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

Overview

Zero to Mastery Machine Learning

Binder Deepnote Colab

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

  1. Create a framework for working through problems (6 step machine learning modelling framework)
  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/
Owner
Daniel Bourke
Machine Learning Engineer live on YouTube.
Daniel Bourke
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates

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Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

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Visual dialog agents with pre-trained vision-and-language encoders.

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Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

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Sicheng Xu 833 Dec 28, 2022
This code is the implementation of the paper "Coherence-Based Distributed Document Representation Learning for Scientific Documents".

Introduction This code is the implementation of the paper "Coherence-Based Distributed Document Representation Learning for Scientific Documents". If

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This is the solution for 2nd rank in Kaggle competition: Feedback Prize - Evaluating Student Writing.

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Mengzi Pretrained Models

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Langboat 424 Jan 04, 2023
Implementations of the algorithms in the paper Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters

Implementations of the algorithms in the paper Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters

Johannes von Lindheim 3 Oct 29, 2022
GPU implementation of $k$-Nearest Neighbors and Shared-Nearest Neighbors

GPU implementation of kNN and SNN GPU implementation of $k$-Nearest Neighbors and Shared-Nearest Neighbors Supported by numba cuda and faiss library E

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Checking fibonacci - Generating the Fibonacci sequence is a classic recursive problem

Fibonaaci Series Generating the Fibonacci sequence is a classic recursive proble

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This repository is for DSA and CP scripts for reference.

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Official implementation of "MetaSDF: Meta-learning Signed Distance Functions"

MetaSDF: Meta-learning Signed Distance Functions Project Page | Paper | Data Vincent Sitzmann*, Eric Ryan Chan*, Richard Tucker, Noah Snavely Gordon W

Vincent Sitzmann 100 Jan 01, 2023
This repository contains demos I made with the Transformers library by HuggingFace.

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This repository provides code for "On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness".

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Code for the paper "SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness" (NeurIPS 2021)

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Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition (NeurIPS 2019)

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Edson-Niu 60 Nov 29, 2022
This project aims to explore the deployment of Swin-Transformer based on TensorRT, including the test results of FP16 and INT8.

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