MACHINE LEARNING ENGINEERING

This repository contains all the coursework I am going through from Machine Learning Zoomcamp, which includes projects, notebooks, notes, and homework.

📖 CONTENT

1. Introduction to Machine Learning

• 1.1 What is Machine Learning
• 1.2 Machine Learning vs Rule-Based System
• 1.3 Supervised Machine Learning
• 1.4 CRISP-DM
• 1.5 The Model Selection Process
• 1.6 Setting Up the Environment
• 1.7 Introduction to NumPy
• 1.8 Linear Algebra Refresher
• 1.9 Introduction to Pandas

2. Machine Learning for Regression

• 2.1 Car Price Prediction Project
• 2.2 Data Preparation
• 2.3 Exploratory Data Analysis
• 2.4 Setting Up the Validation Framework
• 2.5 Linear Regression
• 2.6 Linear Regression: Vector Form
• 2.7 Training Linear Regression: Normal Equation
• 2.8 Baseline Model for Car Price Prediction Project
• 2.9 Root Mean Squared Error
• 2.10 Using RMSE on Validation Data
• 2.11 Feature Engineering
• 2.12 Categorical Variables
• 2.13 Regularization
• 2.14 Tuning the Model
• 2.15 Using the Model
• 2.16 Car Price Prediction Project Summary
• 2.17 Explore More

3. Machine Learning for Classification

• 3.1 Chrun Prediction Project
• 3.2 Data Preparation
• 3.3 Setting Up the Validation Framework
• 3.4 EDA
• 3.5 Feature Importance: Churn Rate and Risk Ratio
• 3.6 Feature Importance: Mutual Information
• 3.7 Feature Importance: Correlation
• 3.8 One-Hot Encoding
• 3.9 Logistic Regression
• 3.10 Training Logistic Regression with Scikit-Learn
• 3.11 Model Interpretation
• 3.12 Using the Model
• 3.13 Summary
• 3.14 Explore More

4. Evaluation Metrics for Classification

• 4.1 Evaluation Metrics: Session Overview
• 4.2 Accuracy and Dummy Model
• 4.3 Confusion Table
• 4.4 Precision and Recall
• 4.5 ROC Curves
• 4.6 ROC AUC
• 4.7 Cross-Validation
• 4.8 Summary
• 4.9 Explore More

5. Deploying Machine Learning Models

• 5.1 Session Overview
• 5.2 Saving and Loading the Model
• 5.3 Web Services: Introduction to Flask
• 5.4 Serving the Churn Model with Flask
• 5.5 Python Virtual Environment: Pipenv
• 5.6 Environment Management: Docker
• 5.7 Deployment to the Cloud: AWS Elastic Beanstalk (optional)
• 5.8 Summary
• 5.9 Explore More
• Deployment Tutorials

6. Decision Trees and Ensemble Learning

• 6.1 Credit Risk Scoring Project
• 6.2 Data Cleaning and Preparation
• 6.3 Decision Trees
• 6.4 Decision Tree Learning Algorithm
• 6.5 Decision Trees Parameter Turning
• 6.6 Ensemble Learning and Random Forest
• 6.7 Gradient Boosting and XGBoost
• 6.8 XGBoost Parameter Tuning
• 6.9 Selecting the Best Model
• 6.10 Summary
• 6.11 Explore More

7. Production-Ready Machine Learning (Bento ML)

• 7.1 Intro/Session Overview
• 7.2 Building Your Prediction Service with BentoML
• 7.3 Deploying Your Prediction Service
• 7.4 Sending, Receiving and Validating Data
• 7.5 High-Performance Serving
• 7.6 Bento Production Deployment
• 7.7 (Optional) Advanced Example: Deploying Stable Diffusion Model
• 7.8 Summary

8. Neural Networks and Deep Learning

• 8.1 Fashion Classification
• 8.1b Setting Up the Environment on Saturn Cloud
• 8.2 TensorFlow and Keras
• 8.3 Pre-Trained Convolutional Neural Networks
• 8.4 Convolution Neural Networks
• 8.5 Transfer Learning
• 8.6 Adjusting the Learning Rate
• 8.7 Checkpointing
• 8.8 Adding More Layers
• 8.9 Regularization and Dropout
• 8.10 Data Augmentation
• 8.11 Training a Larger Model
• 8.12 Using the Model
• 8.13 Summary
• 8.14 Explore More

9. Severless Deep Learning

• 9.1 Introduction to Severless
• 9.2 AWS Lambda
• 9.3 TensorFlow Lite
• 9.4 Preparing the Code for Lambda
• 9.5 Preparing a Docker Image
• 9.6 Creating the Lambda Function
• 9.7 API Gateway: Exposing the Lambda Function
• 9.8 Summary
• 9.9 Explore More

10. Kubernetes and TensorFlow Serving

• 10.1 Overview
• 10.2 TensorFlow Serving
• 10.3 Creating a Preprocessing Service
• 10.4 Running Everything Locally with Docker Compose
• 10.5 Introduction to Kubernetes
• 10.6 Deploying a Simple Service to Kubernetes
• 10.7 Deploying TensorFlow Models to Kubernetes
• 10.8 Deploying to EKS
• 10.9 Summary
• 10.10 Explore More
• Commands References

11. KServe

Information to be added!

Homework

• HW1
• HW2
• HW3
• HW4
• HW5
• HW6
• HW7
• HW8
• HW9
• HW10