Become a Machine Learning and Artificial Intelligence Expert

Not long ago, pivot tables were the only way of processing numbers and analyzing data. Most of the data analysts utilized Excel to process data. Back then, Python was a complex area of programming which was far from reach.

However, today, the situation has changed. We are in a different era where Excels are no longer the optimum way of data analysis, and Python is increasingly becoming more popular due to AI and ML.

Hence, in this article, we will discuss how you can become an expert in machine learning and artificial intelligence.

Of course, there are multiple courses on AI and ML. But, to effectively complete these courses and learn from it, you need to know how to proceed. For example, most of the artificial intelligence and machine learning courses require basic knowledge of software development and related aspects.

So, without further ado, let’s dive in and understand how you can become an expert in ML and AI.

1. The Basics

Before you start a career in machine learning and artificial intelligence, you know what the field is all about. It is not always about robots (similar to what we see Iron Man develop) but AI is in simple things as well. This is the essence of this technology, right?

Gain knowledge of the following concepts:

  • What is machine learning?
  • What is analytics?
  • What is big data?
  • What is data science?
  • What is artificial intelligence?
  • Difference in the above aspects
  • How can the above domains be applied in real-life?

2. Statistics

Not every data scientist or expert in similar fields need to know statistics because you can become a data scientist without becoming a statistician. However, with that being said, it is also true that you can’t completely ignore statistics if you wish to become a machine learning expert.

Simply check the below-suggested topics, try to grasp what these concepts mean and how you can implement these concepts.

  • Sampling
  • Data structures
  • Probability
  • Random variable distribution
  • Categorical and numerical data inference
  • Logistics, linear, and multiple regression

3.  R or Python

Programming languages have now become easier and simpler to learn and you can’t learn machine learning and artificial intelligence without knowing programming languages. So start with R and Python. Both the languages are intertwined and we would suggest learning the following topics:

  • Read, export, and import
  • Data structures
  • Data analysis of quality
  • Data preparation
  • Data cleaning
  • Data visualization
  • Data manipulation – filtering, sorting, aggregating, etc.

4. Unsupervised Models

When you have data with several aspects and variables, you can use unsupervised models for analyzing it. For example, you have data for countries related to population, health, industries, etc. If you have to compare this data, what would you do? It can’t be achieved manually so you would use machine learning. Here, unsupervised machine learning algorithms can help you.

Understand the following topics for same:

  • Association rules
  • k-means clustering

5. Supervised Models

Supervised models are models that use labelled data. This means that the data already know the answer or correct values.

For example, you have a dataset containing thousands of customer profiles with repayment history. Using this data, can you tell which customers are more likely to default?

Supervised learning models can help you solve this problem. You have the data, you have the algorithm, thus, you can find a solution through supervised learning and prediction.

Here are the topics that you should cover:

  • Logistic regression
  • Ensemble models like random forest and bagging
  • Classification trees
  • Supervised vector machines

6. Explore Deep Learning

We are all familiar with Siri. It is a result of deep learning. If you want to deep dive into machine learning and create assistants like Siri, then you need to deeply understand deep learning algorithms.

Using this, you can make machines listen, learn, write, and execute tasks in a defined manner.

Below are the topics that you should cover for deep learning:

  • Natural language processing
  • Artificial neural networks
  • TensorFlow
  • Convolutional neural networks
  • Open CV

7. Complete a Course

Lastly, once you have gained knowledge of the above concepts, take a course in machine learning and artificial intelligence to improve your overall knowledge. You can get a certification which can help you secure a job role in the industry. Further, there are several aspects of artificial intelligence and machine learning that only experts can teach you. Through a course from Global Tech Council, you can understand these crucial aspects.


It is necessary to understand that artificial intelligence and machine learning experts have advanced knowledge of the intricate concepts. More than these, these experts consistently keep brushing their knowledge and keep learning new, evolving aspects. Hence, you need to keep learning and improving your knowledge to keep staying relevant in the job market as well as the industry. If you face any issue in achieving the same, refer to the course from Global Tech Council.