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
YOLOX-RMPOLY

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Deep learning PyTorch library for time series forecasting, classification, and anomaly detection

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An implementation of the "Attention is all you need" paper without extra bells and whistles, or difficult syntax

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Single Image Random Dot Stereogram for Tensorflow

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Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth

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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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Efficient Training of Audio Transformers with Patchout

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Binary classification for arrythmia detection with ECG datasets.

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Double pendulum simulator using a symplectic Euler's method and Hamiltonian mechanics

Symplectic Double Pendulum Simulator Double pendulum simulator using a symplectic Euler's method. The program calculates the momentum and position of

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

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Simone Papicchio 4 Jul 16, 2022
OCRA (Object-Centric Recurrent Attention) source code

OCRA (Object-Centric Recurrent Attention) source code Hossein Adeli and Seoyoung Ahn Please cite this article if you find this repository useful: For

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AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition [ArXiv] [Project Page] This repository is the official implementation of AdaMML:

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[ICML 2021] Towards Understanding and Mitigating Social Biases in Language Models

Towards Understanding and Mitigating Social Biases in Language Models This repo contains code and data for evaluating and mitigating bias from generat

Paul Liang 42 Jan 03, 2023
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency

Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency This is a official implementation of the CycleContrast introduced in

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T2F: text to face generation using Deep Learning

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Animesh Karnewar 533 Dec 22, 2022
Python版OpenCVのTracking APIのサンプルです。DaSiamRPNアルゴリズムまで対応しています。

OpenCV-Object-Tracker-Sample Python版OpenCVのTracking APIのサンプルです。   Requirement opencv-contrib-python 4.5.3.56 or later Algorithm 2021/07/16時点でOpenCVには以

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NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"

[Official] FINE Samples for Learning with Noisy Labels This repository is the official implementation of "FINE Samples for Learning with Noisy Labels"

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PyTorch GPU implementation of the ES-RNN model for time series forecasting

Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm A GPU-enabled version of the hybrid ES-RNN model by Slawek et al that won the M4 time-series

Kaung 305 Jan 03, 2023
TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.

TorchMultimodal (Alpha Release) Introduction TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.

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