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

Numerical Computing with Python: Harness the power of Python to analyze and find hidden patterns in the data
By 作者: Pratap Dangeti – Allen Yu – Claire Chung – Aldrin Yim – Theodore Petrou
ISBN-10 书号: 1789953634
ISBN-13 书号: 9781789953633
Release Finelybook 出版日期: 2018-12-21
pages 页数: (682 )

$49.99


Book Description to Finelybook sorting

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
Contents
1: JOURNEY FROM STATISTICS TO MACHINE LEARNING
2: TREE-BASED MACHINE LEARNING MODELS
3: K-NEAREST NEIGHBORS AND NAIVE BAYES
4: UNSUPERVISED LEARNING
5: REINFORCEMENT LEARNING
6: HELLO PLOTTING WORLD!
7: VISUALIZING ONLINE DATA
8: VISUALIZING MULTIVARIATE DATA
9: ADDING INTERACTIVITY AND ANIMATING PLOTS
10: SELECTING SUBSETS OF DATA
11: BOOLEAN INDEXING
12: INDEX ALIGNMENT
13: GROUPING FOR AGGREGATION, FILTRATION, AND TRANSFORMATION
14: RESTRUCTURING DATA INTO A TIDY FORM
15: COMBINING PANDAS OBJECTS
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
Authors
Pratap Dangeti
Pratap Dangeti is currently working as a Senior Data Scientist at Bidgely Technologies Bangalore. He has a vast experience in analytics and data science. He received his master’s degree from IIT Bombay in its industrial engineering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.

Allen Yu
Allen Yu, PhD, is a Chevening Scholar, 2017-18, and an MSC student in computer science at the University of Oxford. He holds a PhD degree in Biochemistry from the Chinese University of Hong Kong, and he has used Python and Matplotlib extensively during his 10 years of bioinformatics experience.

Apart from academic research, Allen is the co-founder of Codex Genetics Limited, which aims to provide a personalized medicine service in Asia through the use of the latest genomics technology.

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