data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines

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IPython Notebook(s) demonstrating deep learning functionality.IPython Notebook(s) demonstrating scikit-learn functionality.

machine-learning deep-learning data-science big-data aws tensorflow theano caffe scikit-learn kaggle spark mapreduce hadoop matplotlib pandas numpy scipy keras

machineJS - Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml

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I just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competition

PythonMachineLearning - Practice and tutorial-style notebooks covering wide variety of machine learning techniques

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Practice and tutorial-style notebooks covering wide variety of machine learning techniques

numpy statistics pandas matplotlib regression scikit-learn classification principal-component-analysis clustering decision-trees random-forest dimensionality-reduction neural-network deep-learning artificial-intelligence data-science machine-learning k-nearest-neighbours naive-bayes

eland - Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch

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Eland is a Python Elasticsearch client for exploring and analyzing data in Elasticsearch with a familiar Pandas-compatible API. Where possible the package uses existing Python APIs and data structures to make it easy to switch between numpy, pandas, scikit-learn to their Elasticsearch powered equivalents. In general, the data resides in Elasticsearch and not in memory, which allows Eland to access large datasets stored in Elasticsearch.

elasticsearch machine-learning big-data etl scikit-learn pandas lightgbm data-analysis dataframe dataframes time-series-forecasting eland

astroML - Machine learning, statistics, and data mining for astronomy and astrophysics

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AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, and matplotlib, and distributed under the BSD license. It contains a growing library of statistical and machine learning routines for analyzing astronomical data in python, loaders for several open astronomical datasets, and a large suite of examples of analyzing and visualizing astronomical datasets. This project was started in 2012 by Jake VanderPlas to accompany the book Statistics, Data Mining, and Machine Learning in Astronomy by Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray.

Math-of-Machine-Learning-Course-by-Siraj - Implements common data science methods and machine learning algorithms from scratch in python

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This repository was initially created to submit machine learning assignments for Siraj Raval's online machine learning course. The purpose of the course was to learn how to implement the most common machine learning algorithms from scratch (without using machine learning libraries such as tensorflow, PyTorch, scikit-learn, etc). Although that course has ended now, I am continuing to learn data science and machine learning from other sources such as Coursera, online blogs, and attending machine learning lectures at University of Toronto. Sticking to the theme of implementing machine learning algortihms from scratch, I will continue to post detailed notebooks in python here as I learn more.

machine-learning statistical-concepts siraj-raval machine-learning-algorithms machine-learning-from-scratch

python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource

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This GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository instead. What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.

machine-learning machine-learning-algorithms logistic-regression data-science data-mining scikit-learn neural-network

stellargraph - StellarGraph - Machine Learning on Graphs

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StellarGraph is a Python library for machine learning on graphs and networks. StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. It is thus user-friendly, modular and extensible. It interoperates smoothly with code that builds on these, such as the standard Keras layers and scikit-learn, so it is easy to augment the core graph machine learning algorithms provided by StellarGraph. It is thus also easy to install with pip or Anaconda.

machine-learning graphs machine-learning-algorithms networkx graph-data graph-analysis graph-machine-learning link-prediction graph-convolutional-networks gcn saliency-map interpretability geometric-deep-learning graph-neural-networks heterogeneous-networks stellargraph-library

practical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system

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"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

machine-learning deep-learning text-analytics classification clustering natural-language-processing computer-vision data-science spacy nltk scikit-learn prophet time-series-analysis convolutional-neural-networks tensorflow keras statsmodels pandas deep-neural-networks

mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions

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Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries. More details about installing Mars can be found at installation section in Mars document.

machine-learning tensorflow numpy scikit-learn pandas pytorch xgboost lightgbm tensor dask ray dataframe statsmodels joblib

ml-workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.

