https://github.com/syamkakarla98/machine_learning_course
This course covers the explanation and examples on supervised and unsupervised algorithms.
https://github.com/syamkakarla98/machine_learning_course
algorithm hacktoberfest hacktoberfest2019 jupyter-notebook machine-learning matplotlib neural-network numpy pandas python python3 scikit-learn scipy seaborn supervised-machine-learning tensorflow tutorial unsupervised-machine-learning
Last synced: 2 months ago
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This course covers the explanation and examples on supervised and unsupervised algorithms.
- Host: GitHub
- URL: https://github.com/syamkakarla98/machine_learning_course
- Owner: syamkakarla98
- License: mit
- Created: 2019-07-21T07:50:52.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-13T05:44:36.000Z (over 5 years ago)
- Last Synced: 2025-01-12T07:46:39.811Z (4 months ago)
- Topics: algorithm, hacktoberfest, hacktoberfest2019, jupyter-notebook, machine-learning, matplotlib, neural-network, numpy, pandas, python, python3, scikit-learn, scipy, seaborn, supervised-machine-learning, tensorflow, tutorial, unsupervised-machine-learning
- Language: Jupyter Notebook
- Size: 38.5 MB
- Stars: 2
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Machine Learning Course
## Overview
This repository holds the contents of machine learning course. This course helps you to gain the basic and sound knowlege in machine learning domian. It is an one stop to all machine learning concepts where every algorithm is explained with examples, sample codes in jupiter notebook. At the end of the course you will be able to build a real time application in Machine Learning domain.
## What is Machine Learning?
> Machine learning is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
## What will I learn from this course?
* How to apply Supervised and Unsupervised algorithms for different applications.
* Hands on Experience on Machine learning Algorithms.
* Developing a real time applications in Machine Learning domain.
## Prerequisites
The things that you must have a decent knowledge on:
```
1. Python
2. Linear Algebra
3. Machine Learning Terminology
```
## Dependendicies
```
python 3.6.x
```
## Installation
* Clone this repository:
``` bash
git clone https://github.com/syamkakarla98/Machine_Learning_Course.git
```
* Or click here to download this repository: [Click Here](https://github.com/syamkakarla98/Machine_Learning_Course/archive/master.zip)
* Goto Machine_Learning_Course folder:
``` bash
cd Machine_Learning_Course
```
* This project is fully based on python. So, the necessary modules needed for computaion are placed in setup.py:
``` bash
pip install -r setup.txt
```
## How to use
* Go to the directory and use below command to access the jupyter notebooks.
```bash
jupyter notebook
```
## Authors
* [**Syam Kakarla**](https://github.com/syamkakarla98)
* [**Chandu Siddartha**](https://github.com/siddartha19)
## License
This project is licensed under the MIT License - see the [LICENSE.md](https://github.com/syamkakarla98/Machine_Learning_Course/blob/master/LICENSE.md) file for details.