Artificial Intelligence Learning Roadmap 2024 - A Step-by-Step Guide


Understand Artificial Intelligence Basics

To begin the journey of becoming an expert in Artificial intelligence (AI), it is very important to learn the basic concepts of AI that lay its foundation. Learn about its evolution, and core components, like machine learning, natural language processing (NLP), and computer vision. Also, it is very important to familiarise yourself with the ethical considerations of AI applications. This helps to develop a proper knowledge and understanding of AI and by this, you will be ready to move ahead in this roadmap.

Learn Math

To develop a strong foundation for Artificial intelligence (AI), you need to know basic mathematical concepts. These concepts include algorithms, calculus, statistics, and probability. Knowledge of these basic concepts will help us understand various algorithms, models, and optimization techniques of AI.

Developing clarity and grip over these mathematical concepts will also help us to improve the analytical skills that are needed to create and understand complex models of AI. 

Learn Programming Language

To have a solid grip on AI, it is very important to master programming language. You can learn Python or R as they are popular languages. For AI development, Python is an excellent programming language as it has a lot of libraries that make the development purpose simple and efficient. 

So, it is very important to develop strong coding skills as by that we will be able to create and use algorithms, manipulate data, and develop AI solutions. So, to learn AI efficiently and excel in this Roadmap, learning programming is a key factor.

Learn Big Data

Learning Big Data is a very crucial component of AI. There are various technologies like Apache Hadoop and Apache Spark and they play a very crucial role in processing and analysing large datasets. So developing an understanding of these technologies is very crucial. Also, it is very important to master techniques to handle large amounts of data and learn about data storage and retrieval methods. 

These steps will help us to develop the skills required to navigate and leverage big data tools and extract valuable insights for AI development. Having expertise in Big Data technologies will help us to ensure the proper scalability and effectiveness of AI.

Learn Data Science

Information is very important and Data science is all about using the information to gain valuable insights from them so that proper decision-making can be done in the future. It involves data preprocessing, cleaning, and exploration. 

After that data visualization is performed to represent the data in a visual format that will ensure proper communication of data between the developer and the client, investor, or stakeholder, Along with these, understanding methodologies helps us to extract meaningful patterns from data. This helps us to convert the raw data that we have to actionable results. By having great knowledge of data science, you can make well-informed decisions and this will help the AI to act more powerfully. 

Learn Machine Learning Algorithms

Machine learning is a very crucial concept in the roadmap to learning AI. Having proper learning of various types of Machine learning like supervised, unsupervised, and reinforcement learning is very important. 

Along with these, having proper knowledge about the regression, classification, and clustering techniques, and utilizing popular libraries like scikit-learn is crucial. Machine learning is all about training a machine on specific data so that later it can work on its own and give the desired results. 

So, learning to develop these models and train and work with the algorithms is very important to develop a strong base for machine learning and AI. By learning about Machine learning, you can develop systems that are intelligent enough to make informed decisions and predictions.

Learn Deep Learning Algorithms

In the Roadmap to AI, another important thing is to learn about neural networking and deep learning. Understanding about neural networks, backpropagation, and optimization techniques is very important. 

Also, there are various frameworks like Tensorflow and PyTorch, and knowing about them is very important. Along with this, learn about convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These will help you build a strong knowledge of Deep learning algorithms.

Learn Business Intelligence

Learning how AI is used in businesses and organizations is very important as it isn't an application of AI. Business intelligence talks about how Artificial intelligence can be used so that businesses can get profits from it. There are various sectors like Maintaining inventories, supply chain management, timely maintenance of automobiles, and many more areas where AI can be used. 

Work on Projects

Just learning about AI is not enough, it’s very important to have real-world implications of it. This will help to strengthen your skills, develop networking among people with the same interests, and build an amazing portfolio. 

So, you can work on various projects and practice the topics you have learned earlier like NLP, Machine learning, etc. This will help you to showcase your skills and will help you to realize how much you have learned and how much development you need.

How to get jobs?

  • Build Skills: Learn the basics of AI, math, and programming languages like Python. Understand how AI is used in business.
  • Work on Projects: Practice your skills by working on real-world projects. This helps showcase your abilities to employers.
  • Stay Updated and Network Keep up with the latest AI trends and technologies. Connect with professionals in the AI community to learn and find job opportunities.

Moreover, you can hunt jobs with various online job search platforms like, Linkedln, Apna, Angle List, etc.

What does an AI Engineer do daily?

The work of an AI engineer is to design and implement AI solutions and collaborate with cross-functional teams. Also, he has to stay updated about the latest technological advancements taking place in this domain. Also, sometimes they may have to troubleshoot and optimize the AI models that are already made.


This provided AI roadmap will help you become an expert in Artificial intelligence. So, for anyone who is aspiring to become proficient in Artificial Intelligence, following these steps will help them a lot as they will be able to build a strong foundation and gain the skills that are required to excel in this field.

FAQ on AI ML Roadmap

How to study Artificial Intelligence free?

There are numerous online resources and courses available for free on various platforms, however, if you want to learn without hunting for different resources at different places Radient is there for you. We offer comprehensive training in Data Science, Analytics, Cloud Computing, Programming, Networking, and Cybersecurity, putting you at the forefront of the IT landscape. 

Should I learn AI or ML first?

Learning Machine Learning first will be advised as ML is a subset of Artificial intelligence. And learning ML first will help to bring more clarity to the concepts.

Which is hard AI or ML?

AI is a broader concept as compared to ML. Even ML is a subset of AI. Both topics have their challenges. So, the level of difficulty will depend on the topic you are going through.

Can AI work without ML?

In AI, Machine learning plays a very crucial role as using its algorithms, we can learn from data and make intelligent decisions and predictions.

Is AI a good career path?

Yes, today is the time when the demand for AI is very high, so in such an environment, learning and choosing AI as the profession will be the best decision.

Is AI highly paid?

People who are skilled in AI are paid heavily because of the demand for AI nowadays along with the skills they have. Their salaries are different based on their experience, expertise, and location. Top tech giants like Google, Meta, Netflix, and Amazon are paying lakhs of rupees every month.