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)
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- Installation and Setting of the tools
- Resources to be used
- Book to be referred (self study)
- Assignment
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- 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
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- Datatypes
- Creating Expressions using operators
- Built-in data stuctures
- Indtroducing to 30 days coding challenge
- Assignment
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- Conditional statements - if, if-else, nested if-else-if
- Assignment
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- Loops
- Use of range()
- Execution flow during runtime and Debugging
- Control flow breakers - continue, break and pass
- Assignment
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- Python as Intepreted language
- revision of datatypes, operators, conditional statements
- Debugging activities
- Assignment
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- 8 programming question with solutions
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- High-Order functions
- Recursion
- Anonymous function
- map, filter and reduce function
- DYI - class & inheritance
- Class usage programming exercise
- Generators
- Assignment
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- Dynamic programming
- 0/1 knapsack code
- Bellman-Ford code
- Greedy Programming/Algorithm
- Minimum Spanning Tree code
- Dijkstra code
- Dynamic programming
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- Explanation of using numpy and the difference from python sequence
- Creation of array
- Attributes associated with ndarray
- Basic operations
- ufunc
- Exercises
- Cheatsheet
- Practice set
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- 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
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- Type of plots/charts used in ML
- Seaborn
- Plotly
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- 4 + 1 programming sections
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- Introduction to ML
- Video explanation
- Mathematics required for ML
- Resources
- Type of learning in ML
- Reading assignment
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- Introduction to sklearn
- Catergories of API in the library
- Approaches to be used in training a ML model
- Example of RandomForestClassifier