Machine Learning Algorithms

"Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal" - Eric Schmidt (Google Chairman)

This repository was created for shown some algorithms of machine learning. It's important you have a High-Level Python to understanding of various machine learning algorithms. These should be sufficient to get your hands dirty.

Description About this Repository

Basically this repository is defined in two folder. The folder represents the subarea of machine learning that is supervised learning, and unsupervised learning.

Bellow, I describe the files of the each folder, in other wold the supervised and unsupervised learning algorithm's.

Supervised Learning:

• Linear Regression;
• Logistic Regression;
• Logistic Regression applied to Cat Dataset ;
• Random Forest applied to Mnist Dataset;
• XOR gates Using NN;
• Neural Network applied to Cat Dataset ;
• Deep Learning Apply to Titanic Dataset.

Unsupervised Learning:

• Apply PCA Techniques to Titanic Dataset;
• Using K-means Clustering to Image Segmentation.

Life Cycle of Machine Learning

For all these techniques, the following order was maintained:

1. Collecting Data
2. Analysis Data
3. Data Wrabling
4. Test & Train
5. Accuracy Check

Getting Started

Prerequisites

What things you need to undertand this repository

 Good knowledgment in Machine Learning, Deep Learning, Computer Vision and know how to use jupyter-notebook.

Usage

To use some algorithms, like Deep Learning applied to coin brazillians, you need a good computer system with GPU (Graphic Processing Unit).

Sincerely: Neemias B. Silva