Build a foundation for data science and machine learning by using Python to master the concepts and techniques of Object Oriented Programming (OOP), File Handling, and Iteration. Follow along with machine learning expert Advait Jayant through a combination of lecture and hands-on to master essential concepts and techniques in this first course in the Data Science and Machine Learning Series.

Also here are all of Advait Jayant's highly-rated videos on O'Reilly, including the full Data Science and Machine Learning Series.

The following ten topics will be covered in this Data Science and Machine Learning course:

Introducing Python Modules. Create, run, and import Python Modules in this first topic in the Data Science and Machine Learning Series. A Python module is a file containing Python definitions and statements. A module can define functions, classes, and variables. Import and run a standard library of modules including the Math and Sys Modules.
The Python Main Module. Work with the Python main module in this second topic in the Data Science and Machine Learning Series. The module being executed from the main terminal will act as the main module. Practice running from the main modules and determining whether a module is being directly executed in the terminal, or being imported in a module.
Object Oriented Programming in Python. Apply the Object Oriented Programming (OOP) approach using Python in this third topic in the Data Science and Machine Learning Series. OOP is an approach to problem solving where we attempt to implement abstracts of real world entities. Create objects and classes and instance variables and class variables using Python during this session.
Access Modifiers in Python. Leverage private and public access modifiers in Python in this fourth topic in the Data Science and Machine Learning Series. OOP is an approach to problem solving where we attempt to implement abstracts of real world entities. Create objects and classes using Python during this session.
Inheritance in Python. Apply the concept of inheritance using Python in this fifth topic in the Data Science and Machine Learning Series. Inheritance is the capability or a class to derive or inherit properties or data members from another class. Practice applying base and derived classes using Python.
File Handling in Python. Apply file handing using Python in this sixth topic in the Data Science and Machine Learning Series. The different modes of file handling that you will become proficient at include Read Only mode, Read and Write mode, Write Only mode, Write and Read mode, Append Only mode, and Append and Read mode.
File Handling with JSON. Apply file handing using JSON in this seventh topic in the Data Science and Machine Learning Series. Practice serialization and deserialization using JSON.
Exception Handling in Python. Create exception handling functions using Python in this eighth topic in the Data Science and Machine Learning Series. Exceptions are not syntax errors but instead errors that arise during the execution of the code.
Iteration Protocol in Python. Practice coding for iteration using the Python Iteration Protocol in this ninth topic in the Data Science and Machine Learning Series. Be able to explain the concepts of iteration, iterable, and iterator after watching this session.
Iterators in Python. Create and use iterators using Python in this tenth topic in the Data Science and Machine Learning Series. Inheritance is the capability or a class to derive or inherit properties or data members from another class.  
File Handling in Python. Apply the concept of inheritance using Python in this tenth topic in the Data Science and Machine Learning Series. Inheritance is the capability of a class to derive or inherit properties or data members from another class.

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https://www.oreilly.com/library/view/data-science-and/9781634626583/

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