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Learning Data Mining with Python

Learning Data Mining with Python

By : Robert Layton
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Learning Data Mining with Python

Learning Data Mining with Python

By: Robert Layton

Overview of this book

This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations.
Table of Contents (14 chapters)
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More resources


The following would serve as a really good resource for additional information:

Kaggle competitions

URL: www.kaggle.com/

Kaggle runs data mining competitions regularly, often with monetary prizes. Testing your skills on Kaggle competitions is a fast and great way to learn to work with real-world data mining problems. The forums are nice and share environments—often, you will see code released for a top-10 entry during the competition!

Coursera

URL: www.coursera.org

Coursera contains many courses on data mining and data science. Many of the courses are specialized, such as big data and image processing. A great general one to start with is Andrew Ng's famous course: https://www.coursera.org/learn/machine-learning/. It is a bit more advanced than this and would be a great next step for interested readers. For neural networks, check out this course: https://www.coursera.org/course/neuralnets. If you complete all of these, try out the course on probabilistic graphical models at https...

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