Best Resources to Study Machine Learning Online

Best Resources to Study Machine Learning Online

Machine learning (ML) has become popular in the last 15 years. Today, a lot of people use apps based on it. ML is a professional teaching method for computers. It doesn’t require programming. This is somewhat similar to how infants learn to classify things.These technologies have already been used in many areas and contributed to the emergence of virtual assistants that allow us, for example, to traffic forecasts in Google Maps. Sounds nice, right? That is why many want to learn machine learning. Where can they do it? In this post, you will find the Best Resources to Study Machine Learning Online.

Online Machine Learning Courses for Everyone

Machine learning opens up new opportunities for computers to solve difficult tasks. As an indispensable assistant, it stimulates the expansion of the possibilities of artificial intelligence and has even become synonymous. Many developers say that machine learning is one of the most popular applications of AI in modern enterprises. Even companies that have not yet used ML will soon appreciate its potential.

Undoubtedly, AI will become a mainstay of the IT strategies of many enterprises. Artificial intelligence has already played an important role in changing the development of the IT industry. Customers value smart applications to conduct their business with AI. It applies to the traditional business environment and all software-implemented work processes: research, production processes, and, more recently, the product itself.

So, where can you learn machine learning and introduce new technologies into this world?

1.Machine Learning for All (University of London)

Many people often recommend this online course on machine learning. While many other studies require programming skills, this one strives to make ML available to more people. Such advanced courses help students master machine learning from simple models to neural networks. You don’t need to use programming languages such as Python or TensorFlow and machine learning libraries or have deep mathematical knowledge.

After you complete the course, you will have a comprehensive knowledge of machine learning and various applications of it. You will familiarize yourself with the most significant technological ideas of ML. In addition, you will comprehensively understand the machine learning model-building process, from data collection to model evaluation. It will also qualify you for more advanced courses in ML.

2. Machine Learning A-Z: Hands-On Python & R in Data Science (Udemy)

Choosing this course, you will go further slowly, from data preprocessing to model checking, omitting some underlying mathematics. It may suit those who want to start “doing things” right now.

You will take up reinforcement learning and natural language processing and consider the basics of artificial neural networks.

Many consider it one of the best machine learning courses on Udemy. It consists of video tutorials and exercises. Having developed an image of each concept or method, you will apply it to specific tasks using a special machine learning library. During the course, you will master the basic skills, understand the principles of work, and collect a portfolio of projects.

3. Machine Learning (Stanford University)

This resource is considered by many to be the best machine learning course. Andrew Ng teaches students. He is a professor at Stanford University and co-founder of many online platforms. This course covers all the basics you should know. It is an educational material suitable for beginners that can learn the basics of linear algebra and calculus in a curated format.

After learning all the information in this course, you will have a basic knowledge of the concepts and methods of machine learning. In addition, you will be able to implement basic machine learning algorithms.

4. Machine Learning (Georgia Tech)

Experts recommend this course to anyone who wants a holistic approach to programming spheres and an interactive environment. It has a very comprehensive curriculum. A lot is covered in this course. You will not learn about deep neural networks. Yet, it will give you a clear understanding of all types of machine learning algorithms, their advantages and disadvantages, and how you can integrate them into the development of real applications. You will find something nice while learning this course.

5. Machine Learning (Columbia University)

This course requires a comprehensive mathematical education (linear algebra and analysis) and programming skills (Python or Octave). If you are a beginner in programming, don’t take this course. It is perfect for more advanced students who want to develop their mathematical understanding of algorithms further.

Two specialists conduct the study in a conversational style. One of them plays the role of a student. He asks questions and a teacher answers them. It’s fun. They provide students with fundamental knowledge in the learning process for development to the middle level. Developers of machine learning courses usually don’t use such an approach.

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

This course is provided on Google’s development platform. It quickly became popular after its release in 2018. The free 15-hour course consists of 25 lessons, 40 exercises, video lectures by Google researchers, and other interactive elements. The content is practical and flexible. So, even beginners can complete the entire course, while those with experience in machine learning can use it as a repetition.

Also, this course introduces neural networks. It is a topic often overlooked or not covered, as it deserves a separate course. But the course is informative to make neural networks more user-friendly.


Machine learning is simple, but it requires a certain skill. First of all, the key points of machine learning are the knowledge of English (you should read literature and communicate with other experts) and mathematics with an emphasis on algorithms. Knowledge of databases and programming languages is also required. Any language is good, but Python is preferred.

Stop thinking in a stereotyped way, as you were taught in classic programming courses. The very purpose of machine learning is to allow programs to set their conditions and learn to search for patterns. If you decide to study with leading experts, then the list of machine learning courses from this article is what you need.

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