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The code and notes for the Machine Learning ZoomCamp.
ml_zoomcamp's Introduction
Machine Learning Zoomcamp (by DataTalks.Club - alexeygrigorev)
1. Introduction to Machine Learning
- 1.1 Introduction to Machine Learning ✔
- 1.2 ML vs Rule-Based Systems ✔
- 1.3 Supervised Machine Learning ✔
- 1.4 CRISP-DM ✔
- 1.5 Model Selection Process ✔
- 1.6 Setting up the Environment ✔
- 1.7 Introduction to NumPy ✔
- 1.8 Linear Algebra Refresher ✔
- 1.9 Introduction to Pandas ✔
- Summary ✔
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 ✔
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 ✔
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 ✔
5. Deploying Machine Learning Models
- 5.1 Intro / Session overview (Fri, 10 Feb 2023)
- 5.2 Saving and loading the model (Fri, 10 Feb 2023)
- 5.3 Web services : Flask (Fri, 10 Feb 2023)
- 5.4 Serving the churn model with Flask (Fri, 10 Feb 2023)
- 5.5 Python virtual environment: Pipenv (Fri, 10 Feb 2023)
- 5.6 Environment management: Docker (Fri, 10 Feb 2023)
- 5.7 Deployment to the cloud: AWS Elastic Beanstalk (optional) (Fri, 10 Feb 2023)
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 tuning
- 6.6 Ensemble learning and random forest
- 6.7 Gradient boosting and XGBoost
- 6.8 XGBoost parameter tuning
- 6.9 Selecting the best model
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
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
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
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
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