Artificial Intelligence Course in Noida

Module 1: Artificial Intelligence (AI) (Day 1-5)

• Introduction to Artificial Intelligence
• History and Evolution of AI
• AI in Various Industries
• Relation between AI, Machine Learning, and Deep Learning
• Concepts of Reactive Machines and Limited Memory
• Overview of Theory of Mind and Self-aware AI

Module 2: Basic Programming for AI (Day 6-11)

• Introduction to Python (AI’s Preferred Language)
• Python Fundamentals: Data Types, Loops, and Functions
• Libraries for AI - NumPy and Pandas
• Data Visualization with Matplotlib
• Working with Jupyter Notebooks and Google Colab

Module 3: Mathematical Foundations for AI (Day 12-16)

• 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

Module 4: Introduction to Machine Learning (ML) (Day 17-25)

• Introduction to Machine Learning - Concepts and Types
• Supervised Learning: Classification and Regression
• Unsupervised Learning - Clustering
• Linear Regression Fundamentals
• Logistic Regression
• K-Nearest Neighbors (k-NN) Algorithm
• Concept of Decision Trees & Random Forests
• Support Vector Machines (SVM)

Module 5: Data Handling and Preprocessing (Day 26-31)

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

Module 6: Neural Networks and Deep Learning (Day 32-37)

• Introduction to Neural Networks- Perceptron and Activation Functions
• Building a Simple Neural Network in Python
• Backpropagation and Training a Neural Network
• Introduction to Convolutional Neural Networks

Module 7: Natural Language Processing (NLP) (Day 38-43)

• Introduction to NLP and Text Data
• Text Pre-processing and Tokenization
• Word Embeddings (Word2Vec, GloVe)
• Language Models and Text Classification
• Simple Sentiment Analysis

Module 8: Computer Vision and Image Processing (Day 44-50)

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

Module 9: Reinforcement Learning (Day 50-60)

• Introduction to Reinforcement Learning Concepts
• Markov Decision Process (MDP)
• Monte Carlo Methodology
• Temporal Difference Learning
• Policy Optimization Techniques

Module 10: AI Tools and Libraries (Day 61-75)

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

Module 11: Ethics and Fairness in AI (Day 76-86)

• Understanding Bias in AI Models
• Explainable AI (XAI)
• Ethical Concerns in AI (Privacy, Data Security)

Module 12: AI in Practice (Day 87-96)

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

Module 13: Working on Live Projects (Day 97-110)

• Applying Key Concepts in a Live AI Project
• Emerging Future Trends in AI - Reinforcement Learning, AI in Robotics, Future OpportModuleies