How to learn machine learning? What is its learning path? Those interested in learning artificial intelligence and machine learning must go to university or are there other ways? If yes, what ways?

Before answering those questions, other important questions must be answered: First, why should anyone learn machine learning at all? And second, can anyone learn machine learning? Doesn’t machine learning, which is a subset of artificial intelligence, require strong math? Isn’t it very difficult and time-consuming?

Everyone has their own reasons for entering the field of artificial intelligence. But it is possible to find reasons that are common among those who want to learn machine learning. The most important common reason is that artificial intelligence will revolutionize the future of the labor market and the expertise that employers, businesses and industries need. The career and income prospects of machine learning are very bright and promising.

Another common reason is the developments and inventions that can be created with machine learning in different industries and fields. It is no exaggeration to say that AI and machine learning experts are finding new uses for AI every day. The applications of machine learning are not only in the fields of cyber security, space exploration, medicine, banking and self-driving cars. Machine learning can also be used to protect the environment and endangered species.

The last common reason is interest. Some are interested in becoming astronauts or studying astronomy and discovering new planets and stars. Some people are interested in becoming physicists and discovering the unknown laws governing this world. Some people are also interested in teaching things to inanimate beings, machines and software.

It doesn’t matter why you want to learn machine learning and you need a path or paths to learn machine learning, this article will introduce you to 3 paths.

Machine learning path

Next, we will examine 3 ways to learn machine learning. But before that, I have to answer the second question: Yes, learning machine learning can be difficult and time-consuming. Of course, learning anything can be difficult and time-consuming. But it is not true to say that only those who studied mathematics or know mathematics can learn artificial intelligence or machine learning. Mastery of math or a high school subject or college education may just make learning time shorter for some or longer for others. In the end, it all comes down to your interest and motivation, and you are the one who determines whether learning machine learning is difficult or easy for you.

First Path: University Education for Machine Learning

This path takes a lot of time and money and is suitable for those who are at the beginning of their academic and working life. If you have a clear picture of your future and want to completely dedicate your educational and career path to machine learning, this path is the most logical and best for you.

Those who want to immigrate to continue their education in the field of machine learning; There are more, more specialized and of course more expensive choices. In the prestigious universities of the world, there is a field of study in machine learning at the master’s and doctorate levels, in some universities even at the undergraduate level.

Fortunately, many universities, such as Stanford, offer a master’s degree in machine learning completely online. Another university that is very prestigious for learning machine learning is Carnegie Mellon University in America. If you want to apply for a master’s degree in machine learning at this university, your bachelor’s degree does not have to be computer, but you need a TOEFL score (above 110) and GRE, and they will take a test from you to see how familiar you are with the basics of computer science. This course is not offered online. The tuition fee for one year is 52 thousand dollars.

The second path: courses of educational institutions (online and face-to-face)

Not everyone who wants to learn machine learning is a student. Maybe someone is an employee or a web designer or studied electrical engineering and has now decided to learn machine learning. Precisely for this reason, reputable universities and educational centers around the world hold machine learning training courses. Those courses are flexible and ideal for those with full-time or part-time jobs.

Non-university courses (courses of educational institutions) have other advantages:

  • They may be completely online or hybrid (a combination of face-to-face and non-face-to-face). So, if you are one of those who must interact directly with the professor to learn, you can participate in hybrid courses.
  • At the end of the course, a valid certificate will be given to those who have successfully completed the course.
  • Courses in educational centers are cheaper and completed in a shorter period of time compared to studying at a university.

2 examples of fully online and authentic machine learning courses

The number and variety of online courses are large. Because there is a great demand for those courses. Considering the wide applications of machine learning in the world, as well as its current and future good job market, non-university courses that give a valid certificate in a short time (for example, 6 months) that can be recruited with the same certificate are very popular and popular.

The American Berkeley University machine learning course is completely online, 6 months and its cost is 7500 dollars. Google also has an excellent training program for machine learning, the introductory level of which is free, but to get a degree and use the full course, you have to pay a fee. Of course, I must also say that participating in the Google course has prerequisites that are clearly and precisely announced.

The third path: self-study

Yes, fortunately, a college degree is no longer important to employers today. It is important that, for example, the front-end developer is well versed in CSS and JavaScript. This also applies to machine learning to some extent. Someone may have studied IT at university. But he learned machine learning by self-learning and proved his mastery to the employer and was hired as a result.

In the websites, there are many articles with the same title, the path of learning machine learning or the road map of learning machine learning. In most of those materials, a long list of mathematical topics (differential calculus, linear algebra, discrete mathematics and probability theory and statistics) are lined up. In addition to all that math, there are other topics that an interested person must master in order to start learning machine learning:

  • Python programming,
  • Getting to know the principles of software development, and
  • Getting to know the database and the principles and methods of data extraction and processing.

Now the important question is, do you really need to learn so much math? If yes, can anyone learn machine learning by self-learning? Yes, Python programming is necessary for machine learning, but not all that math. Some mathematical topics are the basis of machine learning and only those topics should be mastered. The answer to the second question depends on the student’s academic level and time. Maybe someone who has studied or worked in related fields can learn machine learning more easily and in less time.

Suggested path and resources for self-learning machine learning

If you don’t have any background in math or Python and basically artificial intelligence, you should spend more time on self-learning and maybe get help from online resources (like tutorial videos). The path I suggest is for someone who is completely new to the world of artificial intelligence and machine learning. Start with the following books:

  1. Artificial Intelligence for Dummies
  2. Introduction to Machine Learning with Python
  3. Mathematics for Machine Learning

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