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Numerical Computing with Python
Numerical Computing with Python

Numerical Computing with Python: Harness the power of Python to analyze and find hidden patterns in the data

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Profile Icon Pratap Dangeti Profile Icon Allen Yu Profile Icon Claire Chung Profile Icon Aldrin Yim Profile Icon Theodore Petrou +1 more Show less
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$19.99 per month
Book Dec 2018 pages 1st Edition
Arrow left icon
Profile Icon Pratap Dangeti Profile Icon Allen Yu Profile Icon Claire Chung Profile Icon Aldrin Yim Profile Icon Theodore Petrou +1 more Show less
Arrow right icon
$19.99 per month
Book Dec 2018 pages 1st Edition

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Key benefits

  • Use the power of Pandas and Matplotlib to easily solve data mining issues
  • Understand the basics of statistics to build powerful predictive data models
  • Grasp data mining concepts with helpful use-cases and examples

Description

Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining. You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models. By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following Packt products: • Statistics for Machine Learning by Pratap Dangeti • Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim • Pandas Cookbook by Theodore Petrou

What you will learn

  • Understand the statistical fundamentals to build data models
  • Split data into independent groups
  • Apply aggregations and transformations to each group
  • Create impressive data visualizations
  • Prepare your data and design models
  • Clean up data to ease data analysis and visualization
  • Create insightful visualizations with Matplotlib and Seaborn
  • Customize the model to suit your own predictive goals

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 21, 2018
Length pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789953633
Category :
Concepts :

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View plans & pricing

Product Details

Publication date : Dec 21, 2018
Length pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789953633
Category :
Concepts :

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Frequently bought together

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Python: Advanced Guide to Artificial Intelligence
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Numerical Computing with Python
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Table of Contents

21 Chapters
Title Page Chevron down icon Chevron up icon
Contributors Chevron down icon Chevron up icon
About Packt Chevron down icon Chevron up icon
Preface Chevron down icon Chevron up icon
1. Journey from Statistics to Machine Learning Chevron down icon Chevron up icon
2. Tree-Based Machine Learning Models Chevron down icon Chevron up icon
3. K-Nearest Neighbors and Naive Bayes Chevron down icon Chevron up icon
4. Unsupervised Learning Chevron down icon Chevron up icon
5. Reinforcement Learning Chevron down icon Chevron up icon
6. Hello Plotting World! Chevron down icon Chevron up icon
7. Visualizing Online Data Chevron down icon Chevron up icon
8. Visualizing Multivariate Data Chevron down icon Chevron up icon
9. Adding Interactivity and Animating Plots Chevron down icon Chevron up icon
10. Selecting Subsets of Data Chevron down icon Chevron up icon
11. Boolean Indexing Chevron down icon Chevron up icon
12. Index Alignment Chevron down icon Chevron up icon
13. Grouping for Aggregation, Filtration, and Transformation Chevron down icon Chevron up icon
14. Restructuring Data into a Tidy Form Chevron down icon Chevron up icon
15. Combining Pandas Objects Chevron down icon Chevron up icon
1. Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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