1. Introduction to AI & Machine Learning

• What is AI, ML, DL

• Types of machine learning

• End-to-end ML lifecycle

• Tools & environment setup (Jupyter, VS Code, Anaconda)

2. Python for Machine Learning & Data Analysis

• Python basics & OOP

• NumPy, Pandas, Matplotlib, Seaborn

• EDA (Exploratory Data Analysis)

• Feature engineering & preprocessing

• Working with datasets

3. Machine Learning Algorithms

• Linear & Logistic Regression

• Decision Trees, Random Forests, XGBoost

• SVM, KNN, Naive Bayes

• Hyperparameter tuning

• Model evaluation metrics

4. Deep Learning Foundations

• Neural networks (ANN)

• Activation functions

• Backpropagation

• Optimization techniques

• TensorFlow / Keras basics

5. Natural Language Processing (NLP)

• Text preprocessing & cleaning

• Tokenization, Stemming, Lemmatization

• Bag of Words, TF-IDF

• Word Embeddings (Word2Vec, GloVe)

• RNN, LSTM, GRU models

• Sentiment analysis project

6. Computer Vision

• Image processing basics (OpenCV)

• CNN architecture

• Image classification & object detection

• Transfer Learning

• YOLO / SSD basics

• Real-time image processing project

7. Model Deployment

• Flask basics for deployment

• Streamlit app development

• Building REST APIs

• Hosting ML models on cloud (Render, AWS, Railway)

• Full end-to-end deployment project