What do you think?
Rate this book
PYTHON: Learn Coding Programs with Python Programming and Master Data Analysis & Analytics, Data Science and Machine Learning with the Complete Crash Course for Beginners - 4 Books in 1
If yes, then this Python for Beginners Crash Course is for you . This is the most complete Python guide with 4 Manuscripts in 1
1-Python Programming
2-Python for Data Analysis & Analytics
3-Python for Data Science
4-Python Machine Learning
A great Simplicity, Best Order and Selection of topics to Learn Fast and Selected Practice Exercises and Examples.
In Manuscript 1 “ Python Programming ” you'll
- What is Python
- How to install Python and what is the best distribution
- What are data types and variables
- How to work with numbers in Python
- What operators there are in Python and when to use them
- How to manipulate Strings
- How to implement Program Flow Controls
- How to implement loops in Python
- What are Python Lists, Tuples, Sets, Dictionaries and how to use them
- How to create modules and functions
- How to program according to the Object Oriented paradigm
- How to create classes
- What are and how to use Inheritance, Polymorphism, Abstraction and Encapsulation
And much more...
In Manuscript 2 “ Python for Data Analysis & Analytics ” you'll
- What Data Analysis is and why it is important
- What are the different types of Data Analysis
- What are the 6 key steps of the Data Analysis process that you should follow
- What are the applications of Data Analysis and Analytics
- How to set up the Python environment for Data Analysis
- What are and how to use Python Data Structures
- How to work with IPython/Jupyter Notebook
- How to work with NumPy
- How to visualize data with Matplotlib
- What other visualization libraries are out there
- Why is Big Data important and how to get the best out of it
- How to leverage Neural Networks for Data Analysis
And much more…
In Manuscript 3 “ Python for Data Science ” you'll
- What is Data Science and what does it encompass
- What are the 5 key steps of the Data Science process that you should follow
- How to set up the Python environment for Data Science
- How to work with Seaborn data visualization module
- What are the most important Machine Learning Algorithms
- How to leverage the Scikit-Learn module for Machine Learning
- How to leverage Data Science in the Cloud
- What are the most important application of Data Science
And much more…
In Manuscript 4 “ Python Machine Learning ” you’ll learn
- What is Machine Learning and what does it encompass
- What are the 7 Steps of the Machine Learning Process
- What are the different Machine Learning types
- How is Machine Learning applied to the real world
- What are the main Data Mining techniques
- How to best set up the Python environment for Machine Learning
- What are the most important Python libraries for Machine Learning
And much more…
1-Python Programming
2-Python for Data Analysis & Analytics
3-Python for Data Science
4-Python Machine Learning
A great Simplicity, Best Order and Selection of topics to Learn Fast and Selected Practice Exercises and Examples.
In Manuscript 1 “ Python Programming ” you'll
- What is Python
- How to install Python and what is the best distribution
- What are data types and variables
- How to work with numbers in Python
- What operators there are in Python and when to use them
- How to manipulate Strings
- How to implement Program Flow Controls
- How to implement loops in Python
- What are Python Lists, Tuples, Sets, Dictionaries and how to use them
- How to create modules and functions
- How to program according to the Object Oriented paradigm
- How to create classes
- What are and how to use Inheritance, Polymorphism, Abstraction and Encapsulation
And much more...
In Manuscript 2 “ Python for Data Analysis & Analytics ” you'll
- What Data Analysis is and why it is important
- What are the different types of Data Analysis
- What are the 6 key steps of the Data Analysis process that you should follow
- What are the applications of Data Analysis and Analytics
- How to set up the Python environment for Data Analysis
- What are and how to use Python Data Structures
- How to work with IPython/Jupyter Notebook
- How to work with NumPy
- How to visualize data with Matplotlib
- What other visualization libraries are out there
- Why is Big Data important and how to get the best out of it
- How to leverage Neural Networks for Data Analysis
And much more…
In Manuscript 3 “ Python for Data Science ” you'll
- What is Data Science and what does it encompass
- What are the 5 key steps of the Data Science process that you should follow
- How to set up the Python environment for Data Science
- How to work with Seaborn data visualization module
- What are the most important Machine Learning Algorithms
- How to leverage the Scikit-Learn module for Machine Learning
- How to leverage Data Science in the Cloud
- What are the most important application of Data Science
And much more…
In Manuscript 4 “ Python Machine Learning ” you’ll learn
- What is Machine Learning and what does it encompass
- What are the 7 Steps of the Machine Learning Process
- What are the different Machine Learning types
- How is Machine Learning applied to the real world
- What are the main Data Mining techniques
- How to best set up the Python environment for Machine Learning
- What are the most important Python libraries for Machine Learning
And much more…
456 pages, Kindle Edition
Published July 17, 2020
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 of 1 review
September 8, 2020
Grammar and editing issues
Significant grammar, spelling, and editing issues. They were enough that it got in the way of the material being presented.
Significant grammar, spelling, and editing issues. They were enough that it got in the way of the material being presented.
Displaying 1 of 1 review