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Introduction to AI and Machine Learning Course

Learn the basics of modern-time AI and represent an application of it. Discover the numerous ways to apply AI and possible developments.

AI using Javascript:

Create intelligence with us. This section covers the overview of AI and models, from facial recognition to sound classification.

🎉 Ready to start your child’s coding adventure? Get started with AI and Machine Learning today!

3 Level | Age 13+

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AI and Machine Learning

Choose Your Level

Select the level that best fits your learning journey

Level-1

Rs 3000.00/-

(2 hours)

Level-2

Rs 3000.00/-

(2 hours)

Level-3

Rs 3000.00/-

(2 hours)
Level 1

Introduction to AI and Machine Learning

Step into the fascinating world of Artificial Intelligence (AI) and Machine Learning (ML)! This course introduces students to the fundamentals of AI, exploring how machines can simulate human intelligence to make decisions, recognize patterns, and solve complex problems. Learners will dive into machine learning concepts, including supervised and unsupervised learning, data preprocessing, algorithms, and predictive modeling.

Through hands-on projects and exercises, students will:

  • Understand the basics of AI, ML, and their real-world applications.

  • Explore data analysis, feature selection, and model training.

  • Learn to implement algorithms for classification, regression, and clustering.

  • Work on practical projects that demonstrate AI decision-making and predictions.

  • Develop critical thinking and problem-solving skills to apply AI techniques effectively.

By the end of the course, students will have a solid foundation in AI and ML concepts, practical experience with data-driven projects, and the confidence to explore more advanced topics like deep learning, neural networks, and AI-powered applications.

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Introduction to AI and Machine Learning

Level 1 Syllabus

  • Introduction to AI & ML with JavaScript

    a. Basics & Setup

    • Introduction to AI and ML

    • Basics of JavaScript

    • JS Library P5 – Shapes

    • JS Library P5 – Colors

    b. Game-Based AI Projects

    • AI Powered Snake Game

    c. Machine Learning Algorithms

    • Introduction to ML5 & K-Means Algorithm

    • Image Classification

    • Pose Detection

    • K Nearest Neighbour (KNN) Algorithm

    • KNN Pose Net

    • BodyPix Model and Image Segmentation

    • Sound Classification and Dina Game

    • Word2Vec

    • Sentiment Analysis

  • Advanced AI & Game Development Projects

    a. Object & Face Detection

    • Object Detection

    • Object Detection Project

    • Sketch RNN

    • Sketch RNN Project

    • Face API

    • Face Detection

    b. Neural Networks & AI Integration

    • Neural Networks

    • Basketball Game Development

    • Adding AI to the Game

🎯 Major Projects (Level 1)

Image Classification Project

Dive into the exciting field of computer vision with the Image Classification Project! In this hands-on project, students will learn how to train machine learning models to identify and categorize images into different classes. They will explore the full workflow of an image classification system—from data collection and preprocessing to model training, evaluation, and deployment.

Image Classification Project
Level 2

Mathematics and Statistics

Strengthen your analytical and problem-solving skills with Mathematics and Statistics! This course provides a comprehensive foundation in essential mathematical concepts and statistical techniques that are critical for data analysis, programming, and real-world applications. Learners will explore topics such as algebra, calculus, probability, descriptive and inferential statistics, data visualization, and basic statistical modeling.

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Mathematics and Statistics

Level 2 Syllabus

  • Calculus & Advanced Concepts

    a. Functions & Complex Numbers

    • Composite and Inverse Functions

    • Complex Numbers, Rational Functions

    b. Calculus Fundamentals

    • Limits and Continuity

    • Derivatives

    • Integrals

    • Differential Equations

    • Series

  • Algebra & Functions

    a. Basics & Equations

    • Introduction to Algebra

    • Solving Basic Equations & Inequalities

    • Linear Equations Part 1

    • Linear Equations Part 2

    b. Functions & Polynomials

    • Functions Part 1

    • Functions Part 2

    • Quadratic Equations Part 1

    • Quadratic Equations Part 2

    • Polynomial Expressions

    • Exponential and Logarithmic Functions

    c. Trigonometry & Series

    • Introduction to Trigonometry

    • Trigonometric Functions Part 1

    • Trigonometric Functions

    • Series and Induction

    • Vectors

    • Matrices

  • Statistics & Probability

    a. Basics & Measures

    • Introduction to Statistics

    • Mean, Mode, Median

    • Variance, Covariance, and Correlation

    b. Regression & Probability

    • Linear Regression and Hypothesis Testing

    • Introduction to Probability

    • Counting, Permutation, and Combinations

    • Random Variables, Sampling Distribution

    • Advanced Regression

🎯 Major Projects (Level 2)

