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HomeArtificial IntelligenceHow to learn AI in 2024 - Machine Learning Roadmap

How to learn AI in 2024 – Machine Learning Roadmap

Learning to use AI tools like ChatGPT can make you more productive at your job. But learning to build AI tools like ChatGPT will make sure you have a job to be productive at. Will AI take over your job? Well, I don’t know about that. But one thing I do know is that building AI tools would be one of the last jobs AI can replace.

So, if you want to be future-proof, you might want to invest some time into learning to build AI. And if that’s not a good enough reason for you, you will be shocked to know that OpenAI, the company that built ChatGPT, pays almost 1 million dollars to its AI engineers.

Build AI Tools like chatgpt

In this Article, I will give you a step-by-step guide on everything you need to learn to be able to create AI tools like ChatGPT. This Article is especially important for those who already knows a little bit of programming or Mathematics and wants to transition into an AI-related job.

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Why is machine learning essential in AI?

Human intelligence works through neurons, interconnected nodes in our brain. Similarly, AI uses artificial neurons, forming neural networks. Deep learning, a subset of machine learning, involves training these neural networks with vast amounts of data to make predictions, like how ChatGPT predicts the next word in a sentence.

To understand and build neural networks and deep learning systems, one must first grasp the basics of machine learning.

Machine learning has three pillars: Mathematics, Statistics, and Programming.

learn ai machine learning

To execute these learning follow below steps:

Key Concepts: Linear Algebra, Calculus, and Probability Theory.

Course: Mathematics for Machine Learning and Data Science on Coursera by DeepLearning.ai

Alternative Course: Data Science Math Skills by Duke University for those who want a less comprehensive option.

Key Concepts: Probability Distributions, Central Limit Theorem, Confidence Intervals, and Regression.

Course: Introduction to Statistics by Stanford University.

Key Language: Python (most popular for machine learning).

Resource: LearnPython.org for hands-on exercises on basic Python programming.

Course: Machine Learning Specialization on Coursera

Course Included:

  • Supervised learning algorithms (Linear and Logistic regression).
  • Unsupervised learning algorithms (Clustering).
  • Advanced algorithms with an introduction to neural networks.

Platform: Kaggle for hands-on practice and building a portfolio by following projects and participating in competitions.

Course: Deep Learning Specialization on Coursera.

Course Included:

  • Basics of deep learning.
  • Convolutional Neural Networks (Computer Vision).
  • Sequence Models (Natural Language Processing, including Transformer architecture used in ChatGPT).


By the end of these courses and practice, you will have the knowledge to build AI tools like ChatGPT. This path may seem long, but it’s the cost of working on next-generation technology. If you’re interested in a closely related field, consider Data Science, which involves developing insights from data without needing to be a machine learning expert.

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