Machine learning is at its peak, but newcomers to the field face a large flow of information that is difficult to process. We will provide you with some tips so that you would not get in the amount of ​​information. You will learn what skills are necessary to become a machine-learning software developer. And if you are looking for the best courses to study machine learning, check out Serokell’s blog.

What is ML?

Machine Learning (ML) is a science and applied technology that software experts use to create intelligent programs. ML solutions are very convenient. They allow us to automate and optimize processes delegating part of the decision-making process and pattern recognition to the machines. Computers are much better than humans at making calculations and processing large amounts of unstructured data. Many believe that advances in machine learning can revolutionize our lives.

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Skills you need to become an expert in ML

For a quick dive into ML, you need to have an excellent mathematical background and programming skills. 

  • First, you should be well versed in the basics of mathematics (functions, derivatives, vectors, matrices), statistics, and algebra. If there is not enough knowledge, you can remain a specialist performing the tasks of a coder. You will be able to work with frameworks and open-source libraries even without a profound knowledge of math. However, it will be hard for you to build your own models or fine-tune them.
  • Secondly, you need a good knowledge of one of the popular programming languages. Usually, machine learning experts and data scientists prefer R, Python, or C ++. These languages have rich libraries with many resources for ML. If you do not know any programming language, start with Python, since it is the simplest one.

Best resources to study machine learning

All of the listed sources are already structured and allow to start using machine learning in practice without experience and any primary knowledge. They will guide you from basic concepts to advanced algorithms and methodologies, as well as lay the necessary potential for the formation of professional skills. The courses are available on Coursera.

  1. Machine Learning Foundations: A Case Study Approach. Studying real-life applications of ML, you will get the first-hand experience of this technology. You will learn how to predict housing prices, use sentiment analyses to assess public opinions, build recommender systems, and more. 
  2. Practical Machine Learning. This course will teach you to apply machine learning techniques for pattern recognition and predictions. The methods that you will learn include regression, classification trees, Naive Bayes, and random forests.
  3. Machine Learning. This beginner-level course from Stanford is recognized internationally as one of the best. You will learn the best practices and study how to apply supervised and unsupervised learning to real-life tasks.
  4. Kaggle. This a must-have resource for anybody who is interested in ML, especially if you are learning it by yourself. You will find plenty of tasks for practice and a community to ask questions to.

Final thoughts

The career in the area of machine learning is interesting and inspiring. If you have a good math background and love to invent innovative approaches to a problem, ML is the right field for you. Even if you aren’t planning to become the next Elon Musk, learning about how the ML-powered things around you really work is also extremely fascinating.