Program 1: Foundations of AI (Day 1-5)

• Introduction & Overview to Artificial Intelligence
• Origins and Progression of AI
• AI Transformations Across Industries
• Hierarchy of AI, Machine Learning, and Deep Learning

Program 2: Mathematical Foundations for AI (Day 6-10)

• Linear Algebra Basics (Vectors, Matrices)
• Matrix Operations and Applications in AI
• Probability (Basic Concepts and Distributions)
• Calculus Basics (Functions, Derivatives)
• Application of Calculus in Machine Learning Algorithms

Program 3: Core Programming Skills for AI (Day 11-16)

• Getting Started with Python (AI’s Preferred Language)
• Python Fundamentals: Data Types, Loops, and Functions
• Libraries for AI - NumPy and Pandas
• Data Visualization with Matplotlib
• Hands-on with Jupyter Notebooks and Google Colab Environment

Program 4: Data Science for AI (Day 17-22)

• Fundamentals of Data Science for AI
• Data Exploration and Analysis Techniques
• Statistical Analysis and Data Distributions
• Exploratory Data Analysis (EDA) with Python

Program 5: Data Handling and Preprocessing (Day 17-21)

• Understanding Datasets (Structured vs. Unstructured Data)
• Data Cleaning and Handling Missing Values
• Feature Scaling and Normalization
• Categorising Datasets into Training and Testing Sets

Program 6: Machine Learning Concepts and Algorithms (Day 22-26)

• Introduction to Machine Learning
• Getting Started with Machine Learning - Concepts and Types
• Supervised Learning: Classification and Regression
• Unsupervised Learning - Clustering

Program 7: Essential Algorithms (Day 27-32)

• Linear Regression Fundamentals
• Logistic Regression
• k-Nearest Neighbors Algorithm (k-NN)
• Concept of Predictions Algorithm Decision Trees & Random Forests
• Support Vector Machines Algorithm (SVM)

Program 8: Neural Networks and Deep Learning (Day 33-39)

• Introduction to Neural Networks
• Developing a Simple Neural Network in Python
• Optimizing Neural Networks with Backpropagation
• Basics of Convolutional Neural Networks

Program 9: Natural Language Processing (NLP) (Day 40-45)

• Getting Started with NLP and Text Data
• Text Pre-processing and Tokenization
• Word Embedding (Word2Vec, GloVe)
• Language Models and Text Classification
• Simple Sentiment Analysis

Program 10: Computer Vision and Image Processing (Day 46-52)

• Basics of Image Recognition and Processing
• Image Classification using Pre-trained Models
• Object Detection Techniques
• Image Segmentation
• Generative Models (GANs - Basics)

Program 11: Reinforcement Learning (Day 53-62)

• Getting Started with Reinforcement Learning Concepts
• Markov Decision Process (MDP)
• Monte Carlo Methodology
• Temporal Difference Learning
• Policy Optimization Techniques

Program 12: Tools and Libraries for AI Development (Day 63-73)

• Overview of AI Programming Languages: Python, R
• Getting Started with PyTorch and TensorFlow
• Machine Learning with Scikit-Learn Tools
• Introduction to Keras for Neural Networks
• Data Handling Libraries: NumPy, Pandas
• Working with Cloud AI Platforms (AWS, Google Cloud, Microsoft Azure)

Program 13: Ethics and Fairness in AI (Day 74-83)

• Recognizing Bias in AI Models
• Interpretable AI (XAI)
• Ethical Considerations in AI (Privacy, Data Security)

Program 14: AI in Practice (Day 84-93)

• AI Project Cycle: Problem Definition, Data Collection
• Model Training, Evaluation, and Deployment
• Discovering Case Studies based on AI applications

Program 15: Live Projects and Future Trends in AI (Day 94-110)

• Working on Live Projects
• Executing Key Ideas in a Live AI Project
• Emerging Trends and Futuristic Opportunities in AI