Course Wiki

Jupyter Notebooks

The notebook environment most Zoomcamp lessons run in — and the transition from exploratory notebooks to deployable code.

Notebooks are the default classroom across the Zoomcamps: Machine Learning Zoomcamp’s lessons are Jupyter notebooks, Stock Markets Analytics runs entirely in Colab, and Data Engineering’s workshops mix notebooks with pipeline code. They let each lesson pair explanation with runnable cells.

The courses are also explicit about the notebook’s limit: notebooks are for exploration, not production. MLOps Zoomcamp’s orchestration module turns a training notebook into a Python script before wrapping it in an orchestrator, and Stock Markets Analytics dedicates a module to moving from notebooks to deployable files with scheduling.

Taught in

• Machine Learning Zoomcamp

  • Module 1: Introduction to Machine Learning
    • Setting up the Environment
    • Introduction to NumPy
    • Linear Algebra Refresher
    • Introduction to Pandas
  • Module 2: Machine Learning for Regression
    • Data preparation
    • Exploratory data analysis
    • Setting up the validation framework
    • Linear regression
    • Linear regression: vector form
    • Training linear regression: Normal equation
    • Baseline model for car price prediction project
    • Root Mean Squared Error (RMSE)
    • Computing RMSE on validation data
    • Feature engineering
    • Categorical variables
    • Regularization
    • Tuning the model
    • Using the model
  • Module 3: Machine Learning for Classification
    • Data preparation
    • Setting up the validation framework
    • EDA
    • Feature importance: Churn rate and risk ratio
    • Feature importance: Mutual information
    • Feature importance: Correlation
    • One-hot encoding
    • Logistic regression
    • Training logistic regression with Scikit-Learn
    • Model interpretation
  • Module 4: Evaluation Metrics for Classification
    • Accuracy and dummy model
    • Confusion table
    • ROC Curves
    • ROC AUC
    • Cross-Validation
    • Summary
  • Module 6: Decision Trees and Ensemble Learning
    • Gradient boosting and XGBoost
    • XGBoost parameter tuning
  • Module 8: Neural Networks and Deep Learning
    • Installation of tensorflow
  • Module 10: Kubernetes and TensorFlow Serving
    • Creating a pre-processing service

• LLM Zoomcamp

  • Module 1: Agentic RAG
    • Environment
    • RAG
    • The Course FAQ Dataset
    • Search
    • Building the Prompt
    • The LLM
    • RAG Helper
    • Data Ingestion
    • ToyAIKit
  • Module 2: Vector Search
    • Vector Search
    • Vector Search with sqlitesearch
    • Vector Search with PGVector
    • Using ONNX Runtime instead of PyTorch
  • Module 4: Evaluation
    • Generating Ground Truth Data
    • Generating Ground Truth for All Documents
    • Search Evaluation
    • Generating RAG Answers
  • Module 5: Monitoring
    • Querying Data
  • Module 6: Best Practices
    • Hybrid Search with LangChain
  • Module 7: End-to-End Project
    • End-to-End Project Example
    • Evaluating Retrieval
    • Interface and Ingestion Pipeline

• MLOps Zoomcamp

  • Module 1: Introduction
    • (Optional) Training a ride duration prediction model
    • Homework
  • Module 2: Experiment Tracking & Model Management
    • Getting started with MLflow
    • Homework
  • Module 3: Orchestration & ML Pipelines
    • Turning the Notebook into a Python Script
    • Using an Orchestrator
  • Module 4: Model Deployment
    • MLOps Zoomcamp 4.6 - Batch scoring with Mage

• Data Engineering Zoomcamp

  • Module 3: Data Warehousing
    • Deploying Machine Learning model from BigQuery

• Stock Markets Analytics Zoomcamp

  • Module 1: Introduction and Data Sources
    • Introduction and Data Sources
  • Module 2: Working with the Data
    • Working with the Data
  • Module 3: Analytical Modeling
    • Analytical Modeling
  • Module 4: Trading Strategy and Simulation
    • Trading Strategy and Simulation
  • Module 5: Deployment and Automation
    • Deployment and Automation ## Related concepts

• Deployment Automation

• Workflow Orchestration

• Pandas

• Experiment Tracking