Essential Skills to Become a Machine Learning Expert.
You need a lot of skills to become a machine learning expert, from computer science to math, statistics, and probability. This field has its own challenges, and if you enter it without interest, you will probably get bored in the middle of the way. Although it is impossible to put together everything you need to know to become a machine learning expert, showing the way helps real enthusiasts explore new and relevant things themselves.
Machine learning theories and their algorithms
At this stage, try to increase your general knowledge about machine learning and its theories. Here are some questions you should find the answers to. Think and search about these concepts long enough to make sure you have understood them well.
- What is data science?
- What does Big Data mean?
- What is artificial intelligence?
- What is a learning machine?
- What is Deep Learning?
- What do the above concepts have in common, or how do they differ?
- What is the use of these complicated terms outside in the real world?
As a machine learning expert, you need to know the famous algorithms in this field and the goals they pursue. The next step is to understand how these algorithms work with data. As we mentioned in the previous article, machine learning algorithms are divided into three general categories of supervised, unsupervised, and reinforcement.
Here are some of the most popular algorithms:
- Linear regression
- Logistic regression
- Decision tree
- Random forest
Good understanding of computer science to become a machine learning expert
A machine learning specialist must have a great deal of knowledge in computer science. If you have studied computer engineering or similar at university, you are a few steps ahead of other people who want to learn machine learning.
In general, the concepts of computer science in machine learning include the following:
- Proper understanding of data structure: stack, queue, arrays, tree, forest, etc.
- Proper understanding of algorithm design: search, sorting, optimization, complexity calculation, etc.
- A good understanding of computer architecture: memory, deadlock concept, asymmetric processing, and so on
Learning the above concepts is not enough. As a machine learning expert, you need to implement these concepts and use them properly when programming.
Understanding of statistics and probability
The good news for those who are not happy with math and statistics is that you do not have to be a genius in statistics and probability. It is enough to learn the basic concepts needed. Here are some of these concepts:
- probability distributions
- Distribution of random variables
- Linear, multiple and logistic regression
There are many resources for studying general statistics topics such as books, online courses, and more. In the article How to Become a Science Specialist, we have introduced several statistics books.
Learning Python or R (or both)
As a machine learning expert, you need to learn a programming language. Due to the simplicity and powerful Python libraries, this language is the right choice for people working in machine learning. However, the R language also has many capabilities and can help you solve machine learning problems.
Knowing Big Data
Although working with Big Data is a separate specialty, as a machine learning engineer, you should be familiar with the principles of Big Data as you may be dealing with large amounts of data during your work.
Understanding how big data is stored, retrieved, or processed can provide a perfect solution to various machine learning problems.
I recommend using a Linux distribution to practice and learn Big Data as Linux is well compatible with Apache Hadoop. This open-source toolkit solves big data problems using multiple computers.
Here are some concepts that will help you learn machine learning:
Studying deep learning models
Machine learning models are among the most advanced topics in this field. These models have helped Apple and Microsoft build Siri and Cortana voice assistants or help large companies make driverless cars. After learning the previous topics, you have to enter a serious phase, working with machine learning models.
As a machine learning expert, first, you can design a model that distinguishes a flower’s image from the picture of a fruit. Although this model may not immediately close you to building a driverless car, it is an excellent place to start and gives you a good view of the route you need to take. Some of the topics you should learn in this course are:
- Artificial Neural Networks
- Natural language processing
- Convolutional neural networks
- TensorFlow or TensorFlow
- Open Computer Vision Library