MachineLearningWithMe | 数据科学与深度学习研究所新生入门指南

by TolicWang Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | MachineLearningWithMe Summary

MachineLearningWithMe is a Python library. MachineLearningWithMe has no bugs, it has no vulnerabilities and it has low support. However MachineLearningWithMe build file is not available. You can download it from GitHub.

数据科学与深度学习研究所新生入门指南

Support

Quality

Security

License

Reuse

Support

MachineLearningWithMe has a low active ecosystem.

It has 30 star(s) with 12 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

MachineLearningWithMe has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of MachineLearningWithMe is current.

Quality

MachineLearningWithMe has 0 bugs and 0 code smells.

Security

MachineLearningWithMe has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

MachineLearningWithMe code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

MachineLearningWithMe does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

MachineLearningWithMe releases are not available. You will need to build from source code and install.

MachineLearningWithMe has no build file. You will be need to create the build yourself to build the component from source.

MachineLearningWithMe saves you 596 person hours of effort in developing the same functionality from scratch.

It has 1389 lines of code, 88 functions and 33 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed MachineLearningWithMe and discovered the below as its top functions. This is intended to give you an instant insight into MachineLearningWithMe implemented functionality, and help decide if they suit your requirements.

Train the model
Perform feature scaling
Load training data
Predict spelling
Wraps kmeans model
Compute the F score
Gradient of the gradient descent function
Computes the cost function
Performs stacking on training data
Load train and test data
Visualize the word cloud
Reads data from a file
Random forest classifier
Make pie chart
GradientBoosting classifier
Calculate accuracy
Logistic regression
Run Decision Tree Classifier
Runs the model selection
Generate decision tree
Solve DecisionTree classifier
Creates a prediction for the model
Show visiualization
Linear regression
Compute the F - score for a given sample
Use spellcheck
Load credit card data

Get all kandi verified functions for this library.

MachineLearningWithMe Key Features

No Key Features are available at this moment for MachineLearningWithMe.

MachineLearningWithMe Examples and Code Snippets

No Code Snippets are available at this moment for MachineLearningWithMe.

Community Discussions

No Community Discussions are available at this moment for MachineLearningWithMe.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install MachineLearningWithMe

You can download it from GitHub.
You can use MachineLearningWithMe like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: