Week 1: Foundations of AI & Machine Learning (Days 1–7)

 Day 1 – Introduction

- What is AI vs ML vs Deep Learning
- Types of ML: Supervised, Unsupervised, Reinforcement Learning
- AI applications
- Tools: Python, Jupyter, scikit-learn, Pandas, Numpy

Day 2 – Python for ML

- Data types, loops, functions
- Numpy for numerical data
- Pandas for data handling
- Matplotlib & Seaborn for visualization

Day 3 – Math for ML

- Linear algebra basics
- Probability & stats (mean, variance, std dev, distributions)
- Gradient descent intro

Day 4 – Data Preprocessing

- Handling missing data, outliers
- Label encoding, one-hot encoding
- Feature scaling (Normalization, Standardization)
- Train/test split

Day 5 – Supervised Learning Overview

- Regression vs Classification
- Model training/testing
- Evaluation metrics (accuracy, precision, recall, F1)

Day 6 – Linear Regression

- Simple/multiple linear regression
- Loss function, gradient descent
- Hands-on: Predict house prices

Day 7 – Logistic Regression

- Binary classification
- Sigmoid function
- Hands-on: Classify diabetes dataset

Week 2: Core ML Algorithms (Days 8–14)

Day 8 – Decision Trees & Random Forests

- Tree building, overfitting
- Ensemble methods
- Hands-on: Titanic survival prediction

Day 9 – Support Vector Machines (SVM)

- Linear & kernel SVM
- Hyperparameters
- Hands-on: Classify digits

Day 10 – K-Nearest Neighbors (KNN)

- Lazy learning
- Choosing K
- Hands-on: Iris classification

 Day 11 – Naive Bayes

- Bayes theorem
- Types: Gaussian, Multinomial
- Hands-on: Spam detection

Day 12 – Unsupervised Learning (Clustering)

- K-Means clustering
- Elbow method
- Hands-on: Customer segmentation

Day 13 – Dimensionality Reduction

- PCA (Principal Component Analysis)
- t-SNE (for visualization)
- Hands-on: Visualize MNIST data

Day 14 – Model Evaluation & Tuning

- Cross-validation
- Hyperparameter tuning (GridSearch, RandomSearch)
- Avoiding overfitting (regularization, dropout)

Week 3: Deep Learning & Projects (Days 15–21)

Day 15 – Intro to Deep Learning

- Neural networks
- Perceptron, activation functions
- Feedforward, backpropagation

Day 16 – TensorFlow/Keras Basics

- Building models in Keras
- Compile, fit, evaluate
- Hands-on: Binary classifier (Keras)

Day 17 – CNNs (Convolutional Neural Networks)

- Image data
- Convolutions, pooling, filters
- Hands-on: Classify MNIST/CIFAR10

Day 18 – RNNs (Recurrent Neural Networks)

- Sequence modeling
- LSTM, GRU
- Hands-on: Sentiment analysis

Day 19 – NLP with ML

- Text preprocessing
- TF-IDF, Word2Vec
- Hands-on: Text classification

Day 20 – Mini Project Day

Choose 1:
- Stock price prediction (Regression)
- Fake news detection (NLP)
- Disease prediction (Classification)
- Image classifier (CNN)

Day 21 – Final Wrap-up & Deployment

- Review concepts
- Model deployment: Streamlit / Flask intro
- Exporting ML models with Pickle
- Next steps in AI (e.g., GPT, RL, MLOps)

🛠 Tools You’ll Use: – Python – Jupyter Notebook – scikit-learn – TensorFlow/Keras – Pandas, Numpy, Matplotlib – Google Colab (optional for GPU)