Python for Data Science: Data analysis and Deep learning with Python coding and programming

✍ Scribed by William Wizner

• Tongue: English
• Leaves: 73
• Category: Library

✦ Table of Contents

Introduction:
Chapter 1: What is Data Analysis?
Chapter 2: The Basics of the Python Language
The Statements
The Python Operators
The Keywords
Working with Comments
The Python Class
How to Name Your Identifiers
Python Functions
Chapter 3: Using Pandas
Pandas
Chapter 4: Working with Python for Data Science
Why Python Is Important?
What Is Python?
Python's Position in Data Science
Data Cleaning
Data Visualization
Feature Extraction
Model Building
Python Installation
Installation Under Windows
Conda
Spyder
Installation Under MAC
Installation Under Linux
Install Python
Chapter 5: Indexing and Selecting Arrays
Conditional selection
NumPy Array Operations
Array – Array Operations
Array – Scalar operations
Chapter 6: K-Nearest Neighbors Algorithm
Splitting the Dataset
Feature Scaling
Training the Algorithm
Evaluating the Accuracy
K Means Clustering
Data Preparation
Visualizing the Data
Creating Clusters
Chapter 7: Big Data
The Challenge
Applications in the Real World
Chapter 8: Reading Data in your Script
Reading data from a file
Dealing with corrupt data
Chapter 9: The Basics of Machine Learning
The Learning Framework
PAC Learning Strategies
The Generalization Models
Chapter 10: Using Scikit-Learn
Uses of Scikit-Learn
Representing Data in Scikit-Learn
Tabular Data
Features Matrix
Target Arrays
Understanding the API
Conclusion: