Machine Learning Bootcamp

Exploratory Data Analysis (EDA)

• Importance of EDA
• Data Cleaning and Preprocessing: Handling missing values, data transformation, and scaling
• Data Visualization: Using Matplotlib and Seaborn to visualize distributions and relationships

Introduction to Supervised Learning

• Supervised Learning Concepts: Difference between supervised and unsupervised learning, regression vs. classification
• Linear Regression: Building and evaluating linear regression models with NumPy and scikit-learn

Classification Techniques

• Logistic Regression: Building and evaluating logistic regression models with scikit-learn, model evaluation metrics
• k-Nearest Neighbors (k-NN): Implementing k-NN with scikit-learn

Practical Applications

• Case Studies: Real-world applications of regression and classification
• Hands-on Projects: Building and evaluating regression and classification models on provided datasets

Decision Trees and Ensemble Methods

• Decision Trees: Building and visualizing decision trees, overfitting and pruning
• Ensemble Methods: Random Forests, Gradient Boosting Machines

Model Evaluation and Optimization

• Model Evaluation Techniques: Cross-validation, hyperparameter tuning using GridSearchCV and RandomizedSearchCV
• Feature Engineering: Techniques for feature selection and creation

Neural Networks with TensorFlow

• Introduction to Neural Networks: Understanding neural network architecture, activation functions, and loss functions
• Building Neural Networks with TensorFlow: Implementing a neural network for multi-class classification

Advanced Neural Networks

• Deep Learning Techniques: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs)
• Practical Applications: Hands-on project using TensorFlow for image or text classification

Unsupervised Learning Techniques

• Introduction to Unsupervised Learning: Applications of unsupervised learning
• Clustering Techniques: k-Means Clustering, Hierarchical Clustering, evaluating clustering results

Anomaly Detection

• Techniques for Anomaly Detection: Isolation Forest, One-Class SVM, practical implementation with Python

Recommender Systems

• Introduction to Recommender Systems: Types of recommender systems
• Collaborative Filtering: User-based and item-based collaborative filtering, implementing collaborative filtering in Python

Advanced Recommender Systems

• Content-Based Recommenders: Building content-based recommenders
• Deep Learning for Recommenders: Implementing deep learning-based recommenders using TensorFlow, case studies and hands-on projects

Introduction to MLOps

• Overview of MLOps: Key components and workflow
• Setting Up the MLOps Environment: Tools and platforms (e.g., Docker, Kubernetes), environment setup and configuration

Continuous Integration and Continuous Deployment (CI/CD) for ML

• CI/CD Concepts for ML: Differences between traditional CI/CD and CI/CD for ML
• Building CI/CD Pipelines: Implementing CI/CD pipelines with tools like Jenkins, GitLab CI, or GitHub Actions, automating testing, training, and deployment of ML models

Model Monitoring and Maintenance

• Monitoring ML Models in Production: Techniques for monitoring and logging
• Model Maintenance and Management: Handling model drift and retraining, versioning models with tools like MLflow

Best Practices and Capstone Project

• Best Practices in MLOps: Ensuring reproducibility and traceability, security and compliance, ethical considerations
• Capstone Project: Comprehensive project incorporating MLOps practices, deploying a model, setting up a CI/CD pipeline, monitoring model performance, and handling model updates and maintenance