Week 1: Introduction to AI & Job Roles

• What is AI? Career paths in AI (ML Engineer, Data Scientist, etc.)

• Industry use cases (Healthcare, Fintech, EdTech)

• AI Career Roadmap + LinkedIn Profile Optimization

Week 2: Python for AI

• Data types, loops, functions, OOP

• File handling and exception management

• Hands-on: Write Python scripts for data extraction

Week 3: Data Analysis & Visualization

• Pandas for data manipulation

• Matplotlib & Seaborn for plotting

• Mini Project: Exploratory Data Analysis (EDA) on real datasets

Week 4: Machine Learning Overview + Tools

• Understanding datasets, model training/testing

• Workflow with Scikit-learn

Week 5: Supervised Learning

• Regression: Linear, Polynomial

• Classification: KNN, Decision Tree

• Project: Salary Prediction System

Week 6: Unsupervised Learning

• K-means Clustering

• PCA for dimensionality reduction

• Project: Market Segmentation for an e-commerce company

Week 7: AI Resume Project (Showcase)

• Implement an ML model to automate resume screening

• Upload project on GitHub & add it to LinkedIn

Week 8: Deep Learning Basics

• Neural networks, activation functions, backpropagation

• Keras & TensorFlow setup

• Project: MNIST Handwriting Recognition

Week 9: Natural Language Processing (NLP)

• Text cleaning, TF-IDF, vectorization

• Sentiment analysis with real tweets

• Project: Product Review Sentiment Analyzer

Week 10: Real-Time AI Apps

• AI in Chatbots, Voice, Image, and Video

• Build a simple Chatbot (Rasa/Rule-based)

Week 11–12: Final Capstone + Job Preparation

• Choose One Capstone Project:

  • Fake News Detection

  • Resume Matching System

  • Customer Churn Prediction

• Resume, GitHub, Interview Practice

• Certification + Internship Letter