VinayIN / ML-Teaching-Notes

Workplace teaching session - Python beginners class and Machine learning Introductory notes

ML-Teaching-Notes

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This repository contains notebooks that were used to teach group of co-workers in a hands-on session to help them reach python (beginner's level) and then progress into Machine Learning. The topics taught on each day is provided below:

(Each Notebook read time - 1 hrs approx)

• Day0.ipynb notebook

  • Installation and Setting of the tools
  • Resources to be used
  • Book to be referred (self study)
  • Assignment

• Day1.ipynb notebook

  • Introducing python and differentiating it from other languages
  • Terminologies of programming language
  • Operators and rules conventions
  • Syntax of python
  • Specific terms used in python
  • Assignment

• Day2&3.ipynb notebook

  • Datatypes
  • Creating Expressions using operators
  • Built-in data stuctures
  • Indtroducing to 30 days coding challenge
  • Assignment

• Day4.ipynb notebook

  • Conditional statements - if, if-else, nested if-else-if
  • Assignment

• Day5.ipynb notebook

  • Loops
  • Use of range()
  • Execution flow during runtime and Debugging
  • Control flow breakers - continue, break and pass
  • Assignment

• revision_week1.ipynb notebook

  • Python as Intepreted language
  • revision of datatypes, operators, conditional statements
  • Debugging activities
  • Assignment

• test_week1.ipynb notebook

  • 8 programming question with solutions

• Day6&7.ipynb notebook

  • High-Order functions
  • Recursion
  • Anonymous function
  • map, filter and reduce function
  • DYI - class & inheritance
  • Class usage programming exercise
  • Generators
  • Assignment

• Day8.ipynb notebook

  • Dynamic programming
    • 0/1 knapsack code
    • Bellman-Ford code
  • Greedy Programming/Algorithm
    • Minimum Spanning Tree code
    • Dijkstra code

• Day9.ipynb notebook

  • Explanation of using numpy and the difference from python sequence
  • Creation of array
  • Attributes associated with ndarray
  • Basic operations
  • ufunc
  • Exercises
  • Cheatsheet
  • Practice set

• Day10.ipynb notebook

  • Use of pandas
  • Creation of pandas object
  • Reading/Saving it different formats
  • Viewing of dataframe
  • Selecting of dataframe
  • Indexing techniques
  • Operating on missing data
  • Statistic operations
  • Reshaping techniques
  • Operating on dataframes using function
  • Pivot tables and related operations
  • Comparison with other tools and analogy functions uses
  • Cheatsheet
  • Exercise

• Day11.ipynb notebook

  • Type of plots/charts used in ML
  • Seaborn
  • Plotly

• test_week2.ipynb notebook

  • 4 + 1 programming sections

• Day12.ipynb notebook

  • Introduction to ML
  • Video explanation
  • Mathematics required for ML
  • Resources
  • Type of learning in ML
  • Reading assignment

• Day13.ipynb notebook

  • Introduction to sklearn
  • Catergories of API in the library
  • Approaches to be used in training a ML model
  • Example of RandomForestClassifier