Python programming for beginners: 3 books in 1: Beginner's guide, Data science and Machine learning

253 Pages·7.549 MB·English

by  William Wizner

Python programming for beginners
3 books in 1
Beginner’s guide, Data science and Machine learning.
Switch from noobgramming to PROgramming in 27 days and bring out your
code poet attitude.
William Wizner
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Python for beginners
Introduction
Chapter 1: Installing Python
Lосаl Envіrоnmеnt Sеtuр
Gеttіng Pуthоn
Inѕtаllіng Pуthоn
Hеrе Is A Quick Оvеrvіеw Оf Installing Python Оn Various Platforms:
Unіx And Lіnux Installation
Wіndоwѕ Installation
Macintosh Inѕtаllаtіоn
Setting Uр PATH
Sеttіng Path at Unіx/Lіnux
Sеttіng Раth Аt Windows
Pуthоn Environment Variables
Runnіng Pуthоn
Intеrасtіvе Interpreter
Script from The Cоmmаnd-Lіnе
Intеgrаtеd Dеvеlорmеnt Envіrоnmеnt
IDLE
A Fіlе Edіtоr
Edіtіng A Fіlе
How to Improve Yоur Wоrkflоw
Chapter 2: Python Loops and Numbers
Loops
Numbers
Chapter 3: Data Types
String Manipulation
String Formatting
Type Casting
Assignment and Formatting Exercise
Chapter 4: Variable in Python
Variable Vs. Constants
Variables Vs. Literals
Variables Vs. Arrays
Classifications of Python Arrays Essential for Variables
Naming Variables
Learning Python Strings, Numbers and Tuple
Types of Data Variables
Chapter 5: Inputs, Printing, And Formatting Outputs
Inputs
Printing and Formatting Outputs
Input and Formatting Exercise
Chapter 6: Mathematical Notation, Basic Terminology, and Building
Machine Learning Systems
Mathematical Notation for Machine Learning
Terminologies Used for Machine Learning
Chapter 7: Lists and Sets Python
Lists
Sets
Chapter 8: Conditions Statements
“if” statements
Else Statements
Code Blocks
While
For Loop
Break
Infinite Loop
Continue
Practice Exercise
Chapter 9: Iteration
While Statement
Definite and Indefinite Loops
The for Statement
Chapter 10: Functions and Control Flow Statements in Python
What is a Function?
Defining Functions
Call Function
Parameters of Function
Default Parameters
What is the control flow statements?
break statement
continue statement
pass statement
else statement
Conclusion:
Python for data science
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:
Machine learning with Python
Introduction:
Chapter 1: Python Installation
Anaconda Python Installation
Jupyter Notebook
Fundamentals of Python programming
Chapter 2: Python for Machine Learning
Chapter 3: Data Scrubbing
What is Data Scrubbing?
Removing Variables
One-hot Encoding
Drop Missing Values
Chapter 4: Data Mining Categories
Predictive Modeling
Analysis of Associations
Group Analysis
Anomaly Detection
Chapter 5: Difference Between Machine Learning and AI
What is artificial intelligence?
How is machine learning different?
Chapter 6: K-Means Clustering
Data Preparation
Visualizing the Data
Creating Clusters
Chapter 7: Linear Regression with Python
Chapter 8: Feature Engineering
Rescaling Techniques
Creating Derived Variables
Non-Numeric Features
Chapter 9: How Do Convolutional Neural Networks Work?
Pixels and Neurons
The Pre-Processing
Convolutions
Filter: Kernel Set
Activation Function
Subsampling
Subsampling with Max-Pooling
Now, More Convolutions!
Connect With a "Traditional" Neural Network
Chapter 10: Top AI Frameworks and Machine Learning Libraries
TеnѕоrFlоw
Ѕсikit-lеаrn
AI as a Dаtа Analyst
Thеаnо
Caffe
Keras
Miсrоѕоft Cоgnitivе Tооlkit
PyTorch
Tоrсh
Chapter 11: The Future of Machine Learning
Conclusion:
Python for beginners:
LEARN CODING, PROGRAMMING, DATA ANALYSIS, AND
ALGORITHMIC THINKING WITH THE LATEST PYTHON
CRASH COURSE. A STARTER GUIDE WITH TIPS AND TRICKS
FOR THE APPRENTICE PROGRAMMER.
William Wizner