donnemartin/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.
This project provides a collection of interactive guides for people looking to learn and apply various data analysis and machine learning techniques. It offers practical examples for building predictive models, analyzing large datasets, and visualizing information. This is ideal for data scientists, analysts, or researchers who need hands-on learning resources for common data science workflows.
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Use this if you are a data science practitioner or student seeking practical, code-based examples to understand and implement machine learning algorithms, statistical analyses, or big data processing techniques.
Not ideal if you are looking for a plug-and-play software solution or a high-level conceptual overview without diving into code.
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