GitHub - devAmoghS/Machine-Learning-with-Python: Small scale machine learning pr...
source link: https://github.com/devAmoghS/Machine-Learning-with-Python
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
README.md
Machine-Learning-with-Python
Small scale machine learning projects to understand the core concepts
- Topic Modelling using Latent Dirichlet Allocation with newsgroups20 dataset, implemented with Python and Scikit-Learn
- Implemented a simple neural network built with Keras on MNIST dataset
- Stock Price Forecasting on Google using Linear Regression
- Implemented a simple a social network to learn basics of Python
- Implemented Naives Bayes Classifier to filter spam messages on SpamAssasin Public Corpus
- Churn Prediction Model for banking dataset using Keras and Scikit-Learn
- Implemented Random Forest from scratch and built a classifier on Sonar dataset from UCI repository
- Simple Linear Regression in Python on sample dataset
- Multiple Regression in Python on sample dataset
- PCA and scaling sample stock data in Python [working_with_data]
- Decision Trees in Python on sample dataset
- Logistic Regression in Python on sample dataset
- Built a neural network in Python to defeat a captcha system
- Helper methods include commom operations used in Statistics, Probability, Linear Algebra and Data Analysis
- K-means clustering with example data; clustering colors with k-means; Bottom-up Hierarchical Clustering
- Generating Word Clouds
- Sentence generation using n-grams
- Sentence generation using Grammars and Automata Theory; Gibbs Sampling
- Topic Modelling using Latent Dirichlet Analysis (LDA)
Installation notes
MLwP is built using Python 3.5. The easiest way to set up a compatible environment is to use Conda. This will set up a virtual environment with the exact version of Python used for development along with all the dependencies needed to run MLwP.
-
Create a Conda environment with Python 3.
conda create -n *your env name* python=3.5
-
Now activate the Conda environment.
source activate *your env name*
-
Install the required dependencies.
./scripts/install_requirements.sh
How good is the code ?
- It is well tested
- It passes style checks (PEP8 compliant)
- It can compile in its current state (and there are relatively no issues)
How much support is available?
- FAQs (coming soon)
- Documentation (coming soon)
Issues
Feel free to submit issues and enhancement requests.
Contributing
Please refer to each project's style guidelines and guidelines for submitting patches and additions. In general, we follow the "fork-and-pull" Git workflow.
- Fork the repo on GitHub
- Clone the project to your own machine
- Commit changes to your own branch
- Push your work back up to your fork
- Submit a Pull request so that we can review your changes
NOTE: Be sure to merge the latest from "upstream" before making a pull request!
Recommend
-
261
-
32
README.md Python-Interview-Problems-for-Practice 40+ Common code and interview problems solved in Python (it's growing...) The core idea is not...
-
9
Open clouds vs the big three Yes, the major public clouds offer a lot. But what you may not know are the limitations, and how open clouds make the difference.
-
3
River is a Python library for online machine learning. It is the result of a merger between creme and
-
5
The Ultimate FREE Machine Learning Study Plan A complete study plan to become a Machine Learning Engineer with links to all FREE resources. If you finish the list you will be equipped with enough theoretical and practical experience...
-
3
Machine Learning Can Also Scale Misleading Terms, Unwanted Data Sharing, and Automated BiasMachine Learning Can Also Scale Misleading Terms, Unwanted Data Sharing, and Automated Bias
-
14
Welcome to Gradio Quickly create customizable UI components around your models. Gradio makes it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images, pasting your own text, recordi...
-
8
The Future of Data Science and Machine Learning at Enterprise Scale January 5, 2022 by Qubole Team Updated February 3rd, 2022 Data Science, Artificial Intelligence, Anal...
-
4
Community Adopting MLSecOps for secure machine learning at scale
-
0
Community Responsible use of machine learning to verify identities at scaleÂ
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK