English | 2018 | ISBN: 978-1789537864 | 472 Pages | EPUB | 10 MB
Gain useful insights from your data using popular data science tools
Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.
The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.
By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users
What you will learn
- Set up your data science toolbox on Windows, Mac, and Linux
- Use the core machine learning methods offered by the scikit-learn library
- Manipulate, fix, and explore data to solve data science problems
- Learn advanced explorative and manipulative techniques to solve data operations
- Optimize your machine learning models for optimized performance
- Explore and cluster graphs, taking advantage of interconnections and links in your data
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