Machine Learning & AI

Module 1: Introduction to AI & Machine Learning

Duration: 5 Hours

• Overview of AI & ML
  • What is AI?
  • History and Evolution of AI & ML
  • Applications of AI & ML in Various Industries
• Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Examples and Use Cases

Module 2: Fundamentals of Python Programming for ML

Duration: 10 Hours

• Introduction to Python for ML
  • Setting Up the Environment (Anaconda, Jupyter Notebooks)
  • Data Types, Variables, and Operators
• Control Structures & Functions
  • Loops, Conditionals, and Functions
  • Object-Oriented Programming Basics
• Libraries for Machine Learning
  • NumPy, Pandas, and Matplotlib
  • Data Preprocessing with Scikit-Learn

Module 3: Data Collection and Preprocessing

Duration: 8 Hours

• Understanding Data
  • Types of Data: Structured vs Unstructured
  • Data Collection Methods and Tools
• Data Cleaning and Transformation
  • Handling Missing Values
  • Data Normalization and Standardization
• Feature Engineering
  • Feature Scaling, Encoding, and Selection
  • Dealing with Imbalanced Data

Module 4: Supervised Learning Algorithms

Duration: 12 Hours

• Regression Techniques
  • Linear Regression
  • Multiple Linear Regression
  • Evaluating Regression Models (MSE, RMSE, etc.)
• Classification Algorithms
  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Support Vector Machines (SVM)
• Model Evaluation Metrics
  • Accuracy, Precision, Recall, F1-Score
  • Confusion Matrix
  • Cross-Validation and Hyperparameter Tuning

Module 5: Unsupervised Learning Algorithms

Duration: 8 Hours

• Clustering Techniques
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN
  • Clustering Evaluation Metrics (Silhouette Score)
• Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • t-SNE and Other Techniques

Module 6: Neural Networks & Deep Learning

Duration: 10 Hours

• Introduction to Neural Networks
  • Perceptron and Multilayer Perceptron (MLP)
  • Activation Functions and Backpropagation
• Deep Learning Models
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory Networks (LSTMs)
• Frameworks for Deep Learning
  • TensorFlow and Keras Basics
  • Building a Neural Network Model

Module 7: Reinforcement Learning

Duration: 4 Hours

• Basics of Reinforcement Learning
  • Markov Decision Processes
  • Exploration vs Exploitation
• Popular Algorithms
  • Q-Learning
  • Deep Q Networks (DQN)

Module 8: Capstone Project & Case Studies

Duration: 3 Hours

• Real-World Applications of AI/ML
  • Case Studies from Industry (Healthcare, Finance, Retail, etc.)
• Capstone Project
  • Building an End-to-End Machine Learning Model
  • Presenting Results and Insights

Bonus: Ethical Considerations in AI

Duration: 1 Hour

• AI Ethics and Bias
  • Fairness, Accountability, and Transparency
• AI in Society
  • Future Trends and Opportunities