• Python Scikit Cheat Sheet
• Scikit Cheat Sheet
• Sklearn Cheat Sheet

Scikit-Learn; The goal of this series is to provide introductions, highlights, and demonstrations of how to use the must-have libraries so you can pick what to explore more in depth. Scikit-Learn (website here) is built on top of NumPy, SciPy, and matplotlib. It contains an extensive collection of ready-to-use Machine Learning. Scikit-Learn, also known as sklearn, is Python’s premier general-purpose machine learning library. While you’ll find other packages that do better at certain tasks, Scikit-Learn’s versatility makes it the best starting place for most ML problems.

Complete List of Cheat Sheets and Infographics for Artificial intelligence (AI), Neural Networks, Machine Learning, Deep Learning and Big Data.

Content Summary

Neural Networks
Neural Networks Graphs
Machine Learning Overview
Machine Learning: Scikit-learn algorithm
Scikit-Learn
Machine Learning: Algorithm Cheat Sheet
Python for Data Science
TensorFlow
Keras
Numpy
Pandas
Data Wrangling
Data Wrangling with dplyr and tidyr
Scipy
Matplotlib
Data Visualization
PySpark
Big-O
Resources

Neural Networks

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs. Such systems “learn” to perform tasks by considering examples, generally without being programmed with any task-specific rules.

Neural Networks Graphs

Graph Neural Networks (GNNs) for representation learning of graphs broadly follow a neighborhood aggregation framework, where the representation vector of a node is computed by recursively aggregating and transforming feature vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks.

Machine Learning Overview

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task.

Machine Learning: Scikit-learn algorithm

This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.

Scikit-Learn

Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Machine Learning: Algorithm Cheat Sheet

This machine learning cheat sheet from Microsoft Azure will help you choose the appropriate machine learning algorithms for your predictive analytics solution. First, the cheat sheet will asks you about the data nature and then suggests the best algorithm for the job.

Python for Data Science

TensorFlow

In May 2017 Google announced the second-generation of the TPU, as well as the availability of the TPUs in Google Compute Engine. The second-generation TPUs deliver up to 180 teraflops of performance, and when organized into clusters of 64 TPUs provide up to 11.5 petaflops.

Keras

In 2017, Google’s TensorFlow team decided to support Keras in TensorFlow’s core library. Chollet explained that Keras was conceived to be an interface rather than an end-to-end machine-learning framework. It presents a higher-level, more intuitive set of abstractions that make it easy to configure neural networks regardless of the backend scientific computing library.

Numpy

NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms written for this version of Python often run much slower than compiled equivalents. NumPy address the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays, requiring rewriting some code, mostly inner loops using NumPy.

Pandas

The name ‘Pandas’ is derived from the term “panel data”, an econometrics term for multidimensional structured data sets.

Data Wrangling

The term “data wrangler” is starting to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is introduced as “Steve Woodward, our data wrangler”.

Data Wrangling with dplyr and tidyr

Scipy

SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.

Matplotlib

matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of matplotlib. pyplot is a matplotlib module which provides a MATLAB-like interface. matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.

Data Visualization

PySpark

Big-O

Python Scikit Cheat Sheet

Big O notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. It is a member of a family of notations invented by Paul Bachmann, Edmund Landau and others, collectively called Bachmann–Landau notation or asymptotic notation.

Resources

Scikit Cheat Sheet

Big-O Algorithm Cheat Sheet
Bokeh Cheat Sheet
Data Science Cheat Sheet
Data Wrangling Cheat Sheet
Data Wrangling
Ggplot Cheat Sheet
Keras Cheat Sheet
Keras
Machine Learning Cheat Sheet
Machine Learning Cheat Sheet
ML Cheat Sheet
Matplotlib Cheat Sheet
Matpotlib
Neural Networks Cheat Sheet
Neural Networks Graph Cheat Sheet
Neural Networks
Numpy Cheat Sheet
NumPy
Pandas Cheat Sheet
Pandas
Pandas Cheat Sheet
Pyspark Cheat Sheet
Scikit Cheat Sheet
Scikit-learn
Scikit-learn Cheat Sheet
Scipy Cheat Sheet
SciPy
TesorFlow Cheat Sheet
Tensor Flow
Course Duck > The World’s Best Machine Learning Courses & Tutorials in 2020

Tag: Machine Learning, Deep Learning, Artificial Intelligence, Neural Networks, Big Data

From scikit-learn:
“Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems.

The flowchart is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data. Click on any estimator in the chart below to see it’s documentation.”

Sklearn Cheat Sheet

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