Artificial Intelligence Syllabus

Duration:3.5 Months | Fee: ₹30000/-

1. Introduction to Artificial Intelligence

• • AI Introduction & History
• • AI Applications
• • Introduction to AI, ML, and DL
• what is Agent, types of Agents

2. Python Programming Fundamentals

• Python Important Features
• Python Installation
• Python Basics
• Variables, Data Types & Operators
• Control Statements in Python
• Simple if, if-else, elif Statements in Python
• Functions in Python
• Packages & Modules in Python

3. Python Data Structures

• List Data Structures
• String Data Structure
• Sets and Tuples
• Dictionaries
• Files Concept in Python

4. Artificial Intelligence: Mathematical Foundations

• AI Mathematical Foundations
• AI: Probability, Statistics & Linear Algebra
• AI: Vectors, Scalars, and Matrices Representation
• AI: Differential & Integral Calculus
• Linear Regression & Implementation

5. Data Understanding and Big Data

• AI Data Understanding
• AI Big Data
• Data Collection from Different Sources, Data Cleaning

6. AI: Vision & Classification

• AI Vision: Classification & Retrieval
• AI: Convolutional Neural Networks (CNN)
• CNN & Generative Adversarial Networks (GAN)
• Five Layers of Sequencer

7.Neural Networks

• AI: Introduction to Neural Networks
• AI: Neural Networks in Practice: Optimization
• Recurrent Neural Networks (RNN)
• RNN Sequence Modelling
• About RNNs

8.Reinforcement Learning

• Introduction to Reinforcement Learning
• AI Problem Solving
• Constraint Satisfaction Problems
• Uninformed Search
• Informed Search Algorithms
• A* Search
• Uninformed Search
• Cryptarithm Problems
• Uniform Cost Search & Iterative Depth First Search
• Puzzle Solving Using A* Search

9.Data Analysis & Visualization in Python

• Introduction to NumPy for Numerical Computing
• Introduction to Pandas for Data Manipulation and Analysis
• Data Transformation & Visualization
• Introduction to Matplotlib for Visualization

9.Machine Learning with Scikit-Learn

• Overview of Scikit Learn Library for Machine Learning in Python
• Supervised Learning: Classification & Regression
• Unsupervised Learning: Clustering

10. Real-Time Tasks & Projects

• Implement Tic-Tac-Toe game using python
• Implement 8-Puzzle problem using python
• Implement Water-jug problem using python
• Respiratory Disease Classification
• Diabetic Retinopathy Detection
• Kidney Tumor Detection and Classification
• Crop Disease Prediction
• Traffic Sign Recognition

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