Quadratic Equation and Differential Equation

Build a strong foundation in mathematics with Quadratic Equation and Differential Equation! This course introduces students to key concepts in algebra and calculus that are essential for problem-solving in science, engineering, and programming. In the Quadratic Equation section, learners will explore methods to solve equations, understand roots, discriminants, and apply these concepts to real-world scenarios. The Differential Equation section covers the fundamentals of solving ordinary differential equations, understanding their behavior, and applying them to model dynamic systems such as motion, growth, and decay.

Quadratic Equation and Differential Equation
Level 3

Introduction to Data Science and Tools

Step into the world of Data Science with this comprehensive introduction to its concepts, techniques, and essential tools! This course is designed to provide students with a solid foundation in understanding how data is collected, processed, analyzed, and visualized to drive informed decision-making. Learners will explore the data science workflow, from gathering raw data to cleaning, analyzing, and interpreting results, while gaining hands-on experience with popular tools and technologies.

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Introduction to Data Science and Tools

Level 3 Syllabus

  • NumPy – Numerical Computing with Python

    a. Basics & Arrays

    • What is NumPy?

    • NumPy Arrays

    • Adding, Removing, and Sorting Elements

    • Reshaping and Converting Arrays

    b. Operations & Indexing

    • Array Operations and Broadcasting

    • Indexing and Slicing

    • Matrices and Generating Random Numbers

    • Transposing and Reshaping a Matrix

    • Working with Mathematical Formulas

    c. Data Handling & Visualization

    • Importing and Exporting CSV

    • Plotting Arrays with Matplotlib

  • Pandas – Data Manipulation & Analysis

    a. Basics & Data Structures

    • What is Pandas?

    • Introduction to Data Structures and Basic Functionalities

    • Input/Output Tools

    b. Indexing & Merging

    • Indexing, Selecting Data, Multi-Indexing, and Advanced Indexing

    • Merge, Join, Concatenate, Compare Objects

    • Reshaping and Pivot Tables

    c. Data Cleaning & Visualization

    • Working with Text Data and Missing Data

    • Chart Visualization

    • Table Visualization

    • Computational Tools

    • Time Series / Date Functionality and Time Deltas

  • Matplotlib – Data Visualization

    a. Basics & Figures

    • What is Matplotlib?

    • Anatomy of a Matplotlib Figure

    b. Plotting Techniques

    • Scatter Plot and Bar Plot

    • Histograms and Subplots

    • Customizing the Plots

  • TensorFlow – Machine Learning & Deep Learning

    a. Basics & Variables

    • What is TensorFlow and Why Use It?

    • TensorFlow Variables

    • TensorFlow Automatic Differentiation

    b. Graphs, Layers & Models

    • Introduction to Graphs and Functions

    • Introduction to Modules, Layers, and Models

    • Training Loops

    • Advanced AutoDiff

    c. Advanced Tensor Operations & Pipelines

    • Ragged Tensor

    • Sparse Tensor

    • Tensor Slicing

    • Data Input Pipelines

    • Optimization and Performance

🎯 Major Projects (Level 3)

Project Optimization and Project Visualization

Enhance your data science and analytical skills with Project Optimization and Project Visualization! In the Project Optimization module, students will learn techniques to improve project performance, streamline processes, and make data-driven decisions that maximize efficiency and effectiveness. This includes applying optimization algorithms, evaluating different strategies, and using computational tools to find the best solutions for complex problems.

The Project Visualization module focuses on transforming raw data and project insights into clear, compelling visual stories. Learners will explore charts, graphs, dashboards, and interactive visualizations using tools like Python libraries (Matplotlib, Seaborn, Plotly) or BI platforms. Students will learn how to present data effectively, highlight trends, and communicate results to stakeholders.

Project Optimization and Project Visualization