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Introduction
Course Introduction
Module 1: Foundations of Artificial Intelligence (AI)
1.1 History
1.2 What is AI?
1.3 Artificial Intelligence Based on Capabilities
1.4 Artificial Intelligence Based on Functionalities
1.5 Method-Based and Application Based
1.6 Case Study 1: Healthcare
1.7 Case Study 2: Retail
1.8 Case Study 3: Finance
1.9 Case Study 4: Marketing
Module 2: Mathematical Concepts of AI
2.1 Vectors, Matrices, and Their Operations
2.2 Eigenvalues, Eigenvectors, and Linear Transformations
2.3 Determinants
2.4 Derivatives, Partial Derivatives, and Gradients
2.5 Optimization Techniques
2.6 Integration
2.7 Probability Distributions
2.8 Hypothesis Testing
2.9 Bayesian Inference
2.10 Sets and Logic
2.11 Graph Theory
2.12 Combinatorics
Module 3: Python for Developer
3.1 Getting Started with Python: History and Setup
3.2 Programming Fundamentals: Basic Syntax
3.3 Programming Fundamentals: Control Flow
3.4 Programming Fundamentals: Data Structure
3.5 Code Organization: Modules and Packages
3.6 NumPy: The Numerical Workhorse
3.7 Pandas: The Data Wrangler
3.8 Matplotlib and Seaborn
Module 4: Mastering Machine Learning
4.1 Machine Learning: Past and Present
4.2 Scope and Types of Machine Learning
4.3 Terminologies, and Lifecycle
4.4 Regression and its Types
4.5 Classification: Logistic Regression, Support Vector Machines, and Random Forests
4.6 Clustering
4.7 Dimensionality Reduction
4.8 Metrics and Validation for ML Models
Module 5: Deep Learning
5.1 Building Blocks of Artificial Neural Networks
5.2 Deep Learning Frameworks and its Applications
5.3 CNN: Introduction, Layers, and Architecture
5.4 Understanding RNNs: Architecture, LSTM, and GRU
5.5 RNNs, LSTM, and GRU: Comparison, Selection, and Applications
Module 6: Computer Vision
6.1 Image Representation, Filtering, and Transformations
6.2 Object Detection: Overview, Process and Techniques
6.3 Region Proposal Methods
6.4 Single Shot MultiBox Detector (SSD)
6.5 Image Segmentation: Definition and Types
6.6 Architecture, Training, and Applications
Module 7: Natural Language Processing
7.1 Natural Language Processing and Text Preprocessing and Representation
7.2 Components and Challenges of Text Classification
7.3 Sentiment Analysis, Topic Modelling, and Spam Detection
7.4 Named Entity Recognition: Components, Techniques, and Applications
7.5 Identifying People, Places, Organizations, etc.
7.6 Named Entity Recognition (NER) Process
7.7 BERT and T5: Transforming Question Answering in NLP
7.8 BERT Question-Answering Systems
7.9 T5 (Text-To-Text Transfer Transformer)
Module 8: Reinforcement Learning
8.1 Introduction to Reinforcement Learning
8.2 Agents, Environments, Rewards, Actions, and States
8.3 Types of Reinforcement Learning (RL)
8.4 Applications for Reinforcement Learning
8.5 Challenges for Reinforcement Learning
8.6 RL vs. Supervised Learning: A Comparison and the Future of RL
8.7 Introduction to Q-Learning and Deep Q-Networks (DQNs)
8.8 Value-Based RL, Q-Tables, Function Approximation
8.9 Policy Gradient Method: Policy-based RL
8.10 Policy Gradient Method: REINFORCE Algorithm
8.11 Policy Gradient Method: Actor-Critic Methods
Module 9: Cloud Computing in AI Development
9.1 Cloud and AI: Transforming Innovation and Scalability
9.2 Cloud Computing in AI: Definition and Importance
9.3 Key Cloud Service Models and Deployment Models
9.4 Benefits and Popular Platforms (AWS, Azure, GCP)
9.5 Cloud-AI Integration: Scalability, Elasticity, and Case Studies of Success
9.6 Challenges and Solutions in Cloud Computing for AI Development
9.7 Use Cases and Applications of Cloud Computing in AI Development
9.8 Best Practices for Cloud-Based AI Development
9.9 Cloud-Based AI Security: Identity and Access Management (IAM)
9.10 Compliance Standards and Certifications in Cloud-Based AI
9.11 Pre-trained Models
9.12 AutoML
Module 10: Large Language Models
10.1 Key Components and Architecture
10.2 Training LLMs
10.3 Applications for LLMs
10.4 Large Language Models (LLMs) Variants
10.5 Bias and Fairness in LLMs
10.6 Privacy & security in LLMS
10.7 Requirement Resources for LLMs
10.8 Creative Text Formats and Language Translation
10.9 Multimodal Large Language Models (LLMs)
10.10 Information Retrieval and Knowledge Base Construction
Module 11: Cutting-Edge AI Research
11.1 Fundamentals and Integration of Neuro-Symbolic AI
11.2 Explainable AI (XAI)
11.3 Interpreting AI Models and Building Trust
11.4 Federated Learning: Introduction and Privacy-Preserving Collaboration
11.5 Meta-Learning
11.6 Few Shot Learning
Module 12: AI Communication and Documentation
12.1 Presenting to Technical and Non-Technical Audiences
12.2 Introduction to Documenting AI Systems
12.3 Code Documentation
12.4 Model Explanations
12.5 Bias, Fairness, Transparency, and Accountability
Course Summary
Course Summary
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