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The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. The workspace requires Docker to be installed on your machine (📖 Installation Guide).

nlp docker kubernetes data-science machine-learning r deep-learning jupyter anaconda tensorflow gpu scikit-learn vscode jupyter-notebook data-visualization pytorch neural-networks data-analysis jupyter-lab

Machine-Learning / Deep-Learning / AI + Web3 -Tutorials

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A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

machine-learning deep-learning tensorflow pytorch keras matplotlib aws kaggle pandas scikit-learn torch artificial-intelligence neural-network convolutional-neural-networks tensorflow-tutorials python-data ipython-notebook capsule-network

Seaborn - Statistical data visualization using matplotlib

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Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.Online documentation is available at seaborn.pydata.org. Installation requires numpy, scipy, pandas, and matplotlib. Some functions will optionally use statsmodels if it is installed.

data-visualization visualization statistics

Python-Machine-Learning-Cookbook - Code files for Python-Machine-Learning-Cookbook

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##Instructions and Navigation This is the code repository for Python Machine Learning Cookbook, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. The code files are organized according to the chapters in the book. These code samples will work on any machine running Linux, Mac OS X, or Windows. Even though they are written and tested on Python 2.7, you can easily run them on Python 3.x with minimal changes. To run the code samples, you need to install scikit-learn, NumPy, SciPy, and matplotlib. For Chapter 6, you will need to install NLTK and gensim. To run the code in chapter 7, you need to install hmmlearn and python_speech_features. For chapter 8, you need to install Pandas and PyStruct. Chapter 8 also makes use of hmmlearn. For chapters 9 and 10, you need to install OpenCV. For chapter 11, you need to install NeuroLab.

statistical-analysis-python-tutorial - Statistical Data Analysis in Python

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Chris Fonnesbeck is an Assistant Professor in the Department of Biostatistics at the Vanderbilt University School of Medicine. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He originally hails from Vancouver, BC and received his Ph.D. from the University of Georgia. This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to bootstrapping methods. Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance.

DAT210x - Programming with Python for Data Science Microsoft

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NumPy is a popular Python module for calculating linear algebra very optimally. MatPlotLib for interactive 2D / 3D graphs, Pandas for dataset manipulation, and SciKit-Learn for machine learning. After completing a lab exercise, submit your answers onto the appropriate course lab page in order to receive a grade.

Scikit Learn - Machine Learning in Python

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scikit-learn is a Python module for machine learning built on top of SciPy. It is simple and efficient tools for data mining and data analysis. It supports automatic classification, clustering, model selection, pre processing and lot more.

machine-learning data-mining data-analysis classification

DAT8 - General Assembly's 2015 Data Science course in Washington, DC

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Course materials for General Assembly's Data Science course in Washington, DC (8/18/15 - 10/29/15).

data-science machine-learning scikit-learn data-analysis pandas jupyter-notebook course linear-regression logistic-regression model-evaluation naive-bayes natural-language-processing decision-trees ensemble-learning clustering regular-expressions web-scraping data-visualization data-cleaning

igel - a delightful machine learning tool that allows you to train, test, and use models without writing code

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The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I needed a tool sometimes, which I can use to fast create a machine learning prototype. Whether to build some proof of concept or create a fast draft model to prove a point. I find myself often stuck at writing boilerplate code and/or thinking too much of how to start this.

data-science machine-learning automation neural-network scikit-learn sklearn machine-learning-algorithms artificial-intelligence neural-networks data-analysis machine-learning-library machinelearning preprocessing automl multilayer-perceptron-network scikitlearn-machine-learning multilayer-perceptron automl-api automl-algorithms automl-experiments

rumale - Rumale is a machine learning library in Ruby

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Rumale (Ruby machine learning) is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Linear / Kernel Support Vector Machine, Logistic Regression, Linear Regression, Ridge, Lasso, Kernel Ridge, Factorization Machine, Naive Bayes, Decision Tree, AdaBoost, Gradient Tree Boosting, Random Forest, Extra-Trees, K-nearest neighbor classifier, K-Means, K-Medoids, Gaussian Mixture Model, DBSCAN, SNN, Power Iteration Clustering, Mutidimensional Scaling, t-SNE, Principal Component Analysis, Kernel PCA and Non-negative Matrix Factorization. This project was formerly known as "SVMKit". If you are using SVMKit, please install Rumale and replace SVMKit constants with Rumale.

machine-learning data-science data-analysis artificial-intelligence