https://github.com/hritik5102/python-for-beginners
Python Tutorial for beginners
https://github.com/hritik5102/python-for-beginners
algotirhm basic-python-project basic-python-syntax machine machine-learning machine-learning-algorithms machinelearning-python python python-basics python-beginners python-learning-notes python-tutorial python3 pythonforbeginner
Last synced: 11 days ago
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Python Tutorial for beginners
- Host: GitHub
- URL: https://github.com/hritik5102/python-for-beginners
- Owner: hritik5102
- Created: 2019-08-11T13:37:13.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-08-18T10:26:53.000Z (almost 6 years ago)
- Last Synced: 2025-04-01T06:01:54.613Z (about 2 months ago)
- Topics: algotirhm, basic-python-project, basic-python-syntax, machine, machine-learning, machine-learning-algorithms, machinelearning-python, python, python-basics, python-beginners, python-learning-notes, python-tutorial, python3, pythonforbeginner
- Language: Jupyter Notebook
- Homepage:
- Size: 413 KB
- Stars: 14
- Watchers: 1
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Python for beginners
- [Python Basic](#PythonBasic)
- [Type](#Type)
- [Inbuilt-Function](#Inbuilt-Function)
- [Math function](#bold)
- [If - Statement](#strike-through)
- [Format](#horizontal-rule)
- [While loop](#blockquote)
- [for loop](#link)
- [List](#unorder-list)
- [Basic Inbuilt - Function](#order-list)
- [tuple](#image)
- [set](#image)
- [map](#image)
- [lambda](#image)
- [filter](#image)
- [Unpacking](#GIF)
- [Enumerate](#GIF)
- [Dictionaries](#code-blocks)
- [Function](#tables)
- [Parameter and Arguments](#task-list)
- [keyword argument](#code-blocks)
- [Exception](#tables)
- [Class](#task-list)
- [Inheritance](#task-list)
- [Module](#code-blocks)
- [Packages](#tables)
- [Random-Function](#task-list)
- [Inheritance](#task-list)
- [Files and Directories](#code-blocks)
- [Working with spreadsheet](#tables)
### Basic of Machine Learning
#### Steps :
1) Import the Data
2) clean the Data
3) split the Data into training/test sets
4) create a model
5) train the model
6) make prediction
7) Evaluate and Improve
### Real world problem
- [Importing a data set](#task-list)
- [Importing Data ](#task-list)
- [Learning and Predicting](#task-list)
- [ Calculating the Accuracy](#task-list)
- [Model Persistance](#task-list)
- [Visualizing a Decision Tree](#task-list) -