Phase 1: Python Foundations (12th Standard & Beyond)

Chapter 1: Kickstarting with Python – Setting up environments (Jupyter, VS Code), basic syntax, variables, and data types (Strings, Integers, Floats).

Chapter 2: Logic and Control – Mastering if-else statements, for and while loops, and error handling with try-except.

Chapter 3: Data Structures – Comprehensive study of Lists, Tuples, Dictionaries, and Sets for data organization.

Chapter 4: Functional Programming – Defining functions, scope, recursion, and lambda functions.

Chapter 5: Object-Oriented Programming (OOP) – Classes, objects, inheritance, and encapsulation for building scalable software.

Phase 2: Data Science Toolkit (The Core of AI)

Chapter 6: Numerical Computing with NumPy – High-performance arrays and mathematical operations.

Chapter 7: Data Manipulation with Pandas – Cleaning, filtering, and analyzing datasets using DataFrames.

Chapter 8: Data Storytelling – Advanced visualization using Matplotlib and Seaborn to identify data patterns.

Chapter 9: The Math of AI – Practical linear algebra, calculus, and statistics required for model understanding.

Phase 3: Machine Learning Mastery

Chapter 10: Introduction to ML – Understanding the ML pipeline: Data collection, preprocessing, and model training.

Chapter 11: Supervised Learning (Regression) – Predicting values with Linear and Multiple Regression.

Chapter 12: Supervised Learning (Classification) – Logistic Regression, Decision Trees, Random Forests, and SVMs.

Chapter 13: Unsupervised Learning – Finding hidden patterns with K-Means Clustering and Dimensionality Reduction (PCA).

Chapter 14: Model Evaluation – Mastering metrics like Accuracy, Precision, Recall, and Hyperparameter Tuning using GridSearchCV.

Phase 4: Advanced AI & Deep Learning

Chapter 15: Neural Networks Foundations – Building multi-layer perceptrons, understanding activation functions, and backpropagation.

Chapter 16: Computer Vision (CNNs) – Image recognition and classification using TensorFlow or PyTorch.

Chapter 17: Natural Language Processing (NLP) – Text processing, sentiment analysis, and sequence modeling with RNNs and LSTMs.

Chapter 18: Generative AI & LLMs – Introduction to Transformers, Prompt Engineering, and building applications with Large Language Models.

Phase 5: Future-Ready Projects & Deployment

Chapter 19: Agentic AI – Understanding autonomous agents that can perform tasks independently.