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

Last update: Jan 8, 2023

Overview

Zero to Mastery Machine Learning

Welcome! This repository contains all of the code, notebooks, images and other materials related to the Zero to Mastery Machine Learning Course on Udemy and zerotomastery.io.

If you'd like to see anything in particular, please send me an email: [email protected] or leave an issue.

What this course focuses on

Create a framework for working through problems (6 step machine learning modelling framework)
Find tools to fit the framework
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.

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

Comments

I got an errors when I was trying to do data preprocessing

I was trying to follow your steps to convert the categorical features in the car_sales dataframe to numbers but got some errors

This is the thread: https://github.com/scikit-learn/scikit-learn/issues/17741

opened by Blessing988 2
Update introduction-to-pandas notebook: typos and unfound bugs
Here is my changes detail to introduction-to-pandas notebook:

Typos (aniamls -> animals, car_sales_missening -> car_sales_missing)

Change call column car_sales[“Odometer”] (can not found the column) to “car_sales[“Odometer (KM)”] ”

Change path in pd.read_csv("car-sales-missing-data.csv") (not found directory) to “../data/car-sales-missing-data.csv”

Love your course ^^
opened by mtosity 1
[Deleted] Update introduction-to-pandas notebook: typos and unfound bugs
Here is my changes detail to introduction-to-pandas notebook:

Typos (animals, car_sales_missing)

Change call column “Odometer” (can not found column) to “Odometer (KM)”

Change path in pd.read_csv("car-sales-missing-data.csv") (not found) to “../data/car-sales…”

Love your course ^^
opened by mtosity 1
Add links to launch repo in Binder, Colab & Deepnote

As seen on Aurélien Geron's Hands-On ML repo (https://github.com/ageron/handson-ml2), I have added links to launch the repo in Google Colab, Binder and Deepnote

opened by jaintj95 1
Fixed the typo - xlabel and ylabel were interchanged for the bar plot in matplotlib exercise's solution

Hey, I just noticed that the labels were interchanged for a bar plot in matplotlib exercise solutions. I know it's not really a big deal, just wanted to contribute :)

opened by pavanskipo 1
fix: make use of functions parameters instead of global variables

Hi Daniel @mrdbourke

I am sending this PR as a follow up to my couple of comments on your amazing Udemy course.

As you have noticed, two functions in the course are passed parameters they never use, relying instead on global variables. This pull request help fix the issue by replacing the global variables with the proper function parameters.

I am really enjoying the course and looking for more content from you in the future.

Cheers Younes

opened by youneshenniwrites 1
fixed recall definition

Fixed recall definition in end-to-end-hearth-disease-classification. There was a typo stating that the formula is TP / (TP + FP). This should be TP / (TP + FN)

opened by Matus-Dubrava 1
Avoid adding cents in Price column

While converting Price column into Int, cents are appended and get converted into whole integer. $4000.00 -> 400000

After the suggested change, $4000.00 -> 4000

opened by SaketMunda 0
Fix Sklearn version upgrades videos/code

Some students are getting different results when running different models in Scikit-Learn.

This is because of different version upgrades (e.g. Scikit-Learn 0.23.0 -> 1.0.0).

Find the videos/code that is showing the worst results and update them with the newer versions.

opened by mrdbourke 1

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

Related tags

Overview

Zero to Mastery Machine Learning

What this course focuses on

How this course is structured

Student notes

Comments

I got an errors when I was trying to do data preprocessing

Update introduction-to-pandas notebook: typos and unfound bugs

[Deleted] Update introduction-to-pandas notebook: typos and unfound bugs

Add links to launch repo in Binder, Colab & Deepnote

Fixed the typo - xlabel and ylabel were interchanged for the bar plot in matplotlib exercise's solution

fix: make use of functions parameters instead of global variables

fixed recall definition

Avoid adding cents in Price column

Fix Sklearn version upgrades videos/code

Owner

Daniel Bourke

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

This repository contains all the code and materials distributed in the 2021 Q-Programming Summer of Qode.

Pre-trained model, code, and materials from the paper "Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation" (MICCAI 2019).

Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

Computer Vision Script to recognize first person motion, developed as final project for the course "Machine Learning and Deep Learning"

Code for the AAAI 2022 paper "Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph".

Bachelor's Thesis in Computer Science: Privacy-Preserving Federated Learning Applied to Decentralized Data

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.

This repo contains research materials released by members of the Google Brain team in Tokyo.

The materials used in the SaxonJS tutorial presented at Declarative Amsterdam, 2021

An SE(3)-invariant autoencoder for generating the periodic structure of materials

Workshop Materials Delivered on 28/02/2022

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding.

A Peer-to-peer Platform for Secure, Privacy-preserving, Decentralized Data Science

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

Related tags

Overview

Zero to Mastery Machine Learning

What this course focuses on

How this course is structured

Student notes

Comments

Owner

Daniel Bourke

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

This repository contains all the code and materials distributed in the 2021 Q-Programming Summer of Qode.

Pre-trained model, code, and materials from the paper "Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation" (MICCAI 2019).

Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

Computer Vision Script to recognize first person motion, developed as final project for the course "Machine Learning and Deep Learning"

Code for the AAAI 2022 paper "Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph".

Bachelor's Thesis in Computer Science: Privacy-Preserving Federated Learning Applied to Decentralized Data

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

This repo contains research materials released by members of the Google Brain team in Tokyo.

The materials used in the SaxonJS tutorial presented at Declarative Amsterdam, 2021

An SE(3)-invariant autoencoder for generating the periodic structure of materials

Workshop Materials Delivered on 28/02/2022

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding.

A Peer-to-peer Platform for Secure, Privacy-preserving, Decentralized Data Science

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.