Artificial Intelligence Learning Roadmap [AI Roadmap] 2024- Step-by-Step Guide

Artificial Intelligence Learning Roadmap

Do you want to learn Artificial Intelligence and looking for an Artificial Intelligence Learning Roadmap [AI Roadmap]?… If yes, this article is for you. In this article, you will find a step-by-step Roadmap to learn Artificial Intelligence for 2024.

So, without further ado, let’s start the Artificial Intelligence Learning Roadmap [AI Roadmap].

Artificial Intelligence Learning Roadmap 2024/AI Engineer Roadmap For Beginners

Artificial Intelligence is getting popular nowadays. We can see the evolution of AI in every field such as Self- Driven Cars, Robotics, Product Recommendation, Google Assistant/Siri/Alexa, etc.

To become an AI Engineer, you must have the following skills-

  1. Mathematics
  2. Programming
  3. Big Data
  4. Data Science
  5. Machine Learning
  6. Deep Learning
  7. Natural Language Processing
  8. Business Intelligence

Now, let’s move to the Artificial Intelligence Learning Roadmap 2024

Step 1. Understand Artificial Intelligence Basics

Before learning other essential skills, first, learn the basics of Artificial Intelligence.

At this step, you have to understand what is Artificial Intelligence, its impact, future trends of Artificial Intelligence, and its applications in various fields.

You can learn these things from any Youtube tutorial or from any FREE course.

I am also going to list some resources to learn the fundamentals of Artificial Intelligence.

Resources for Learning AI

Step 2. Learn Math

Your next step should be to learn Math. In the upcoming steps, you have to learn Machine Learning and Deep Learning algorithms. And knowledge of math will help you to understand the workings of deep learning and machine learning algorithms.

In Math, you have to learn-

  1. Linear Algebra: Essential for data manipulation, involving vectors and matrices used in data preprocessing and machine learning algorithms.
  2. Calculus: Differential calculus is crucial for optimization in machine learning, especially gradient-based techniques.
  3. Probability and Statistics: Important for understanding data distributions, hypothesis testing, and regression analysis in AI.
  4. Multivariate Calculus: Necessary for optimizing functions with multiple variables, common in AI models.
  5. Information Theory: Includes concepts like entropy, mutual information, and Kullback-Leibler divergence, relevant in data analysis and machine learning.
  6. Optimization: Knowledge of optimization algorithms like gradient descent for model training.
  7. Discrete Mathematics: Essential for algorithms and graph theory, used in search algorithms and network analysis.
  8. Set Theory: Fundamental for logic and knowledge representation in symbolic AI.
  9. Linear Programming: Useful in optimization problems, like resource allocation.
  10. Complex Analysis (Optional): Relevant for specific applications, such as signal processing.

There are various resources available to learn math concepts. I am also going to list some of the resources.

Resources for Learning Math

Step 3. Learn Programming Language

After learning math, your next step should be to learn the Programming Language.

In Artificial Intelligence, knowledge of programming language is essential. Without having programming language knowledge, you can’t implement anything.

For Artificial Intelligence, you can learn Python, R, or Java Programming language.

But if you ask me, I would suggest Python programming. Because it is beginner friendly language. If you are a beginner, you can easily learn Python.

Python has various supportive libraries and packages for Machine Learning and Deep Learning.

Resources for Learning Python Programming

Step 4. Learn Big Data

Why Big Data?

Because the AI model is trained using Data. And if the data size is big, then you should know Big Data Tools to manage this huge amount of Data.

Big companies like YouTube and Google are using recommendation systems to recommend something based on the previous search history, this is the blend of AI and Big Data.

That’s why knowledge of Big data tools is essential.

Hadoop, Spark, Cassandra, and MongoDB are the Big Data tools. You can learn any one tool.

Resources for Learning Big Data

Step 5. Learn Data Science

At this step, you need to learn Data acquisition, Data preparation, Data Analysis, and Data Manipulation.

You can learn Data Science from any course. I am going to list some of the Data Science Courses.

Resources for Learning Data Science

Step 6. Learn Machine Learning Algorithms

The next most essential skill is to learn machine learning algorithms.  You can learn Machine Learning Basics with the “Machine Learning by Andrew Ng” FREE Course.

You have to learn Machine Learning Algorithms-

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Resources for Learning Machine Learning

Step 7. Learn Deep Learning Algorithms

Once you learn the Machine Learning Algorithms, next learn Deep Learning Algorithms. Along with Deep Learning algorithms, you need to learn its Frameworks such as Tensorflow or Keras.

In Deep Learning algorithms, learn the following topics-

  • Neural Network
  • CNN
  • RNN
  • GAN
  • LSTM

Resources for Learning Deep Learning

Step 8. Learn Business Intelligence

Business Intelligence helps companies to make more data-driven decisions. At this step, you need to learn any Business Intelligence tools such as Tableau, PowerBI, or Qlikview.

These tools help you to create charts or graphs of your findings so that you can easily express your results to the stakeholders.

Resources for Learning BI Tools

Step 9. Work on Projects

First of all Congratulations! You are now well-versed in Artificial Intelligence Skills. It’s time to start working on some Real-World projects. Projects are most important to getting a job as an AI Engineer.

The more projects you will do, the more understanding of AI you will grasp. Projects will also provide more privileges to your Resume.

