. Introduction to AI and Machine Learning

• Overview of AI and ML
  • History and evolution
  • Key milestones and breakthroughs
• Applications of AI and ML
  • Real-world use cases
  • Industry-specific applications

2. Mathematical Foundations

• Linear Algebra
  • Vectors and matrices
  • Eigenvalues and eigenvectors
• Probability and Statistics
  • Probability distributions
  • Hypothesis testing and p-values
• Calculus
  • Derivatives and integrals
  • Gradient and optimization

3. Basic Machine Learning Concepts

• Supervised Learning
  • Regression (Linear Regression, Polynomial Regression)
  • Classification (Logistic Regression, K-Nearest Neighbors, Support Vector Machines)
• Unsupervised Learning
  • Clustering (K-Means, Hierarchical Clustering)
  • Dimensionality Reduction (PCA, t-SNE)
• Evaluation Metrics
  • Accuracy, precision, recall, F1-score
  • ROC and AUC

4. Intermediate Machine Learning Techniques

• Ensemble Methods
  • Bagging and Boosting
  • Random Forests, Gradient Boosting Machines
• Model Selection and Hyperparameter Tuning
  • Cross-validation
  • Grid Search and Random Search
• Feature Engineering
  • Feature selection
  • Feature scaling and normalization

5. Neural Networks and Deep Learning

• Introduction to Neural Networks
  • Perceptrons
  • Feedforward Neural Networks
• Deep Learning Basics
  • Neural network architectures
  • Activation functions (ReLU, Sigmoid, Tanh)
• Training Neural Networks
  • Backpropagation
  • Optimization techniques (SGD, Adam)
• Deep Learning Frameworks
  • Introduction to TensorFlow
  • Introduction to PyTorch

6. Convolutional Neural Networks (CNNs)

• Basics of CNNs
  • Convolution operation
  • Pooling layers
• CNN Architectures
  • LeNet, AlexNet, VGG, ResNet
• Advanced CNN Topics
  • Transfer Learning
  • Fine-tuning pre-trained models
• Practical Implementation with Libraries
  • Implementing CNNs with TensorFlow/Keras
  • Implementing CNNs with PyTorch

7. Recurrent Neural Networks (RNNs) and Sequence Models

• Introduction to RNNs
  • Basics of sequence data
  • RNN architectures
• Advanced RNNs
  • Long Short-Term Memory (LSTM)
  • Gated Recurrent Units (GRUs)
• Applications of RNNs
  • Time series prediction
  • Language modeling
• Practical Implementation with Libraries
  • Implementing RNNs with TensorFlow/Keras
  • Implementing RNNs with PyTorch

8. Natural Language Processing (NLP)

• Text Processing and Feature Extraction
  • Tokenization, stemming, and lemmatization
  • TF-IDF, word embeddings (Word2Vec, GloVe)
• NLP Tasks and Models
  • Sentiment Analysis, Named Entity Recognition (NER)
  • Transformers and BERT
• Practical Implementation with Libraries
  • NLP with TensorFlow/Keras
  • NLP with Hugging Face Transformers

9. Reinforcement Learning

• Introduction to Reinforcement Learning
  • Markov Decision Processes (MDPs)
  • Q-Learning and SARSA
• Advanced RL Techniques
  • Deep Q-Networks (DQN)
  • Policy Gradients and Actor-Critic Methods
• Practical Implementation with Libraries
  • RL with OpenAI Gym
  • RL with TensorFlow/Keras

10. Advanced Topics

• AI Ethics and Fairness
  • Bias in AI models
  • Ethical considerations and responsible AI
• AI in Production
  • Model deployment and monitoring
  • CI/CD for ML models
• Explainability and Interpretability
  • SHAP and LIME
  • Model interpretability techniques
• GPT
  • Making personalised get with available api
  • Using GPT with best prompts

11. Practical Projects and Case Studies

• End-to-End Projects
  • Data collection, preprocessing, and cleaning
  • Model building, evaluation, and deployment
• Case Studies
  • Detailed analysis of successful AI implementations

12. Capstone Project

• Project Planning and Execution
  • Selecting a problem statement
  • Developing and presenting a solution