Description

Module 1: Introduction to Artificial Intelligence

• Definition and history of AI

• Difference between AI, Machine Learning, and Deep Learning

• Applications and real-world use cases of AI

• AI branches: Reactive systems, limited memory, theory of mind, and self-aware AI

Module 2: Python for AI

• Python fundamentals

• Working with data structures

• Functions, loops, and file operations

• Libraries for AI: NumPy, Pandas, Matplotlib

Module 3: Mathematics for AI

• Linear Algebra (vectors, matrices, operations)

• Probability and Statistics

• Calculus basics (derivatives and gradients)

• Optimization concepts (gradient descent)

Module 4: Machine Learning Basics

• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

• Common algorithms: Linear Regression, Decision Trees, KNN, SVM

• Model training, evaluation, and tuning

Module 5: Deep Learning

• Introduction to Neural Networks

• Architecture: neurons, layers, activation functions

• Forward and backward propagation

• Introduction to TensorFlow and Keras

• Building and training a simple neural network

Module 6: Natural Language Processing (NLP)

• Text preprocessing techniques (tokenization, stemming, stop words)

• Bag of Words and TF-IDF

• Sentiment analysis and text classification

• Introduction to transformers and large language models (LLMs)

Module 7: Computer Vision

• Image processing basics

• Convolutional Neural Networks (CNNs)

• Image classification and object detection

• Introduction to OpenCV

Module 8: Reinforcement Learning (Optional/Advanced)

• Understanding agents, environment, actions, and rewards

• Q-Learning and Policy Gradients

• Simple RL simulations using OpenAI Gym

Module 9: Ethics and Safety in AI

• AI bias and fairness

• Explainable AI

• Responsible AI development

• Future of AI and societal impact

Module 10: Real-World AI Projects

• Chatbot using NLP

• Handwritten digit recognition

• AI for medical image analysis

• Sentiment analysis of product reviews

• AI-based recommendation system

Tools and Technologies

• Python

• NumPy, Pandas, Matplotlib, Seaborn

• Scikit-learn

• TensorFlow / Keras

• OpenCV

• NLTK / SpaCy / Hugging Face Transformers

• Jupyter Notebook / Google Colab

Requirements