Python for Beginners: Comprehensive Guide to the Basics of Programming, Machine Learning, Data Science and Analysis with Python.

Introduction
What is Python?
Installing Python 3 - Windows
Installing Python - Mac OSX
Installing Python - Linux
Running Python Programs
Data Types and Variables
Assign a Variable With a Value
Using Comments
Simultaneous Assignment
Data Types
Receiving An Input from The Console
Importing a Module
Python Numbers
Determining Types
Python Operators
Operator Precedence
Augmented Assignment Operator
Python Strings
Creating Strings
String Operations
Slicing Strings
ord() and chr() Functions
Python String Functions
In and Not In Operators
String Comparison
String Iteration Using a For Loop
Testing Strings
Searching for Substrings
Converting Strings
Python Lists
Creating Lists
Accessing List Elements
Common List Operations
List Slicing
+ and * Operators in List
in or not in Operator
Using a For Loop to Traverse a List
List Comprehension
Python Dictionaries
Creating a Dictionary
Retrieving, Modifying And Adding Elements
Deleting Items
Looping Items
Find the Dictionary Length
in or not in Operators
Equality Tests
Dictionary Methods
Python Tuples
Creating A Tuple
Tuples Functions
Iterating Through Tuples
Slicing Tuples
In And Not In Operator
Datatype Conversion
Converting an int to a Float
Converting a Float to an int
Converting a String to an int
Converting a Number to a String
Rounding Numbers
Python Control Statements
Nested if Statements
Python Functions
Creating Functions
Function With A Return Value
Global Variables Vs. Local Variables
Arguments With Default Values
Keyword Arguments
Combining Keyword and Positional Arguments
Multiple Values Returned From Function
Python Loops
The for Loop
range(a, b) Function
The while Loop
The break Statement
The continue Statement
Mathematical Functions
Generating Random Numbers
File Handling
Open a File
Close a File
Append Data
Using a For Loop
Reading and Writing - Binary
Objects and Classes
Define a Class
Self
Objects Created from Classes
How to Hide Data fields
Operator Overloading
Inheritance
Multiple Inheritance
Method Overriding
isinstance() Function
Exception Handling
Raising Exceptions
Exception Objects
Create A Custom Exception Class
Using A Custom Exception Class
Modules
Creating a Module
Using the From Statement With Import
Using the dir() Method
Beginner Tips for Learning Python
Conclusion
References
Introduction
Chapter O n e : A n O v e r v i e w o f M a c h i n e Learning
Machine Learning Categories
Examples of Machine Learning Applications
	Classification: Predicting Discrete Labels
	Regression: Predicting Continuous Labels
	Clustering: Inferring the Labels on Unlabeled Data
	Dimensionality Reduction: Inferring the Structure of Unlabeled Data
Chapter Two: Regression Machine Learning Models
When is Regression Required?
Different Types of Regression Techniques
	Linear Regression
	Logistic Regression
	Polynomial Regression
	Stepwise Regression
	Ridge Regression
	Lasso Regression
	ElasticNet Regression
C h a p t e r T h r e e: C l a s s i f i c a t i o n M a c h i n e L e a r n i n g M o d e l s
Different Classification Algorithms for Python
	Logistic Regression
	Naïve Bayes
	Stochastic Gradient Descent
	K-Nearest Neighbors
	Decision Tree
	Random Forest
	Support Vector Machine
	Accuracy
C h a p t e r F o u r : U n s u p e r v i s e d M a c h i n e L e a r n i n g A l g o r i t h m s
Why Choose Unsupervised Learning?
Different Types of Unsupervised Machine Learning
	Clustering
Types of Clustering
Supervised Machine Learning vs. Unsupervised Machine Learning
Unsupervised Machine Learning Applications
The Disadvantages of Using Unsupervised Learning
Chapter Five: Your First Machine Learning Project
The Hello World of Python Machine Learning
Chapter Six: An Introduction to Data Science
What is Data Science?
Using Pandas for Exploratory Analysis
Using Pandas for Data Wrangling
Building the Model
How to Learn Python For Data Science
	Step 1: Learn The Fundamentals of the Python Language
	Step 2: Do Some Small Python Projects
	Step 3: Learn the Python Data Science Libraries
	Step 4: As You Learn Python, Build Up a Data Science Portfolio
	Step 5: Apply Some Advanced Techniques in Data Science
Chapter Seven – Ten Things You Should Know About Machine Learning
Conclusion
Introduction
Chapter 1: Basics of Data Science
What is Data Science?
Career Scope and Impact of Data Science Using Python
Chapter 2: Various Aspects of Data Science
Steps Involved in a Data Science Project
	Defining the Problem
	Collecting Data from Sources
	Data Processing
	Feature Engineering
	Algorithm Selection
	Hyperparameter Tuning
	Data Visualization
	Interpretation of Results
How to Solve the Problems with Python
Chapter 3: Python Exploratory Analysis with Pandas
Understanding DataFrames and Series
Data Set For Practice – A Loan Prediction Problem
	Beginning with Exploration
	How to Import the Data Set and Libraries
	Quick Data Exploration
	Distribution Analysis
	Categorical Variable Analysis
Using Pandas for Data Munging in Python
	Checking for Missing Values
	Treating Extreme Values in a Distribution
Chapter 4: Basics of Python for Data Analysis
Why Do We Use Python v.3 And Not V.2|?
Data Structures of Python
Data Analysis in Python Using Pandas
Data Science Using Python: Start Instantly
Read the Tutorial Carefully
	Anaconda
	Jupyter Notebook
	Open New Notebook
	Math Calculations
	Data Importing
	Importing Dataset
	Exploration
	Clean the Dataset
	Features
	Develop an Easy Model
Using Matplotlib
Chapter 5: Metrics in Python along with Demo
Changes when a System shows unusual Behavior
What Are Metrics And How Many Types Of Metrics Are There?
Counters
Gauges
Histograms or Timers
Demo 1
	Mean
	Median
	Percentile
	Histogram and Cumulative Histogram
Demo 2
	Network Applications
	Long Processes
	How to Monitor in a Python Application
Chapter 6: How to Build a Predictive Model in Python
Logistic Regression
Decision Tree
Data Prediction and Analysis
C h a p t e r 7 : I n c o m e I n c r e m e n t u s i n g D a t a S c i e n c e w i t h P y t h o n
Search Options
Churn Prediction
Churn Categories
Data Science and Python: The Essential Relationship
Learning Python for Data Science
Conclusion
Introduction
Chapter 1: Data Analysis? Data Science? Or Machine Learning?
Machine Learning and Data Analysis Limitations
The Potential and the Implications
Chapter 2: Get and Process Your Data
CSV Files
Internal Data
Chapter 3: Data Visualization
Importing and Using Matplotlib
Supervised and Unsupervised Learning
Chapter 4: A Deeper Look at Regression
Multiple Linear Regression
Decision Tree Regression
Random Forest Regression
Chapter 5: Digging into Classification
Logistic Regression
K-Nearest Neighbors
Decision Tree Classification
Random Forest Classification
Chapter 6: A Look at Clustering
Clustering Goals and Uses
K-Means Clustering
Anomaly Detection
Chapter 7: What Is Reinforcement Learning?
Reinforcement Learning Compared with Supervised and Unsupervised Learning
How to Apply Reinforcement Learning
Chapter 8: The Artificial Neural Network
Imitating the Human Brain
Conclusion
References