Machine Learning Zoomcamp: A Free 4-Month Course on ML Engineering

Master the fundamentals of machine learning, from regression and classification to deployment and deep learning.

Join #course-ml-zoomcamp Channel on Slack • Telegram Announcements • Course Playlist • FAQ • Tweet about the Course

How to Enroll

2024 Cohort

• Start Date: September 16, 2024 (17:00 Berlin time)
• Register Here: Sign up
• Stay Updated: Subscribe to our Google Calendar

Self-Paced Learning

All course materials are freely available for independent study. Follow these steps:

1. Watch the course videos and work through the code.
2. Join the Slack community (#course-ml-zoomcamp).
3. Ask questions in Slack or refer to the FAQ.
4. Complete the homework assignments (solutions provided but attempt first).
5. Work on at least one project for deeper learning.

Syllabus Overview

The course consists of structured modules covering the full ML pipeline, from fundamentals to advanced techniques.

Prerequisites

• Prior programming experience (at least 1+ year)
• Comfort with command line basics
• No prior ML knowledge required

Modules

Module 1: Introduction to Machine Learning

• ML vs Rule-Based Systems
• Supervised Learning
• CRISP-DM Framework
• Model Selection Process
• Environment Setup
• Homework

Module 2: Machine Learning for Regression

• Car Price Prediction Project
• Exploratory Data Analysis
• Linear Regression Basics
• Feature Engineering & Regularization
• Homework

Module 3: Machine Learning for Classification

• Churn Prediction Project
• Feature Selection & Encoding
• Logistic Regression
• Model Interpretation
• Homework

Module 4: Evaluation Metrics

• Accuracy, Precision, Recall
• ROC Curves & AUC
• Cross-Validation
• Homework

Module 5: Deploying ML Models

• Saving & Loading Models
• Flask API Deployment
• Docker & Virtual Environments
• Cloud Deployment (AWS)
• Homework

Module 6: Decision Trees & Ensemble Learning

• Decision Trees
• Random Forest & Gradient Boosting
• Model Selection & Hyperparameter Tuning
• Homework

Module 7: Neural Networks & Deep Learning

• TensorFlow & Keras
• Convolutional Neural Networks
• Transfer Learning
• Model Optimization & Regularization
• Homework

Module 8: Serverless Deep Learning

• Introduction to Serverless
• AWS Lambda & TensorFlow Lite
• API Gateway
• Homework

Module 9: Kubernetes & TensorFlow Serving

• TensorFlow Model Serving
• Kubernetes Basics
• Deploying ML Models to Kubernetes
• Homework

Capstone Projects

• Midterm & Final Projects integrating all learned concepts

Community & Support

Getting Help on Slack

Join the #course-ml-zoomcamp channel on DataTalks.Club Slack for discussions, troubleshooting, and networking.

To keep discussions organized:

• Follow our guidelines when posting questions.
• Review the community guidelines.

We encourage Learning in Public

Sponsors & Supporters

A special thanks to our course sponsors for making this initiative possible!

Interested in supporting our community? Reach out to alexey@datatalks.club.

About DataTalks.Club

DataTalks.Club is a global online community of data enthusiasts. It's a place to discuss data, learn, share knowledge, ask and answer questions, and support each other.

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All the activity at DataTalks.Club mainly happens on Slack. We post updates there and discuss different aspects of data, career questions, and more.

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