AI, ML and Data Science Tutorial
Last Updated :
25 Aug, 2025
This article covers everything you need to learn about AI, ML and Data Science, starting with Python programming, statistics and probability. It also includes EDA, visualization, ML, deep learning, AI, projects and interview questions for career preparation.
1. Learning Python
Python is one of the most popular programming languages today, known for its simplicity, extensive features and library support. Its clean syntax makes it beginner-friendly, while its libraries and frameworks makes it perfect for developers.
2. Math For Data Science
Math for Data Science is all about the fundamental mathematical tools and concepts you need to work effectively with data. It includes Statistics & Probability, Linear Algebra and Calculus.
3. Exploratory Data Analysis
Exploratory Data Analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often using visual methods. It involves understanding data, cleaning data, visualizing data and further analysis.
4. Data Analysis
Data Analysis is the technique of collecting, transforming and organizing data to make future predictions and informed data-driven decisions. It also helps to find possible solutions for a business problem.
There are six steps for Data Analysis which are: Ask or Specify Data Requirements, Prepare or Collect Data, Clean and Process, Analyze, Share, Act or Report.
5. Data Visualization
Data visualization is the process of turning data into visual representations like charts, graphs and maps. It helps us understand trends, patterns and outliers.
6. Machine Learning
Machine learning is a subset of Artificial Intelligence (AI) that enables computers to learn from data and make predictions without being explicitly programmed.
It can be categorized into three types: Supervised Learning, Unsupervised Learning and Reinforcement Learning.
7. Data Science with Python
Data science enables organizations to make informed decisions, solve problems and understand human behavior. As the volume of data grows, so does the demand for skilled data scientists. The most common languages used for data science are Python and R, with Python being particularly popular.
8. Deep Learning
Deep Learning is a branch of Artificial Intelligence (AI) that enables machines to learn from large amounts of data. It uses neural networks with many layers to automatically find patterns and make predictions.
9. Artificial Intelligence
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans.
10. Generative AI & LLM
Generative AI (Gen AI) is a branch of artificial intelligence that can create new content instead of just analyzing data. It uses machine learning models (like large language models, GANs, and diffusion models) to generate text, images, audio, code, or even video.
LLM (Large Language Model) is a type of artificial intelligence model designed to understand and generate human-like language.
AI-ML-DS Interview Questions
The AI-ML-DS Interview Series is an essential resource designed for individuals aspiring to start or switch careers in the fields of Artificial Intelligence (AI), Machine Learning (ML) and Data Science (DS).
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Machine Learning Basics
Python for Machine Learning
Feature Engineering
Supervised Learning
Unsupervised Learning
Model Evaluation and Tuning
Advanced Techniques
Machine Learning Practice