Training on Artificial Intelligence & Machine Learning Engineering

Module 1: Introduction to Artificial Intelligence & Machine Learning

• Overview of AI and ML concepts

• Types of AI: narrow, general, and super AI

• Types of ML: supervised, unsupervised, reinforcement learning

• Key AI/ML applications in business and industry

• Case Study: AI in predictive maintenance for manufacturing

Module 2: Python Programming for AI & ML

• Python fundamentals for AI/ML

• Libraries: NumPy, Pandas, Matplotlib, Scikit-learn

• Data structures, data manipulation, and visualization

• Practical : Data exploration and preprocessing

• Case Study: Using Python to analyze retail sales data

Module 3: Data Preparation & Feature Engineering

• Understanding and cleaning datasets

• Feature selection and dimensionality reduction

• Handling missing values and outliers

• Scaling and normalization of features

• Practical : Preparing data for ML models

• Case Study: Feature engineering for customer churn prediction

Module 4: Supervised Learning Algorithms

• Linear regression, logistic regression, decision trees

• Support vector machines, k-nearest neighbors

• Model training, testing, and evaluation metrics

• Practical : Building predictive models

• Case Study: Predicting loan default risks in banking

Module 5: Unsupervised Learning & Clustering

• Clustering techniques: K-Means, Hierarchical, DBSCAN

• Dimensionality reduction: PCA, t-SNE

• Anomaly detection

• Practical Lab: Implementing clustering models

• Case Study: Customer segmentation for targeted marketing

Module 6: Advanced Machine Learning Techniques

• Ensemble methods: Random Forest, Gradient Boosting, XGBoost

• Model tuning and hyperparameter optimization

• Cross-validation techniques

• Practical : Improving model accuracy and robustness

• Case Study: Predictive analytics in supply chain management

Module 7: Introduction to Deep Learning

• Neural networks and deep learning fundamentals

• Activation functions, loss functions, and optimization

• Convolutional Neural Networks (CNN) for image analysis

• Recurrent Neural Networks (RNN) for time series and sequences

• Practical : Training a neural network

• Case Study: AI for image classification in healthcare diagnostics

Module 8: Natural Language Processing (NLP) & AI Applications

• Text preprocessing and tokenization

• Sentiment analysis, language modeling, and chatbots

• Introduction to Transformer models (BERT, GPT)

• Practical Lab: Building a text classifier or chatbot

• Case Study: NLP for customer feedback analysis

Module 9: Model Deployment & AI System Integration

• Exporting and deploying ML models

• Introduction to cloud AI platforms (AWS, Azure, GCP)

• API integration and building AI applications

• Model monitoring and maintenance

• Practical : Deploying a machine learning model

• Case Study: AI-based recommendation system in e-commerce

Module 10: AI Strategy, Ethics & Capstone Project

• AI strategy for organizations

• Ethical AI, fairness, transparency, and regulatory considerations

• Emerging trends in AI/ML engineering

• Capstone Project: Build and present a complete AI/ML solution

• Case Study: End-to-end predictive analytics project in finance or healthcare