Module 1:
■ Introduction to Artificial Intelligence
■ History and Evolution of AI
■ Types of AI:
Narrow AI, General AI, and Super AI
■ Real-World Applications of AI
Module 2:
■ Understanding Machine Learning
■ Basics of Machine Learning
Supervised, Unsupervised, and Reinforcement Learning
■ Common Algorithms (Linear Regression, Decision Trees, KNN)
■ Introduction to Deep Learning
Module 3:
■ Python for AI Development
■ Setting Up Python for AI
■ Introduction to Key Libraries: NumPy, Pandas, Matplotlib, Scikit-learn
■ Data Preprocessing and Feature Engineering
Module 4:
■ Building Machine Learning Models
■ Data Splitting (Train-Test Split)
Training and Evaluating Models
■ Hyperparameter Tuning
Cross-Validation
Module 5:
■ Introduction to Deep Learning
■ Understanding Neural Networks
■ Activation Functions and Backpropagation
■ Building a Neural Network with TensorFlow/Keras
Module 6:
■ Natural Language Processing (NLP)
■ Introduction to NLP
Text Preprocessing (Tokenization, Lemmatization)
■ Sentiment Analysis
■ Building a Basic NLP Model with Python
Module 7:
■ Computer Vision with AI
■ Introduction to Computer Vision
■ Image Preprocessing Techniques
■ Building an Image Classification Model
Module 8:
■ AI Project Development
■ Project Planning and Dataset Selection
■ Building and Training an AI Model
■ Evaluating and Improving the Model
■ Deploying the AI Model