You can take the help of Kaggle to find projects and competitions on Artificial Intelligence.

These are some easy AI project ideas:

  1. Picture Sorter: Make a program that can recognize and sort different types of pictures.
  2. Feeling Checker: Create a tool that can tell if a message or review is positive or negative.
  3. Chat Helper: Build a chatbot that can have a conversation with people.
  4. What to Watch: Make a system that recommends movies or shows based on what someone likes.
  5. Face Finder: Create a program that can recognize faces in photos.
  6. Language Magic: Work on projects that involve understanding and working with languages.
  7. Mini Self-Driving Car: Try making a little car or robot that can move around on its own.
  8. Odd Thing Detector: Create a system that can find unusual stuff in data, like spotting fraud or problems.
  9. Health Help: Make something to assist with health or medicine, like a symptom checker or drug discovery tool.
  10. Game AI: Build an opponent for a game or a program that can make new game content.
  11. Future Predictor: Work on projects that guess what might happen next, like predicting stock prices or the weather.
  12. Voice Assistant: Make a talking assistant that can do things when you speak to it.
  13. Art Creator: Use AI to generate art, music, or even stories.
  14. Teaching AI: Develop something to help people learn better, like adaptive study tools.
  15. Money and AI: Work on money-related projects like stock trading, loan decisions, or spotting financial fraud.
  16. Robot Friend: Build a robot that can do things like cleaning or playing games.

Pick a project that interests you and matches your skills.

So that’s all, only these skills are required for learning Artificial Intelligence. Congratulations, it’s your first step toward AI.

Simplified Time-Framed AI Roadmap/ Artificial Intelligence Learning Roadmap 2024

Time FrameSteps to FollowWhat to DoResources
Months 1-2Get the Basics of AI– Learn what AI is and the main terms.1. AI For Everyone– Coursera FREE to Audit Course
2. Intro to Artificial Intelligence– Udacity FREE Course
Months 2-4Strengthen Your Math Skills– Start with simple math and move to more complex topics.1. Intro to Statistics Udacity FREE Course
2. Linear Algebra Refresher Course– Udacity FREE Course
Months 4-5Pick Up a Programming Language– Learn a language like Python and practice basic coding.1. Introduction to Python Programming(Udacity Free Course)
2. The Python Tutorial (PYTHON.ORG)
Month 6Understand Big Data and Data Science– Know about big data and the basics of data science.1. Intro to Hadoop and MapReduce(Udacity FREE Course)
2. Spark (Udacity FREE Course)
3. IBM Data Science Professional Certificate– Coursera
Months 7-8Explore Machine Learning– Begin with easy ML concepts and advance to more complex ones.1. Machine Learning by Georgia Tech(Udacity Free Course)
2. Machine Learning by Stanford University(Coursera Free to Audit Course)
Months 9-10Dive into Deep Learning– Learn about deep learning and get hands-on with TensorFlow or PyTorch.1. Deep Learning Specialization (
2. Deep Learning– Udacity
Months 11-12Focus on Business Intelligence and Projects– Learn AI in business and start working on your AI projects.1. Data Visualization in Tableau– Udacity FREE Course
2. Fundamentals of Visualization with Tableau– Coursera FREE to Audit Course

What does an AI Engineer do daily?

  1. Understanding Problems: An AI engineer starts their day by trying to understand what sorts of problems AI can help solve.
  2. Data Collection and Cleanup: They gather data from various sources and ensure it’s clean and ready to use in their AI models.
  3. Creating AI Models: AI engineers build AI models, which are like special tools that can help with these problems.
  4. Testing Models: They check how well these AI tools work and make improvements if needed.
  5. Writing Instructions: They write instructions in the form of computer code to make sure their AI models understand what to do.
  6. Staying Updated: They continue learning about the latest AI technology and techniques to stay on top of their game.
  7. Teamwork: AI engineers work with others on AI projects and share their progress and ideas.
  8. Fixing Issues: If anything goes wrong during their work, like computer problems, they figure out how to fix them.
  9. Keeping Records: They write down everything they do and how it all works for future reference.
  10. Meetings and Communication: They participate in meetings to discuss how their projects are going and plans for the future.
  11. Project Management: They ensure their AI projects stay on track, meeting deadlines and goals.
  12. Thinking Ethically: AI engineers consider what’s right and wrong in using AI and ensure their work follows the rules and is ethically sound.
  13. Continuous Learning: They keep learning new skills and stay updated with the latest advancements in AI.
  14. Putting AI to Use: They make sure the AI models they’ve created are working correctly in real-life situations.
  15. Quality Checks: They ensure the results produced by AI are accurate and trustworthy through careful testing.

Being an AI engineer requires flexibility, good problem-solving skills, and proficiency in computer programming to succeed in the exciting field of AI.

Now it’s time to wrap up this Artificial Intelligence Learning Roadmap [AI Roadmap] 2024!


In this article, I have discussed a step-by-step Artificial Intelligence Learning Roadmap [AI Roadmap] 2024. If you have any doubts or queries, feel free to ask me in the comment section. I am here to help you.

All the Best for your Career!

Happy Learning!

FAQ on AI ML Roadmap

Thank YOU!

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Though of the Day…

It’s what you learn after you know it all that counts.’

John Wooden

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