Python toolkit to get started with data science and machine learning notebooks
When I started learning python, I used to get lost in what concepts to learn in python language, especially coming from a non programming background. This repository is aimed to help learners from non computer science background to know what key concepts of python progamming language is important for data science and machine learning projects.
This repository will have a series of notebooks to get learn basic python concepts necessary to get started with data science and machine learning projects. In future, I will be adding notebooks for learning key python libraries - pandas and numpy.
Some of the examples in these notebooks have been referenced from learnbyexample and real python which are very good resources to learn python.
Python:
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Part 1
- Math Operators
- Variables
- Flow Control
- Boolean Operators
- List
- Dictionary
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Part 2
- Dictionary continued
- Comprehension
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Part 3
- Strings
- Python escape sequences
- String Slicing
- If-Else-Elif
- Loops - For & While
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Part 4
- OOPS (Classes, objects and methods)
- Inheritance
- super()
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Functions
- functions using def
- lambda functions
- lambda functions with map, filter and reduce
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Decorators
- Functions as first-class objects
- Creating Decorators
- Decorator with arguments
- Classes as decorators/Decorators as classes
Pandas
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Part 1
- Series, Dataframes
- Basic methods(head, tail, unique, etc.)
- Filtering
- Pivoting
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Part 2
- Concat/Append
- Merge
- Groupby
References: