A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

Overview

Machine Learning Mindmap / Cheatsheet

A Mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

Overview

Machine Learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data.

Machine Learning is as fascinating as it is broad in scope. It spans over multiple fields in Mathematics, Computer Science, and Neuroscience. This is an attempt to summarize this enormous field in one .PDF file.

Download

Download the PDF here:

https://github.com/dformoso/machine-learning-mindmap/blob/master/Machine%20Learning.pdf

Same, but with a white background:

https://github.com/dformoso/machine-learning-mindmap/blob/master/Machine%20Learning%20-%20White%20BG.pdf

I've built the mindmap with MindNode for Mac. https://mindnode.com

Companion Notebook

This Mindmap/Cheatsheet has a companion Jupyter Notebook that runs through most of the Data Science steps that can be found at the following link:

https://github.com/dformoso/sklearn-classification

Mindmap on Deep Learning

Here's another mindmap which focuses only on Deep Learning

https://github.com/dformoso/deeplearning-mindmap

1. Process

The Data Science it's not a set-and-forget effort, but a process that requires design, implementation and maintenance. The PDF contains a quick overview of what's involved. Here's a quick screenshot.

alt text

2. Data Processing

First, we'll need some data. We must find it, collect it, clean it, and about 5 other steps. Here's a sample of what's required.

alt text

3. Mathematics

Machine Learning is a house built on Math bricks. Browse through the most common components, and send your feedback if you see something missing.

alt text

4. Concepts

A partial list of the types, categories, approaches, libraries, and methodology.

alt text

5. Models

A sampling of the most popular models. Send your comments to add more.

alt text

References

I'm planning to build a more complete list of references in the future. For now, these are some of the sources I've used to create this Mindmap.

 Stanford and Oxford Lectures. CS20SI, CS224d.
> Books: 
  > Deep Learning - Goodfellow. 
  > Pattern Recognition and Machine Learning - Bishop. 
  > The Elements of Statistical Learning - Hastie.
- Colah's Blog. http://colah.github.io
- Kaggle Notebooks.
- Tensorflow Documentation pages.
- Google Cloud Data Engineer certification materials.
- Multiple Wikipedia articles.

About Me

Twitter:

https://twitter.com/danielmartinezf

Linkedin:

https://www.linkedin.com/in/danielmartinezformoso/

Email:

[email protected]

Owner
Daniel Formoso
Machine Learning Cloud Consultant at Google
Daniel Formoso
Titanic Traveller Survivability Prediction

The aim of the mini project is predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked and more.

John Phillip 0 Jan 20, 2022
Implementation of the Object Relation Transformer for Image Captioning

Object Relation Transformer This is a PyTorch implementation of the Object Relation Transformer published in NeurIPS 2019. You can find the paper here

Yahoo 158 Dec 24, 2022
PLUR is a collection of source code datasets suitable for graph-based machine learning.

PLUR (Programming-Language Understanding and Repair) is a collection of source code datasets suitable for graph-based machine learning. We provide scripts for downloading, processing, and loading the

Google Research 76 Nov 25, 2022
QML: A Python Toolkit for Quantum Machine Learning

QML is a Python2/3-compatible toolkit for representation learning of properties of molecules and solids.

176 Dec 09, 2022
Data Efficient Decision Making

Data Efficient Decision Making

Microsoft 197 Jan 06, 2023
CD) in machine learning projectsImplementing continuous integration & delivery (CI/CD) in machine learning projects

CML with cloud compute This repository contains a sample project using CML with Terraform (via the cml-runner function) to launch an AWS EC2 instance

Iterative 19 Oct 03, 2022
Graphsignal is a machine learning model monitoring platform.

Graphsignal is a machine learning model monitoring platform. It helps ML engineers, MLOps teams and data scientists to quickly address issues with data and models as well as proactively analyze model

Graphsignal 143 Dec 05, 2022
Dragonfly is an open source python library for scalable Bayesian optimisation.

Dragonfly is an open source python library for scalable Bayesian optimisation. Bayesian optimisation is used for optimising black-box functions whose

744 Jan 02, 2023
A Streamlit demo to interactively visualize Uber pickups in New York City

Streamlit Demo: Uber Pickups in New York City A Streamlit demo written in pure Python to interactively visualize Uber pickups in New York City. View t

Streamlit 230 Dec 28, 2022
Covid-polygraph - a set of Machine Learning-driven fact-checking tools

Covid-polygraph, a set of Machine Learning-driven fact-checking tools that aim to address the issue of misleading information related to COVID-19.

1 Apr 22, 2022
Distributed Computing for AI Made Simple

Project Home Blog Documents Paper Media Coverage Join Fiber users email list Uber Open Source 997 Dec 30, 2022

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Code Repository for Machine Learning with PyTorch and Scikit-Learn

Sebastian Raschka 1.4k Jan 03, 2023
Customers Segmentation with RFM Scores and K-means

Customer Segmentation with RFM Scores and K-means RFM Segmentation table: K-Means Clustering: Business Problem Rule-based customer segmentation machin

5 Aug 10, 2022
MasTrade is a trading bot in baselines3,pytorch,gym

mastrade MasTrade is a trading bot in baselines3,pytorch,gym idea we have for example 1 btc and we buy a crypto with it with market option to trade in

Masoud Azizi 18 May 24, 2022
Azure MLOps (v2) solution accelerators.

Azure MLOps (v2) solution accelerator Welcome to the MLOps (v2) solution accelerator repository! This project is intended to serve as the starting poi

Microsoft Azure 233 Jan 01, 2023
Python ML pipeline that showcases mltrace functionality.

mltrace tutorial Date: October 2021 This tutorial builds a training and testing pipeline for a toy ML prediction problem: to predict whether a passeng

Log Labs 28 Nov 09, 2022
hgboost - Hyperoptimized Gradient Boosting

hgboost is short for Hyperoptimized Gradient Boosting and is a python package for hyperparameter optimization for xgboost, catboost and lightboost using cross-validation, and evaluating the results o

Erdogan Taskesen 34 Jan 03, 2023
Python module for performing linear regression for data with measurement errors and intrinsic scatter

Linear regression for data with measurement errors and intrinsic scatter (BCES) Python module for performing robust linear regression on (X,Y) data po

Rodrigo Nemmen 56 Sep 27, 2022
A Python Module That Uses ANN To Predict A Stocks Price And Also Provides Accurate Technical Analysis With Many High Potential Implementations!

Stox A Module to predict the "close price" for the next day and give "technical analysis". It uses a Neural Network and the LSTM algorithm to predict

Stox 31 Dec 16, 2022
Machine Learning from Scratch

Machine Learning from Scratch Author: Shengxuan Wang From: Oregon State University Content: Building Machine Learning model from Scratch, without usin

ShawnWang 0 Jul 05, 2022