Course Wiki

Module 1: Introduction to Machine Learning — Machine Learning Zoomcamp

Lessons, materials, and key concepts for module 1 of Machine Learning Zoomcamp.

Machine Learning Zoomcamp > Module 1: Introduction to Machine Learning

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 The Modelling Step (Model Selection Process)
• 1.6 Setting up the Environment
• 1.7 Introduction to NumPy
• 1.8 Linear Algebra Refresher
• 1.9 Introduction to Pandas
• 1.10 Summary
• 1.11 Homework

Community notes

Did you take notes? You can share them here (or in each unit separately)

• Notes by Ayoub Berdeddouch

Lessons

Key concepts

• CRISP-DM
• Model Deployment
• LLM Evaluation
• Neural Networks
• MCP
• Logistic Regression
• Decision Trees
• Gradient Boosting
• Docker
• TensorFlow
• Pandas
• scikit-learn
• Jupyter Notebooks

Homework and deadlines live in the course repository: machine-learning-zoomcamp.

Other modules in this course

• Module 2: Machine Learning for Regression
• Module 3: Machine Learning for Classification
• Module 4: Evaluation Metrics for Classification
• Module 5: Deploying Machine Learning Models
• Module 6: Decision Trees and Ensemble Learning
• Module 8: Neural Networks and Deep Learning
• Module 9: Serverless Deep Learning
• Module 10: Kubernetes and TensorFlow Serving