos, and interactive content. It covers all the crucial topics you need to solve Machine Learning problems as soon as possible. In this case, Python is the programming language of choice, and you will also be introduced to TensorFlow. In addition, each main section of the curriculum features an interactive Jupyter notebook hosted on Google Colab.
The course provides succinct and straightforward articles and video lectures, allowing you to move through the course at your own pace quickly. As a result, it is an excellent option for users who are already familiar with Machine Learning but are hoping to cover all their bases. In addition, it discusses several nuances of Machine Learning that may take countless hours to learn serendipitously.
However, I must mention that the course apparently does not offer a certificate of completion at present. So if that's something you need, you may want to seek another option.
Machine Learning with Python by Coursera
Machine Learning with Python by Coursera is another beginner course from Coursera that deals exclusively with the most fundamental Machine Learning algorithms. The instructor, slide animations, and explanation of the algorithms work smoothly together to help the student get well-acquainted with the basics.
Like the previous course from Google AI, this one uses Python and is relatively light on the mathematics behind the algorithms. Each module allows you to spool up an interactive Jupyter notebook in your browser to review the new concepts you just learned. These notebooks reinforce your knowledge and give reliable instructions for using an algorithm on real data.
I really appreciate the practical advice this course offers for each algorithm. Whenever you are introduced to a new algorithm, the instructor will explain how it works, its pros and cons, and what types of situations you should use it in. Most other courses tend to exclude these important points that help new learners understand the broader context.
Introduction to Machine Learning for Coders by Fast.ai
The founders of Fast.ai have come up with this high-quality, free Machine Learning course for students who already have experience in Python programming for about a year. The amount of time and effort they have put into the course is truly astonishing. As the content is based on the University of San Diego's Data Science program, the lectures are held in a classroom with students in the MIT OpenCourseWare style.
The course has several videos, comprehensive notes, a discussion board, and some homework assignments. However, it does not provide graded assignments and certification or quizzes upon completion. Therefore, Coursera or EdX would be better options for students who would like to have those features. In addition, since most of the course content is applied, you will learn how to use the Machine Learning models and launch them on cloud providers such as AWS.
I recommend this course to programmers who want to learn and apply Machine Learning techniques.
Machine Learning by EdX
This is an advanced course with a very high math prerequisite compared to other courses on this list. Before enrolling, you must have a solid understanding of calculus, linear algebra, probability, and programming. Although the course has interesting programming assignments in either Python or Octave, it does not teach either language.
One of the unique features of this course is its coverage of the probabilistic approach to Machine Learning. If you are planning to read a textbook such as Machine Learning: A Probabilistic Perspective (one of the most popular data science books in Master’s programs), then this course would be an excellent complement to the same.
Although other courses aimed at beginners cover most of the topics included here, EdX takes care not to water down the mathematics. If you are interested in delving deeper into the mathematics behind Machine Learning or wish to work on programming assignments that derive some of the algorithms, then you should try this course.
Machine Learning by Georgia Tech
The Georgia Institute of Technology offers this course on Udacity and is also available as a part of Georgia Tech’s Online Master of Computer Science (OMSCS). It broadly covers Machine Learning and emphasizes breadth over depth. Instead of delving into the redundant details of implementing specific Machine Learning algorithms, the course opts for a high-level approach to Machine Learning concepts.
The main strength of the course lies in its instructional approach. It is taught by two instructors who present the lessons as a conversation between them, with one playing the student’s role and asking questions. Their exchange is humorous and entertaining, something that is missing from many other Machine Learning courses.
The course is divided into three broad Machine Learning tasks. It first covers supervised learning, followed by unsupervised learning and reinforcement learning. You will also learn methods tailored to each of these problems, implement methods to solve them, interpret the results, and evaluate their correctness. The course has 21 lessons, each comprising short videos with in-video quizzes.
Machine Learning A-Z by Udemy
True to its name, this course is a detailed but practical introduction to Machine Learning. It slowly proceeds from data pre-processing to model validation, glossing over some of the underlying math. It starts by covering different types of classification, regression, and clustering models. Subsequently, it discusses reinforcement learning, natural language processing, and the basics of artificial neural networks.
The course uses the Python and R programming languages and the TensorFlow Machine Learning library. It includes more than 40 hours of video lessons, interspersed with practical exercises. As the course progresses, you will develop an intuition for each concept and method before applying them to solve problems using dedicated Machine Learning libraries.
With more than 800k registered learners, Machine Learning A-Z is one of the most popular and successful Machine Learning courses on Udemy. Between them, instructors Kirill Eremenko and Hadelin de Ponteves have created more than 80 courses and have nearly 3.5 million students.
Intro to Machine Learning by Kaggle
Kaggle’s Machine Learning course is an introduction to the basics of the subject and covers everything from using data science methodologies to building your own models. It is part of a series of “micro” courses covering AI, data science, and deep learning concepts. It teaches students how to solve real-world problems with the help of Machine Learning.
If you are planning to start a career in data science or are interested in the capabilities of Machine Learning algorithms, this could be an ideal course for you. Although you don’t need any background Machine Learning to take this course, it is advisable to have a basic understanding of Python. It tries to provide a simple overview of familiar topics, such as how to use different languages for data science and what it means to build a model from scratch.
The course begins with an introduction to the core ideas of Machine Learning and helps you develop an understanding of how models work. It is free to take and rewards you with a certificate of completion when you’re finished.
Introduction to Artificial Intelligence by SimpliLearn
This course provides a comprehensive view of the artificial intelligence (AI) landscape and gives learners a complete insight into the basics of AI concepts and workflows. If you are aiming for a career in AI or want to expand your data science skills, this self-paced and versatile course is a great way to get started in the industry. It covers all aspects of an artificial intelligence and machine learning course. And, it strongly focuses on Machine Learning concepts such as supervised and unsupervised learning.
Although you don’t need any prior knowledge of Machine Learning concepts, you should know about Python programming and statistics. The course covers everything from fundamental AI workflows and concepts to the more complex ideas in deep learning. In addition, it includes a vast collection of videos and resources created by mentors well-versed in AI.
Students can access the course free of charge for up to 90 days, and the training lasts around two hours. It awards you a certificate of completion when you are finished.
Machine Learning for Musicians and Artists by Kadenze
This course is offered by Goldsmith, University of London, through Kadenze. Somewhat unconventionally, it approaches to Machine Learning from an artistic angle – from music to visual arts. If that is the mix you are looking for, this one should be your top priority.
In this course, you will learn the fundamentals of Machine Learning by connecting the topic to art, motion, and sound. You will learn to use Machine Learning to interpret human movement, music, and other real-time data sources. Also, the course involves learning more pedestrian but essential Machine Learning concepts such as classification, regression, and segmentation. It also deals with practical concepts such as configuring an end-to-end Machine Learning pipeline.
The course consists of seven sessions, each involving about eight work hours. You will have free access to the study material (but not the assignments) if you audit the course. If you subscribe, you will have access to the assignments and a certificate of completion.
Machine learning has become a popular concept in the business world and a valuable tool for data scientists and programmers. By choosing the right Machine Learning course, you can develop a solid understanding of all aspects of the topic – from supervised and unsupervised learning to the benefits of cross-validation.
What are the best machine learning courses?
Machine Learning Courses by Coursera, Google AI and EdX are some of the best machine learning courses online.
Is Python enough for machine learning?
Python is one of the mediums for learning machine learning effectively. If you are good at it, you already have the upper hand.
Are 6 months enough for machine learning?
For most parts, yes. But you should always give about 12-18 months for the best results and a much more comprehensive career.