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All course materials for the Zero to Mastery Machine Learning and Data Science course.

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# Zero to Mastery Machine Learning
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Welcome! This repository contains all of the code, notebooks, images and other materials related to the [Zero to Mastery Machine Learning Course on Udemy](https://dbourke.link/mlcourse) and [zerotomastery.io](https://dbourke.link/ZTMmlcourse).

## Quick links

* 🎥 Watch the [first 10 hours of the course on YouTube](https://youtu.be/r67SfaiYaDI).
* 📚 Read the materials of the course in a [beautiful online book](https://dev.mrdbourke.com/zero-to-mastery-ml/).
* 🤔 Found something wrong with the code? Leave an [issue](https://github.com/mrdbourke/zero-to-mastery-ml/issues).
* ❓ Got a question? [Post a discussion](https://github.com/mrdbourke/zero-to-mastery-ml/discussions) (see the [question template](https://github.com/mrdbourke/zero-to-mastery-ml/discussions/48)). 

## Updates

* **30 October 2024** - Add course book version of [Milestone Project 2: Bulldozer Price Regression](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-bluebook-bulldozer-price-regression-v2/) (updated for 2025 onwards)
* **12 September 2024** - Working on updating the materials for 2025, see progress in [#105](https://github.com/mrdbourke/zero-to-mastery-ml/discussions/105)
* **12 October 2023** - Created an online book version of the course materials, see: https://dev.mrdbourke.com/zero-to-mastery-ml/ 

## Contents

The following contents are listed in suggested chronological order.

But feel free to mix in match in anyway you feel fit.

> **Note:** All of the datasets we use in the course are available in the [`data/`](https://github.com/mrdbourke/zero-to-mastery-ml/tree/master/data) folder.

| **Section** | **Resource** | **Description** |
|-----|-----|-----| 
| 00 | [A 6 step framework for approaching machine learning projects](https://dev.mrdbourke.com/zero-to-mastery-ml/a-6-step-framework-for-approaching-machine-learning-projects/) | A guideline for different kinds of machine learning projects and how to break them down into smaller steps. |
| 01 | [Introduction to NumPy](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-numpy/) | NumPy stands for Numerical Python. It's one of the most used Python libraries for numerical processing (which is what much of data science and machine learning is). | 
| 02 | [Introduction to pandas](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-pandas/) | pandas is a Python library for manipulating and analysing data. You can imagine pandas as a programmatic form of an Excel spreadsheet. |
| 03 | [Introduction to Matplotlib](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-matplotlib/) | Matplotlib helps to visualize data. You can create plots and graphs programmatically based on various data sources. |
| 04 | [Introduction to Scikit-Learn](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-scikit-learn/) | Scikit-Learn or sklearn is full of data processing techniques as well as pre-built machine learning algorithms for many different tasks. |
| 05 | [Milestone Project 1: End-to-end Heart Disease Classification](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-heart-disease-classification/) | Here we'll put together everything we've gone through in the previous sections to create a machine learning model that is capable of classifying if someone has heart disease or not based on their health characteristics. We'll start with a raw dataset and work through performing an exploratory data analysis (EDA) on it before trying out several different machine learning models to see which performs best. | 
| 06 | [Milestone Project 2: End-to-end Bulldozer Price Prediction](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-bluebook-bulldozer-price-regression-v2/) | In this project we'll work with an open-source dataset of bulldozer sales information. We'll use this data to build a machine learning model capable of predicting the sales price of a bulldozer based on several input parameters such as size and brand. Since this dataset isn't perfect, we'll work through several data preprocessing steps before building a model. And since we'll be working towards predicting a number (price of bulldozers), this project is known as regression project. | 
| 07 | [Milestone Project 3: Introduction to TensorFlow/Keras and Deep Learning](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-dog-vision-v2/) | TensorFlow/Keras are deep learning frameworks written in Python. Originally created by Google and are now open-source. These frameworks allow you to build and train neural networks, one of the most powerful kinds of machine learning models. In this section we'll learn about deep learning and TensorFlow/Keras by building Dog Vision 🐶👁️, a neural network to identify dog breeds in images. |
| 08 | [Communicating your work](https://dev.mrdbourke.com/zero-to-mastery-ml/communicating-your-work/) | One of the most important parts of machine learning and any software project is communicating what you've found/done. This module takes the learnings from the previous sections and gives tips and tricks on how you can communicate your work to others. |

## What this course focuses on

1. Create a framework for working through problems ([6 step machine learning modelling framework](https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-1-getting-ready-for-machine-learning/a-6-step-framework-for-approaching-machine-learning-projects.md))
2. Find tools to fit the framework
3. Targeted practice = use tools and framework steps to work on end-to-end machine learning modelling projects 

## How this course is structured 

* Section 1 - Getting your mind and computer ready for machine learning (concepts, computer setup)
* Section 2 - Tools for machine learning and data science (pandas, NumPy, Matplotlib, Scikit-Learn)
* Section 3 - End-to-end structured data projects (classification and regression)
* Section 4 - Neural networks, deep learning and transfer learning with TensorFlow 2.0
* Section 5 - Communicating and sharing your work

## Student notes

Some students have taken and shared extensive notes on this course, see them below.

If you'd like to submit yours, leave a pull request.

1. Chester's notes - https://github.com/chesterheng/machinelearning-datascience
2. Sophia's notes - https://www.rockyourcode.com/tags/udemy-complete-machine-learning-and-data-science-zero-to-mastery/