mlclass | Python code for Machine Learning course from Coursera | Machine Learning library
kandi X-RAY | mlclass Summary
kandi X-RAY | mlclass Summary
mlclass is a Python library typically used in Artificial Intelligence, Machine Learning, Numpy applications. mlclass has no bugs, it has no vulnerabilities and it has low support. However mlclass build file is not available. You can download it from GitHub.
These are Python code for Machine Learning course’s exercise from Coursera, the one taught by Andrew Ng. You can find the course available online here: I made it in Python since some of the Octave functionalities don’t work on my computer, for example plotting. Thus I managed to recreate most if not all of the exercises in Python.
These are Python code for Machine Learning course’s exercise from Coursera, the one taught by Andrew Ng. You can find the course available online here: I made it in Python since some of the Octave functionalities don’t work on my computer, for example plotting. Thus I managed to recreate most if not all of the exercises in Python.
Support
Quality
Security
License
Reuse
Support
mlclass has a low active ecosystem.
It has 8 star(s) with 128 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
mlclass has no issues reported. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of mlclass is current.
Quality
mlclass has no bugs reported.
Security
mlclass has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
mlclass 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
mlclass releases are not available. You will need to build from source code and install.
mlclass has no build file. You will be need to create the build yourself to build the component from source.
Top functions reviewed by kandi - BETA
kandi has reviewed mlclass and discovered the below as its top functions. This is intended to give you an instant insight into mlclass implemented functionality, and help decide if they suit your requirements.
- Calculate the minimization function for a function .
- Partial part 2 . 3 .
- Partition 2 test .
- This function is used to train the dataset .
- Partial layer 2 .
- Displays the data on the image .
- Create a learning curve
- Calculate cost function .
- The main function of dataset 3 .
- Plots the minimum of the feature 2 .
Get all kandi verified functions for this library.
mlclass Key Features
No Key Features are available at this moment for mlclass.
mlclass Examples and Code Snippets
No Code Snippets are available at this moment for mlclass.
Community Discussions
Trending Discussions on mlclass
QUESTION
Andrew Ng's ML course (in python) - Applying gradient descent with multiple variables, confused about intuition
Asked 2020-Jun-30 at 20:20
I am trying to create the equation for gradient descent with multiple variables. Picture of equation: https://www.holehouse.org/mlclass/04_Linear_Regression_with_multiple_variables_files/Image%20[3].png
The final solution is:
...ANSWER
Answered 2020-Jun-30 at 19:30np.dot includes the summation of multiplied elements.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install mlclass
You can download it from GitHub.
You can use mlclass 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.
You can use mlclass 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:
Reuse Trending Solutions
![https://www.holehouse.org/mlclass/04_Linear_Regression_with_multiple_variables_files/Image%20[3].png](https://www.holehouse.org/mlclass/04_Linear_Regression_with_multiple_variables_files/Image%20%5B3%5D.png){kind=link}
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
