https://github.com/msadeqsirjani/machine_learning_hands_on

A comprehensive machine learning repository containing different concepts, algorithms and techniques.
https://github.com/msadeqsirjani/machine_learning_hands_on

cnn computer-vision data-analysis data-science data-visualization deep-learning keras machine-learning matplotlib neural-network nlp numpy open-source pandas python rnn scikit-learn seaborn tensorflow

Last synced: about 1 month ago
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A comprehensive machine learning repository containing different concepts, algorithms and techniques.

• Host: GitHub
• URL: https://github.com/msadeqsirjani/machine_learning_hands_on
• Owner: msadeqsirjani
• Created: 2023-11-11T16:46:14.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2023-11-30T15:39:50.000Z (about 1 year ago)
• Last Synced: 2024-01-28T22:35:53.802Z (12 months ago)
• Topics: cnn, computer-vision, data-analysis, data-science, data-visualization, deep-learning, keras, machine-learning, matplotlib, neural-network, nlp, numpy, open-source, pandas, python, rnn, scikit-learn, seaborn, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 32.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
***Techniques, tools, best practices, and everything you need to learn machine learning!***

The complete Machine Learning Package comprises 35 notebooks covering Python programming, data manipulation, analysis, visualization, cleaning, classical machine learning, Computer Vision, and Natural Language Processing(NLP). Each notebook provides a high-level overview of the topic and includes visuals to aid understanding.

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## Tools Overview

The following are the tools covered in the Complete Machine Learning Package. They are popular tools that most machine learning engineers and data scientists need in one way or another and day to day. 

* [Python](https://www.python.org) is a high-level programming language prevalent in the data community. With the rapid growth of the libraries and frameworks, this is the correct programming language for ML.

* [NumPy](https://numpy.org) is a scientific computing tool for array or matrix operations. 

* [Pandas](https://pandas.pydata.org) is a great and simple tool for analyzing and manipulating data from various sources.

* [Matplotlib](https://matplotlib.org) is a comprehensive data visualization tool for creating static, animated, and interactive visualizations in Python.

* [Seaborn](https://seaborn.pydata.org) is another data visualization tool built on Matplotlib, which is pretty simple.

* [Scikit-Learn](https://scikit-learn.org/stable/): Instead of building machine learning models from scratch, Scikit-Learn makes using classical models in a few lines of code easy. This tool is adapted by almost all ML communities and industries, from startups to big techs. 

* [TensorFlow](https://www.tensorflow.org) and [Keras](https://keras.io) for deep learning: TensorFlow is a popular deep learning framework used for building models suitable for different fields such as Computer Vision and Natural Language Processing. Keras is a high-level neural network API that makes it easy to design deep learning models. TensorFlow and Keras have a great [community](https://discuss.tensorflow.org) and ecosystem that include tools like [TensorBoard](https://www.tensorflow.org/tensorboard), [TF Datasets](https://www.tensorflow.org/datasets), [TensorFlow Lite](https://www.tensorflow.org/lite), [TensorFlow Extended](https://www.tensorflow.org/tfx/), [TensorFlow Hub](https://www.tensorflow.org/hub), [TensorFlow.js](https://www.tensorflow.org/js), [TensorFlow GNN](https://github.com/tensorflow/gnn), and [much](https://www.tensorflow.org/resources/models-datasets) [more](https://www.tensorflow.org/resources/tools).

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