There are a lot of roadmaps that explain how to start AI learning, and they all start somewhere. Many start with Python, many start with maths, and many directly start with large language models and other AI agents.
As a student, you do not have to start learning everything in AI right from the beginning. You can start learning in layers. First, the basic concepts, then enough programming to test and try things. While you learn the necessary mathematics for AI, you start learning how to work with data. While you learn more about the data and programming, you start learning about Machine Learning. Finally, you start learning the deeper AI subjects. At every stage of learning, you create something small.
How to Start AI Learning as a Beginner?
To start with, you need to learn the basics of Python and work with simple data. After that, you will learn mathematics and statistics. Post that, you will deep dive into Machine Learning, Deep Learning and Generative AI.
Theory and practice are equally important when it comes to learning.
Step 1: Understand What Learning AI Actually Means
Using an AI chatbot does not mean you know how to build an AI system.
A student can:
Utilise AI using prompts and AI-driven software applications
Build with AI through the integration of models into software applications
Create models using data preparation, model training and evaluation
You don’t have to choose a specialisation right away. It’s enough to understand how AI is different from traditional software and how Generative AI is different from other Machine Learning models, for example, prediction models.
If you want to know where AI fits in technology education, read this guide on whether AI is a branch of engineering.
Step 2: Learn Basic Python and Start Building
Python is very practical for working with data, Machine Learning and, in general, with AI.
The student learns to use variables, if/else structures, for/while loops, functions, lists, dictionaries, file input/output, basic debugging, modules, notebooks, documentation and GitHub.
It is not necessary to become a complete programmer, but to understand how to write a simple programme to solve a problem, without copying a tutorial line by line.
A simple analyser, a tracker or even a tool to process text is enough for this task.
Step 3: Learn Mathematics and Data Together
Focus on:
Basic knowledge of algebra and linear algebra
Probability and statistics
Familiarity with functions, slopes and gradients
They learn step by step the required maths to work with the data as well: reading a dataset, dealing with missing values, making simple charts and dividing the data into a training set and a test set.
Mathematical thinking is not the same as memorising formulas. In small projects, e.g. building a dashboard, doing data analysis or even a programming task in the area of data modelling, such as finding patterns in the dataset, students see how mathematics and data can come together.
Step 4: Learn Machine Learning Before Jumping Too Far Ahead
Students who wish to gain more technical depth into how AI works should first learn basic Machine Learning before moving on to more advanced LLM frameworks/AI agents.
Start with supervised learning and unsupervised learning, and then move to regression, classification and clustering. Train a model and test it on a holdout set, using a train-test split, and also get familiar with overfitting and some basic measures to evaluate a model, e.g. accuracy, precision and recall, for supervised learning approaches.
The basic process matters:
Describe the problem.
Prepare the data.
Build the model.
Test it on new data.
A simple predictor, a classifier or even a recommendation system is enough for the first practice. But the important question is not only whether the code runs, but also why it worked and where it failed.
Step 5: Move to Deep Learning and Generative AI
After the students have mastered the basic steps of Machine Learning, they can proceed to understand the basics of neural networks, how different layers work, and how to train and evaluate them.
Next, students learn about Generative AI. This includes large language models, prompt design, embeddings, retrieval, model APIs, tool calling and AI agents.
Don’t try to learn all of the topics for Generative AI in sequence. Choose one and build a really good application with it, e.g. a document assistant, a classification system or a recommendation system. You should be able to explain why you chose to use the model or technique that you did.
Use a Learn–Build–Review Cycle
Use a simple cycle:
Learn: Learn one small concept.
Build: Utilise it in a tiny undertaking.
Review: Discuss what went well, what went wrong and what things you copied without understanding.
Then repeat.
The projects that matter start with a problem, not technology. For example, India Today highlighted this insurance workflow tool for agents and brokers that SST student 18-year-old Advith Sharma created to support the work of insurance agents and brokers started with analysing where insurance agents and brokers were spending their time, and then he came up with a tool to make their work easier. Lesson for beginners: first the problem, then the AI.
As you gain more experience and work in the field, improve a past project of yours by adding better data, a way to evaluate it, a simple interface to interact with it, deployment and documentation. A small project that you know inside out is worth way more than a copy of a tutorial that you can’t even understand.
Choose a Learning Path That Matches Your Goal
Everybody’s objectives are different when it comes to learning to use AI.
If you're intent on diving super deep into programming and related technical areas around AI/ML and other areas, then you’d likely benefit from locking down the Computer Science foundations first and then moving into related AI/ML and other subjects as you continue along your college pathway. The CS & AI programme here at Scaler School of Technology is specifically designed as a CS and AI-focused programme with core components like AI/ML, project work and industry exposure.
Students who are interested in building AI products and studying how users interact with problems and can be served by AI products can follow the AI & Business programme, which combines technology with product and business thinking to create great business solutions.
Conclusion
Firstly, if you are wondering how to start AI learning, don’t try to master every tool out there! AI for beginners should start from the basics of AI and learning Python first. Then learn the required maths and data skills. Afterwards, move on to ML, then to DL and then to Generative AI.
At each step in your learning process, focus on building. Measuring your learning in terms of how much you understand a topic, can explain it to others, and even go back to improve your own prior learning on the topic is far more important than counting how many courses you took on the topic. Students can also explore the wider AI engineering scope.
FAQs
Do I need coding to start learning AI?
As an AI developer, learning to code is crucial. For beginners, it is recommended that they start with learning the basics of Python programming and gradually increase the difficulty level of the projects that they work on.
How much mathematics is needed to learn AI?
For beginners who wish to gain in-depth knowledge of AI systems, it is crucial for them to grasp the basic mathematical concepts, such as algebra, linear algebra, probability, statistics and even a bit of calculus, in a very practical manner, rather than studying all of them in detail beforehand in order to start building AI systems.
Should I learn Machine Learning before Generative AI?
For the student who wants to explore more of the technical details behind an AI system, it is best to start learning the basics of Machine Learning first. Once a solid foundation in Machine Learning is established, it becomes increasingly easier to delve into more complex topics, such as neural networks, embeddings and Large Language Models (LLMs) and their applications.
