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

✍ Scribed by Pratap Dangeti, Allen Yu, Claire Chung, Aldrin Yim, Theodore Petrou

• Publisher: Packt Publishing
• Year: 2018
• Tongue: English
• Leaves: 676
• Edition: 1
• Category: Library

✦ Synopsis

Understand, explore, and effectively present data using the powerful data visualization techniques of Python

Key Features
• 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

Book 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

Who this book is for
If you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.

✦ Table of 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

✦ Subjects

Machine Learning; Data Analysis; Unsupervised Learning; Reinforcement Learning; Decision Trees; Python; Clustering; Principal Component Analysis; Data Visualization; Naive Bayes; Statistics; Monte Carlo Simulation; Logistic Regression; matplotlib; pandas; Dynamic Programming; Seaborn; Random Forest; AdaBoost; Temporal Difference Learning