AI Application Development using Python

Module 1: Python Foundations for AI

• Variables, data types, control structures, functions
• Lists, dictionaries, OOP concepts
• NumPy arrays, Pandas DataFrames basics

Module 2: Data Preprocessing and EDA

• Data cleaning, handling missing values, outliers
• Feature scaling, encoding categorical variables
• Exploratory Data Analysis with visualizations

Module 3: Machine Learning Fundamentals

• Supervised learning (regression, classification)
• Unsupervised learning (clustering, dimensionality reduction)
• Model evaluation metrics, cross-validation

Module 4: Scikit-learn for Practical ML

• Pipeline creation, GridSearchCV hyperparameter tuning
• Ensemble methods (Random Forest, XGBoost)
• Feature selection techniques

Module 5: Neural Networks with TensorFlow/Keras

• Sequential vs Functional API models
• Optimizers, loss functions, callbacks
• Overfitting prevention (Dropout, EarlyStopping)

Module 6: Computer Vision Applications

• CNN architectures (VGG, ResNet)
• Image preprocessing, augmentation
• Object detection with YOLO basics

Module 7: Natural Language Processing (NLP)

• Tokenization, stemming, lemmatization
• Word embeddings (Word2Vec, GloVe)
• Transformers introduction (BERT basics)

Module 8: Web Frameworks for AI Deployment

• Flask/FastAPI for ML model serving
• REST API creation with Pydantic validation
• CORS, authentication for AI endpoints

Module 9: Recommendation Systems

• Collaborative filtering, content-based filtering
• Matrix factorization techniques
• Hybrid recommendation engines

Module 10: Time Series Forecasting with AI

• ARIMA vs LSTM models
• Prophet library for business forecasting
• Multivariate time series analysis

Module 11: Generative AI Applications

• GANs basics for image generation
• Text generation with GPT-like models
• Diffusion models introduction

Module 12: MLOps and Production Deployment

• Docker containerization of AI apps
• Model versioning with MLflow
• CI/CD pipelines for ML models

Module 13: AI Ethics and Explainability

• Bias detection and mitigation strategies
• SHAP, LIME for model interpretability
• Fairness metrics and responsible AI

Module 14: Cloud AI Deployment

• AWS SageMaker, Google AI Platform
• Serverless deployment (AWS Lambda)
• Model monitoring and retraining

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