AI & Machine Learning​

Module 1: Introduction to Artificial Intelligence and Machine Learning

• Overview of AI and ML
  • Definition of artificial intelligence and machine learning
  • Historical background and evolution of AI
  • Applications of AI and ML in various industries
  • Key terminology and concepts

Module 2: Mathematics for Machine Learning

• Mathematical Foundations
  • Linear algebra basics: vectors, matrices, and operations
  • Probability theory: distributions, conditional probability, and Bayes’ theorem
  • Statistics fundamentals: mean, median, variance, and standard deviation
  • Optimization techniques: gradient descent and its variants

Module 3: Data Preprocessing

• Data Handling and Preparation
  • Importance of data quality and preprocessing
  • Data cleaning techniques: handling missing values, duplicates, and outliers
  • Data transformation: normalization and standardization
  • Feature engineering and selection techniques

Module 4: Supervised Learning

• Regression Algorithms
  • Linear regression: model formulation, cost function, and optimization
  • Polynomial regression and regularization techniques (Lasso and Ridge)
• Classification Algorithms
  • Logistic regression: binary classification and evaluation metrics
  • Decision trees and random forests: building and interpreting models
  • Support vector machines (SVM): hyperplanes and kernels
  • K-Nearest Neighbors (KNN): distance metrics and classification

Module 5: Unsupervised Learning

• Clustering Algorithms
  • K-Means clustering: algorithm and applications
  • Hierarchical clustering and DBSCAN
• Dimensionality Reduction
  • Principal Component Analysis (PCA): theory and implementation
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)

Module 6: Reinforcement Learning

• Introduction to Reinforcement Learning
  • Key concepts: agents, environments, and rewards
  • Markov Decision Processes (MDP)
  • Q-learning and policy-based methods
  • Applications of reinforcement learning

Module 7: Deep Learning

• Neural Networks
  • Fundamentals of neural networks: architecture and activation functions
  • Training neural networks: forward and backward propagation
  • Overfitting and regularization techniques
• Advanced Deep Learning
  • Convolutional Neural Networks (CNNs) for image recognition
  • Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequential data
  • Transfer learning and fine-tuning pre-trained models

Module 8: Natural Language Processing (NLP)

• Introduction to NLP
  • Text preprocessing techniques: tokenization, stemming, and lemmatization
  • Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) models
  • Word embeddings: Word2Vec and GloVe
  • Sentiment analysis and text classification

Module 9: Model Evaluation and Deployment

• Evaluating Models
  • Evaluation metrics for regression and classification
  • Cross-validation and hyperparameter tuning
  • Avoiding overfitting and underfitting
• Deploying Machine Learning Models
  • Overview of model deployment strategies
  • Using Flask or FastAPI for serving models
  • Introduction to cloud platforms for deployment (e.g., AWS, Google Cloud)

Module 10: Real-World Applications and Projects

• Hands-On Projects
  • Implementing machine learning solutions for real-world problems
  • Capstone project: end-to-end machine learning project from data collection to deployment
  • Presenting project findings and insights