eeeds/ML-Camp

Jupyter Notebook

Machine Learning Zoomcamp

This is the repository for the Machine Learning Zoomcamp course.

Original repository

1. Introduction to Machine Learning

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
2.18 Homework

3. Machine Learning for Classification

3.1 Churn 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
3.15 Homework

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
4.10 Homework

5. Deploying Machine Learning Models

5.1 Intro / 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
5.10 Homework

5b. Bento ML

More information coming soon!

6. Decision Trees and Ensemble Learning

8. Midterm Project

Putting everything we've learned so far in practice!

8. Neural Networks and Deep Learning

8.1 Fashion classification
8.2 TensorFlow and Keras
8.3 Pre-trained convolutional neural networks
8.4 Convolutional 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
8.15 Homework

9. Serverless Deep Learning

9.1 Introduction to Serverless
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
9.10 Homework

10. Kubernetes and TensorFlow Serving

10.1 Overview
10.2 TensorFlow Serving
10.3 Creating a pre-processing 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
10.11 Homework

11. KServe

11.1 Overview
11.2 Running KServe locally
11.3 Deploying a Scikit-Learn model with KServe
11.4 Deploying custom Scikit-Learn images with KServe
11.5 Serving TensorFlow models with KServe
11.6 KServe transformers
11.7 Deploying with KServe and EKS
11.8 Summary
11.9 Explore more

12. Capstone Project

Putting everything we've learned so far in practice one more time!

13. Article

Writing an article about something not covered in the course.

14. Third project (optional)