Top 10 Books To Learn Python | Best Books For Python

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This Edureka video on 'Best Books for Python' will suggest to you what we think are the best books for Python, even if you are an experienced programmer or a complete beginner. Below are the topics covered in this video:

This Edureka video on ‘Best Books for Python’ will suggest to you what we think are the best books for Python, even if you are an experienced programmer or a complete beginner. Below are the topics covered in this video:

Agenda - 0:46
Why Python - 1:10
Beginner Level Books - 1:46
Domain Specific Books - 4:12
Bonus Book - 6:53

Links for the Python Books:
Learning Python by Mark Lutz: http://bit.ly/2BR38aY
Python Crash Course by Eric Matthews: http://bit.ly/2BLlJ8i
Think Python by Allen Downey: http://bit.ly/2pjoXNC
Python Programming by John M Zelle: http://bit.ly/31SkYon
Python in a Nutshell by Alex Martelli: http://bit.ly/32UOyeh
Programming Python Mark Lutz: https://amzn.to/31Slhj1
Effective Computation in Physics by Anthony Scopatz, Kathryn D. Huff: http://bit.ly/2BPD00c
Python for Data Analysis by Wes McKinney: http://bit.ly/2pWCaMo
Python Machine Learning by Sebastian Raschka and Vahid Mirjalili: https://amzn.to/36amOV3
Django for Beginners by William S. Vincent: https://amzn.to/36lQtuG

Violent Python by TJ O’Connor: https://amzn.to/36cgs7w

Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s

#BestBooksforPython #Edureka #PythonEdureka #Pythonforbeginners #learnpython #pythonprogramming #pythontutorial #PythonTraining

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About the Course

Edureka’s Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:

  1. Master the Basic and Advanced Concepts of Python
  2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
  3. Master the Concepts of Sequences and File operations
  4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions using modules in Python
  5. Gain expertise in machine learning using Python and build a Real-Life Machine Learning application
  6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
  7. Master the concepts of MapReduce in Hadoop
  8. Learn to write Complex MapReduce programs
  9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
  10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
  11. Master the concepts of Web scraping in Python
  12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands-on Project Experience

Why learn Python?

Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built-in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain.

Who should go for python?

Edureka’s Data Science certification course in Python is a good fit for the below professionals:

· Programmers, Developers, Technical Leads, Architects

· Developers aspiring to be a ‘Machine Learning Engineer’

· Analytics Managers who are leading a team of analysts

· Business Analysts who want to understand Machine Learning (ML) Techniques

· Information Architects who want to gain expertise in Predictive Analytics

· ‘Python’ professionals who want to design automatic predictive models