Course curriculum

  • 1
    Module 2: Introduction to AI
  • 2
    Module 3: Foundations of AI
  • 3
    Module 4: AI Technologies and Algorithms
  • 4
    Module 5: Practical Applications of AI
  • 5
    Module 6: AI in Technology
  • 6
    Module 7: Explainable AI (XAI)
  • 7
    Module 8: AI Ethics
  • 8
    Module 9: Generative AI and Prompt Engineering
  • 9
    Module 10: Fundamentals of ML
  • 10
    Module 11: Data Preprocessing
  • 11
    Module 12: Supervised Learning Regression
  • 12
    Module 13: Regression
  • 13
    Module 14: Classification
  • 14
    Module 15: Unsupervised Learning
  • 15
    Module 16: Reinforcement Learning
  • 16
    Module 17: Model Deployment & MLOps
  • 17
    Module 18: Deep Learning
  • 18
    Module 19: Federated Learning
  • 19
    Module 20: Natural Language Processing
  • 20
    Module 21: Time Series Forecasting
  • 21
    Module 22: Ethical and Advanced Concepts
  • 22
    Module 23: Cloud & AI Services
  • 23
    Module 24: Interactive coding and AI labs
  • 24
    Module 25: ML in Cybersecurity
  • 25
    Module 26: ML Techniques for Cybersecurity
  • 26
    Module 27: Applications of ML in Cybersecurity
  • Introduction to Artificial Intelligence (AI) Brief history and evolution of AI Current state of AI and future trends
  • Types of AI: Narrow AI, General AI, and Superintelligent AI Major Approaches to AI: Symbolic, Machine Learning, and Hybrid
  • Fundamental Algorithms in AI and ML Introduction to Neural Networks Architecture based Neural Networks ( FNNs, RNNs/LSTM, Transformer) Data processing based Neural Networks ( CNNs, GNNs) Functionality based Neural Networks ( GANs, SNNs) Natural Language Processing (NLP) Computer Vision (CV)
  • AI in Healthcare AI in Business AI in Autonomous Vehicles AI in Entertainment AI in Finance
  • AI with Blockchain AI in Technology AI for Edge computing
  • Basics of XAI
  • Understanding AI Ethics Potential Pitfalls and Controversies in AI Strategies for Responsible AI Deployment
  • Generative AI What is Prompt Engineering? Importance of Prompt Engineering Applications of Prompt Engineering
  • What is Machine Learning? Types of Machine Learning Importance of Data in ML Bias and Variance in ML Curse of Dimensionality Dimensionality Reduction Difference Between AI, ML, and Deep Learning
  • Importing data Data Cleaning Data Transformation Feature Engineering Concepts Feature Scaling and Normalization Feature Extraction
  • Supervised Learning Overfitting vs. Underfitting
  • Linear Regression Polynomial Regression Decision Tree Regression Random Forest Regression
  • Logistic Regression Support Vector Machine Naive Bayes
  • Clustering using K-means in Python Hierarchical Clustering Applications of Clustering in Real Life Difference Between Supervised and Unsupervised Learning
  • Reinforcement Learning Concept of Rewards and Penalties in RL Types of RL Algorithms (Value-Based, Policy-Based, Model-Based)
  • Basics of Model Deployment Introduction to MLOps
  • Introduction to Deep Learning Introduction to Optimization Activation Functions in Neural Networks Transformers
  • Federated Learning
  • Natural Language Processing (part 1) Natural Language Processing (part 2)
  • Stationarity in Time Series Time Series Decomposition
  • Ethical Concerns in ML (Bias, Fairness, Transparency) Interpretability of ML Models Future Trends in ML AutoML (Automated Machine Learning) Gradient Boosting
  • AI and ML in cloud
  • Interactive AI coding environment Rise of AI augmented coding
  • Role of Machine Learning in Cybersecurity Difference Between Traditional Security and AI-Driven Security
  • Anomaly Detection in Network Security Supervised vs. Unsupervised Learning for Threat Detection Deep Learning for Intrusion Detection Systems (IDS) Behavior-Based Malware Detection
  • Phishing Detection Using ML Spam Filtering and Email Security Fraud Detection in Financial Transactions ML in Identity and Access Management (IAM)