Days for Python Study in Data Science
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NumPy
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Pandas
## install gitbook
npm install gitbook -g
npm install gitbook-cli -g
gitbook --version
## build boook
gitbook build
gitbook pdf ./ ./a.pdf # 生成 pdf
## build a serve
gitbook serve
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Awesome Data Science: Here you’ll find every step that you need to take till the end of your journey.
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Data Science Resource: Data science tutorials to help you conquer this skill set.
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Text Books in Data Science: books on machine learning, data mining, statistics, data visualization etc.
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Pandas on PyVideo tutorials
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Data Science Algorithms: algorithms such as Linear Regression, Logistic Regression, K-Mean Clustering, Random Forest.
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Statistics and ML: tutorials to python pandas, machine learning algorithms, statistics and data visualization
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Scikit Learn: Scikit learn is a python library for machine learning. ( Hint: Dig Deeper )
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Awesome Machine Learning: tutorials, resources, guides for machine learning, data analysis, natural language processing, data visualization in all the programming languages like Python, R, Java, Go, C++, Swift. Choose accordingly.
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Complete Machine Learning: collection of tutorials and examples for solving problems using machine learning. It consist of beginning to end steps of ML covering stages such as model evaluation, implementation of ML algorithms, data visualization etc.
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Parallel Machine Learning: This tutorial is on using scikit learn and ipython for parallel machine learning. Here you’ll find a 2 hours long video from Pycon 2013 with lecture notes and other useful resources.
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Machine Learning Courses: a list of Best Machine Learning Courses in the world.
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Caffe: a deep learning framework made with expression, speed, and modularity in mind.
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Awesome Deep Learning: tutorials on Deep Learning which includes deep learning courses, free books, videos and lectures, papers
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Deep Learning in Python: tutorial on implementation of Deep Learning in Python
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Recurrent Neural Networks: dedicated resources for RNN. codes, lectures, books and resources on multiple applications of RNN.
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matplotlib: plotting with Python
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seaborn: a library for making statistical graphics in Python. It is built on top of matplotlib and closely integrated with pandas data structures.
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Bokeh: JavaScript visualization library with a Python frontend that creates highly interactive visualizations capable of handling very large and/or streaming datasets.
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Plotly is the eponymous open source product of the Plotly company, and is similar in spirit to Bokeh. Because Plotly is the main product of a startup, it is receiving a high level of development effort. Use of the library is entirely free.
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Vispy is an actively developed project focused on dynamic visualizations of very large datasets. Because it is built to target OpenGL and make use of efficient graphics processors in your computer, it is able to render some quite large and stunning visualizations.
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Vega and Vega-Lite are declarative graphics representations, and are the product of years of research into the fundamental language of data visualization. The reference rendering implementation is JavaScript, but the API is language agnostic. There is a Python API under development in the Altair package. Though as of summer 2016 it's not yet fully mature, I'm quite excited for the possibilities of this project to provide a common reference point for visualization in Python and other languages.