ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
1. Introduction to Artificial Intelligence
- Overview of Artificial Intelligence
- History and Evolution of AI
- Key Concepts in AI: Agents, Environments, and Goals
- AI Applications in Various Industries
2. Machine Learning Fundamentals
- Introduction to Machine Learning
- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Mathematical Foundations: Probability, Statistics, and Linear Algebra
- Data Preprocessing and Feature Engineering
3. Supervised Learning
- Regression
- Linear Regression
- Polynomial Regression
- Ridge and Lasso Regression
- Classification
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Decision Trees and Random Forests
- Naive Bayes Classifier
- Model Evaluation: Cross-Validation, Confusion Matrix, ROC-AUC
- Hyperparameter Tuning: Grid Search, Random Search
4. Unsupervised Learning
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Association Rule Learning: Apriori, Eclat
5. Neural Networks and Deep Learning
- Introduction to Neural Networks
- Activation Functions and Backpropagation
- Deep Learning Basics: Neural Networks, Layers, and Nodes
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Generative Adversarial Networks (GANs)
- Transfer Learning and Pre-trained Models
6. Natural Language Processing (NLP)
- Text Preprocessing and Tokenization
- Bag of Words and TF-IDF
- Word Embeddings: Word2Vec, GloVe
- Sequence Models: RNNs, LSTMs, and GRUs
- Sentiment Analysis
- Text Classification and Summarization
7. Reinforcement Learning
- Introduction to Reinforcement Learning
- Markov Decision Processes (MDPs)
- Q-Learning and Deep Q-Networks (DQN)
- Policy Gradients and Actor-Critic Methods
8. AI in Practice
- Building AI Models with Python and Libraries (TensorFlow, Keras, PyTorch)
- AI for Computer Vision
- AI for Healthcare, Finance, and Other Industries
- Ethics in AI and Bias Mitigation
9. Project Work
- End-to-End Machine Learning Project
- Deep Learning Project with CNNs or RNNs
- NLP Project: Text Classification or Sentiment Analysis
- Reinforcement Learning Project
10. Soft Skills and Interview Preparation
- Problem-Solving Techniques
- System Design Concepts
- Coding Practice with Data Structures and Algorithms
- Mock Interviews and Resume Building
11. Optional Topics
- Advanced Deep Learning: Autoencoders, GANs, Transformers
- AI in Robotics
- AI for Internet of Things (IoT)
- AI Model Deployment and Monitoring
