Difference: AI vs ML vs DL vs Data Science
History and Evolution of AI
Applications of AI (Healthcare, Finance, Robotics, NLP)
Types of AI: Narrow, General, Super
Types of ML: Supervised, Unsupervised, Reinforcement Learning
Tools & Languages: Python, Jupyter, Colab, Anaconda

Python Basics: Syntax, variables, loops, functions Data Structures: Lists, Tuples, Dictionaries, Sets
File handling
NumPy for numerical computing
Pandas for data manipulation
Matplotlib and Seaborn for visualization
Basic OOP in Python

Linear Algebra: Vectors, Matrices, Matrix operations, Eigenvalues & Eigenvectors
Calculus: Derivatives, Partial Derivatives, Gradient Descent
Probability & Statistics:
Bayes Theorem
Probability distributions (Normal, Binomial, Poisson)
Mean, Median, Variance, Standard Deviation
Hypothesis testing and p-values

✅ Supervised Learning: Linear Regression (simple, multiple)
Logistic Regression
K-Nearest Neighbors (KNN)
Decision Trees
Random Forest
Support Vector Machines (SVM)
Naive Bayes
Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC, Confusion Matrix
✅ Unsupervised Learning: K-Means Clustering
Hierarchical Clustering
PCA (Principal Component Analysis)
Anomaly Detection

Artificial Neural Networks (ANN)
Architecture: Input, Hidden, Output layers
Activation functions: ReLU, Sigmoid, Softmax, Tanh
Forward & Backpropagation
Loss functions
Gradient Descent optimization
TensorFlow & Keras Introduction
Model creation, compilation, training, and evaluation
Saving and loading models

Convolutional Neural Networks (CNNs): Convolutions, Pooling, Filters
Image classification with CNN
Transfer learning
Recurrent Neural Networks (RNNs), LSTM, GRU
Sequence modeling, time-series data
Autoencoders
Hyperparameter Tuning: Grid Search, Random Search
Early stopping, Dropout

Text preprocessing: Tokenization, Stemming, Lemmatization
Bag-of-Words, TF-IDF
Sentiment Analysis
Word Embeddings: Word2Vec, GloVe
Sequence models for text (RNN, LSTM)
Text generation using LSTM or transformers (intro only)

Introduction to RL
Terminology: Agent, Environment, Actions, Rewards
Q-learning
Exploration vs Exploitation
Markov Decision Process (MDP)
Intro to Deep Q-Networks (DQN)

Python Libraries: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn
Deep Learning: TensorFlow, Keras, PyTorch (intro)
Data Handling: OpenCV (vision), NLTK/SpaCy (NLP)
Model Deployment Tools: Flask for serving models
Streamlit for UI
Google Colab / Jupyter for development

Saving and loading ML models (pickle, joblib)
Creating REST APIs using Flask or FastAPI
Streamlit for interactive dashboards
Introduction to Docker & Git for version control
Hosting models: Heroku, Render, Hugging Face

Build and deploy an end-to-end AI/ML project using real-world datasets
Example Projects:
Movie Recommendation System
Real Estate Price Predictor
Fake News Detector (NLP)
COVID-19 Detection from X-ray Images (CNN)
Stock Price Prediction (Time-series)
Chatbot (NLP + DL)
Include:
Data collection & cleaning
Model building & evaluation
Deployment with user interface
GitHub documentation