https://github.com/dipto9999/ml_introduction
An Introduction to Machine Learning, primarily using Python scikit-learn library.
https://github.com/dipto9999/ml_introduction
data-science decision-trees jupyter-notebook k-means-clustering k-nearest-neighbors linear-regression logistic-regression machine-learning machine-learning-algorithms matplotlib numpy pandas principal-component-analysis python random-forest scikit-learn seaborn support-vector-machines
Last synced: 2 months ago
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An Introduction to Machine Learning, primarily using Python scikit-learn library.
- Host: GitHub
- URL: https://github.com/dipto9999/ml_introduction
- Owner: Dipto9999
- Created: 2020-06-24T08:49:39.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-22T00:12:55.000Z (over 3 years ago)
- Last Synced: 2025-09-08T00:19:34.283Z (10 months ago)
- Topics: data-science, decision-trees, jupyter-notebook, k-means-clustering, k-nearest-neighbors, linear-regression, logistic-regression, machine-learning, machine-learning-algorithms, matplotlib, numpy, pandas, principal-component-analysis, python, random-forest, scikit-learn, seaborn, support-vector-machines
- Language: Jupyter Notebook
- Homepage:
- Size: 7.23 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Support: Support_Vector_Machines/Optimal_Hyperplane.jpg
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README
# Introduction to Machine Learning
## Table of Contents
* [Tutorial](Tutorial)
* [Linear Regression](Linear_Regression)
* [Logistic Regression](Logistic_Regression)
* [K-Nearest Neighbors](K-Nearest_Neighbors)
* [K-Means Clustering](K-Means_Clustering)
* [Decision Trees and Random Forests](Decision_Trees_and_Random_Forests)
* [Support Vector Machines](Support_Vector_Machines)
* [Principal Component Analysis](Principal_Component_Analysis)
## Brief Description
Given access to FreeCodeCamp Website Resources, I have touched up on some fundamental Machine Learning Algorithms.
I installed the Anaconda Data Science Platform to implement some basic Python scikit-learn library
functionalities in Jupyter Notebook.
### Import Statements
All the Machine Learning Algorithms covered in this Introduction import the following open-source libraries.
_____________________________________________________________________________________________________________________________________________
```python
# Import Library for Working with Tabular Data
import pandas as pd
# Import Library for Numerical Computing
import numpy as np
# Import Library for Data Visualization
import matplotlib.pyplot as plt
# Import Another Library for Data Visualizations
# This makes it easier to create beautiful data visualizations using matplotlib.
import seaborn as sns
# matplotlib visualizations will embed themselves
# directly in our Jupyter Notebook. This will make them easier to
# access and interpret.
%matplotlib inline
```
### PDF Report
We are able to generate a PDF Report of the Jupyter Notebook using the following command.
```
jupyter nbconvert ---to webpdf .ipynb
```
These can be found in the Algorithm directories
_____________________________________________________________________________________________________________________________________________
## Source
The information in this repository is derived from a FreeCodeCamp
Article written by Nick McCullum.