Module 1: Introduction to Artificial Intelligence
  • What is AI?
    • History and evolution of AI
    • Types of AI: Narrow AI, General AI, Superintelligence
    • Key concepts: Machine Learning (ML), Deep Learning (DL), Neural Networks
    • Applications of AI
      • AI in different industries: Healthcare, Finance, Retail, Automotive
      • Real-world AI applications: Chatbots, recommendation systems, self-driving cars
      • Ethical considerations and AI safety
     
Module 2: Mathematics and Statistics for AI
  • Linear Algebra
    • Vectors, matrices, matrix operations
    • Eigenvalues and eigenvectors
    • Applications in AI and ML
  • Calculus for Optimization
    • Derivatives and integrals
    • Partial derivatives and gradients
    • Optimization techniques: Gradient descent
  • Probability and Statistics
    • Basic probability concepts
    • Bayes’ theore
Module 3: Machine Learning Fundamentals
  • Introduction to Machine Learning (ML)
    • Types of ML: Supervised, Unsupervised, Reinforcement Learning
    • ML workflow: Data collection, data preprocessing, training, testing, evaluation
  • Supervised Learning
    • Algorithms: Linear regression, logistic regression, decision trees, K-nearest neighbors (KNN)
    • Applications: Classification, regression
    • Model evaluation metrics: Accuracy, precision, recall, F1-score, confusion matrix
  • Unsupervised Learning
    • Algorithms: Clustering (K-means, hierarchical), dimensionality reduction (PCA)
    • Applications: Customer segmentation, anomaly detection
  • Reinforcement Learning
    • Basic concepts: Agents, environments, rewards
    • Q-learning and policy gradients
    • Applications: Game AI, robotics
Module 4: Deep Learning
  • Neural Networks (NN)
    • Structure of neurons and layers
    • Forward and backward propagation
    • Activation functions: Sigmoid, ReLU, Softmax
    • Loss functions: Mean Squared Error, Cross-Entropy
  • Deep Neural Networks (DNN)
    • Deep learning architectures
    • Hyperparameter tuning: Learning rate, batch size, epochs
  • Convolutional Neural Networks (CNN)
    • Introduction to CNN for image processing
    • Layers in CNN: Convolution, pooling, fully connected
    • Applications: Image recognition, computer vision
  • Recurrent Neural Networks (RNN)
    • Introduction to RNN for sequential data
    • Long Short-Term Memory (LSTM) networks
    • Applications: Time series analysis, speech recognition
Module 5: Natural Language Processing (NLP)
  • Introduction to NLP
    • Tokenization, stemming, lemmatization
    • Vectorization techniques: Bag of Words, TF-IDF, word embeddings
  • NLP Models and Techniques
    • Language models: N-grams, Markov models
    • Sentiment analysis, text classification, named entity recognition (NER)
    • Popular NLP tools: NLTK, spaCy, Hugging Face transformers
  • Advanced NLP Techniques
    • Sequence-to-sequence models
    • Attention mechanisms and transformers
    • Applications: Machine translation, chatbots, voice assistants
Module 6: Computer Vision
  • Basics of Computer Vision
    • Image representation and preprocessing
    • Image augmentation and feature extraction
  • Image Recognition and Detection
    • Object detection: YOLO, R-CNN, Fast R-CNN
    • Semantic segmentation
  • Face Recognition and Video Analytics
    • Face recognition algorithms
    • Applications of computer vision in security and surveillance