HediaTnani/ml-engineering

The repository contains all the work including projects, notes, and articles related to ML Engineering while I am learning.

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

5b. BentoML

Information to be added!

6. Decision Trees and Ensemble Learning

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7. Midterm Project

Information to be added!

8. Neural Networks and Deep Learning

Information to be added!

9. Severless Deep Learning

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10. Kubernetes and TensorFlow Serving

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11. KServe

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12. Capstone Project

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14. Second Capstone Project (optional)

Information to be added!

Homework

• HW1
• HW2
• HW3
• HW4