Basics of AI & ML

Build a strong foundation in Artificial Intelligence and Machine Learning. Designed for beginners to understand concepts through simple explanations and hands-on projects.

Course Overview

The Basics of Artificial Intelligence & Machine Learning course is designed to introduce learners to AI & ML concepts in a simple, structured, and practical manner.

The course emphasizes:

  • Strong conceptual foundation
  • Logical thinking and problem-solving skills
  • Hands-on exposure to real-world applications

This program avoids heavy mathematics and instead focuses on intuitive understanding, practical demonstrations, and real-time examples, making it ideal for beginners.

By the end of the course, learners will have a clear understanding of how AI & ML systems work and how they are applied across industries such as education, healthcare, finance, and automation.


What you will learn

    Introduction to AI & ML

    • What is Artificial Intelligence?
    • What is Machine Learning?
    • AI vs ML vs Deep Learning
    • Real-world applications of AI

    Python Installation & Environment Setup

    • What is Python and why it is used in AI & ML
    • Installing Python (Windows / macOS / Linux)
    • Installing libraries using pip
    • Introduction to IDEs: VS code & Jupyter Notebook
    • Running first Python program

    Python Programming Basics

    • Python syntax and indentation
    • Variables and data types
    • Operators and expressions
    • Conditional statements (if, else, elif)
    • Conditional loops (for, while)
    • Functions and basic program structure
    • Lists, tuples, dictionaries, and sets
    • Writing simple logical programs

    Python Libraries & Package Management

    • What are libraries and packages?
    • Installing packages using pip
    • Importing libraries in Python
    • Understanding virtual environments (concept)
    • NumPy: Numerical computing basics
    • Pandas: Data handling and analysis
    • Matplotlib: Data visualization
    • scikit-learn: Machine learning algorithms

    Data Concepts & Data Handling with Pandas

    • What is data? Types of data
    • Understanding datasets (rows, columns, features, labels)
    • Loading CSV and Excel files using Pandas
    • Pandas DataFrame and Series
    • Viewing and exploring data (head, tail, info, describe)
    • Selecting and filtering data
    • Handling missing values (basic level)
    • Simple data preprocessing concepts

    Data Visualization Basics

    • Why data visualization is important
    • Introduction to charts and plots
    • Line, bar, scatter plots (concept + demo)
    • Visualizing trends and patterns
    • Interpreting visual data for insights

    Machine Learning Fundamentals

    • What does it mean for a machine to learn?
    • Types of Machine Learning:
      • Supervised Learning
      • Unsupervised Learning
      • Semi-Supervised Learning
      • Reinforcement Learning
    • Training data vs testing data
    • Introduction to regression and classification
    • Model training workflow (high-level)
    • Overfitting and underfitting (concept)
    • Model evaluation basics (accuracy concept)

    Introduction to Deep Learning & AI Ethics

    • What is Deep Learning?
    • Basic idea of neural networks
    • Difference between ML and Deep Learning
    • Applications of deep learning
    • Ethical challenges in AI
    • Responsible and safe use of AI

    Hands-on Projects

    • Student Performance Prediction – Predicting student outcomes based on basic academic data.
    • Customer Segmentation – Grouping customers based on behavior or purchase patterns.
    • Message Classification – Classifying messages as important or non-important.
    • Simple Prediction Model – Building a basic model to predict values from given data.
    • Mini AI Application – A small end-to-end AI project combining data, model, and output.

Who Can Join This Course

  • School students (9th Std and above)
  • College students from any discipline
  • Beginners with no coding background
  • Working professionals entering AI & ML

Course Duration & Format

Duration 2 – 3 months (customizable based on batch)
Mode Offline / Online
Sessions Theory + hands-on practical labs
Access Real-time guidance and doubt-clearing sessions

Certification

Participants will receive a Course Completion Certificate upon successful completion of the program.