• • Introduction to Machine Learning
  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
  • Key concepts: Training, Testing, Validation

  Data Preprocessing:

  • Data cleaning, normalization, and transformation
  • Handling missing data and outliers

  Supervised Learning Algorithms:

  • Linear Regression, Logistic Regression
  • Decision Trees, Random Forests

  Unsupervised Learning Algorithms:

  • K-means Clustering, Hierarchical Clustering
  • Principal Component Analysis (PCA)

  Model Evaluation and Selection:

  • Cross-validation, confusion matrix, precision, recall, F1 score

• Introduction to Neural Networks
• Perceptron, activation functions, feedforward neural networks

Deep Learning Architectures:

• Convolutional Neural Networks (CNNs) for image processing
• Recurrent Neural Networks (RNNs) for sequential data

Training Deep Neural Networks:

• Backpropagation, gradient descent, optimization techniques

Transfer Learning and Fine-tuning:

• Using pre-trained models for new tasks

• Introduction to NLP
• Basics of text processing and tokenization

Text Classification and Sentiment Analysis:

• Bag of Words, TF-IDF, word embeddings (Word2Vec, GloVe)

Sequence Models in NLP:

• RNNs, LSTMs, GRUs

Advanced NLP Techniques:

• Transformers, BERT, GPT

• Introduction to Reinforcement Learning
• Key concepts: agents, environments, rewards

Policy and Value-Based Methods:

• Q-learning, Deep Q-Networks (DQN)

Advanced Topics in RL:

• Policy gradients, Actor-Critic methods

Case Studies in RL:

• Applications of RL in gaming, robotics, finance

AI in Healthcare:

• Applications and challenges

AI in Finance:

• Fraud detection, algorithmic trading

AI in Autonomous Systems:

• Self-driving cars, drones

AI in Natural Sciences:

• Climate modeling, bioinformatics

Future of AI:

• Emerging trends and technologies

• Capstone Project Development
• Choose a real-world problem to solve using AI
• Develop a project plan, preprocess data, build and train models
• Evaluate and fine-tune the models
• Present the project results