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6 Steps to Become a Machine Learning Expert

Everything you need to know to become a machine learning expert.

  • Programming languages ​​for machine learning
  • Libraries for Python and R
  • Tools you should know for machine learning
  • Disciplines you need to learn
  • Algorithms for machine learning
  • Resources and YouTube channels for machine learning

What is Machine Learning?

As you know, the amount of data produced has increased with the development of the internet and social media. If AI is today’s electricity, data is today’s oil. Companies such as Google, Facebook, and Amazon became huge companies because they evaluated the data they obtained. To use oil, you have to process it right? To extract meaningful information from data, you need to process it.

Machine learning is a subfield of AI that aims at automatically building models using data.

You may ask how machine learning came about. Let me explain this briefly. Previously, rules were entered into the machine, and output was produced for inputs according to these rules.

Machine Learning vs Traditional Approach
(image by author)

Step 1. Programming Language

An important part of machine learning is programming. You need to know a programming language to preprocess data and build a model. The most used programming languages ​​for machine learning are Python and R. Python is preferred more because it is a general programming language with easy syntax.

Step 2. Libraries for Machine Learning

You can build a machine learning model from scratch. But, there is no need to reinvent the wheel. You can build faster and more practical models using libraries such as scikit learn.


In machine learning, you do a lot of matrix and array operations. The library you need to know for multidimensional array operations is NumPy.


Another important library is Pandas. Real world datasets are dirty. To clean these dirty datasets, data preprocessing are required. To do this, you can use Pandas.

Matplotlib and Seaborn

It is important to explore the data before building the model. Data visualization is the easiest way to explore data. Matplotlib and seaborn libraries are mostly used for data visualization.


The main purpose of machine learning is to build a good model. You can use the scikit-learn library to build a model. You can find many supervised and unsupervised learning algorithms in the scikit-learn library.

TensorFlow and Keras

Another important library for making machine learning projects is TensorFlow. With TensorFlow, you can build end-to-end machine learning projects. In other words, with TensorFlow, you can do every step from data preprocessing to deploying the model. Also, Keras API has been added for easier analysis with TensorFlow. Keras is a widely used API, especially for deep learning.

Machine Learning Libraries for R

Although Python libraries are used very often in machine learning, R also has very powerful packages. I can tell you the libraries you need to know for R programming, ggplot2 for data visualization, dplyr, and date.frame for data manipulation, caret and mlr for building machine learning models, and shiny for making interactive web applications.

Step 3. Tools You Need to Know for Machine Learning

There are many tools you can use for machine learning. Let’s take a look at the tools you need to know for machine learning.


With a user base of more than 20 million, Anaconda is a fantastic platform and it includes many libraries and tools. You can use many libraries at the same time without error. I highly recommend you to use Anaconda for machine learning projects. Also, you can work with many different projects to create a virtual environment in Anaconda.

Jupyter Notebook

An editor or IDE allows you to easily write codes. The jupyter notebook is perhaps the most used tool for writing code when analyzing data. Jupyter notebook is web-based and a good tool for visualizing data. Jupyter notebook also has cells where you can write texts.


Another fantastic tool you can use for machine learning is Colab. Colab is a free cloud service provided by Google. Colab comes loaded with many libraries such as TensorFlow. Colab’s interface is similar to the jupyter notebook.

Git and Github

An important tool you can use for teamwork is git. Git is a version control system. Using Git, you can collaborate with your teammates on your files and codes. You can store your project free on GitHub. With GitHub, you can also present your project to the world and share your codes.

Other Tools

You can use Notion tool to manage your projects. Notion is a web-based tool where you can easily keep your notes.

Step 4. Disciplines for Machine Learning

You can do machine learning projects using libraries. But to understand the machine learning steps, you need to know mathematics, probability, statistics, and linear algebra. Don’t be afraid when you see these disciplines. To learn machine learning, it is enough to know these disciplines at a basic level.

Step 5. Algorithms for Machine Learning

Data quality is very important for a machine learning project to be successful. Another important point is to use an algorithm suitable for the data. There are many algorithms you can use for machine learning. We can divide these algorithms into supervised learning and unsupervised learning.

Step 6. Websites for Machine Learning

There are many sites you can use for machine learning. Kaggle comes first among these sites.


Kaggle is one of the world’s largest data science and machine learning platforms. There are more than 1 million registered users on this platform. Some of the world’s top data scientists actively use this site.


Another site I would recommend is Medium. You can find a lot of blog posts about machine learning and data science on this website. You can read these blog posts to keep yourself updated.

Machine Learning Mastery

Another fantastic site I would recommend is the machine learning mastery website. This site has some great articles on machine learning and deep learning, both theoretical and practical.

YouTube Channels

Nowadays, video lectures are very popular. You can find many free machine learning courses on Coursera or Udemy.



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