https://github.com/vaibhavs10/kaggle-titanic

An open cheat sheet which goes in somewhat detail in understanding the Machine Learning concepts and some code :)
https://github.com/vaibhavs10/kaggle-titanic

Last synced: 17 days ago
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An open cheat sheet which goes in somewhat detail in understanding the Machine Learning concepts and some code :)

• Host: GitHub
• URL: https://github.com/vaibhavs10/kaggle-titanic
• Owner: Vaibhavs10
• Created: 2016-07-14T16:45:10.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2016-08-06T07:52:03.000Z (over 8 years ago)
• Last Synced: 2023-08-26T05:22:32.304Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 506 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
#Machine Learning Cheat Sheet

There are a lot of cheatsheets, blogs, books, repos, etc online but I decided to take my own take on it and make something of my own which gets the best of both the worlds!

In case of any doubts, corrections or suggestions, I am available at: [email protected]

___

###What is Machine Learning?

**Machine learning** is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

In a very broad perspective, it gives the computer an ability to learn something without explicitily being programmed.

###Types of Machine Learning?

1. Supervised Learning

2. Unsupervised Learning

3. Reinforcement Learning

**Supervised Learning** - It is an algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

This is one of the most common type of ML.

**Unsupervised Learning** - It is the task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution.

**Reinforcement Learning** - It is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

###Most commonly used Machine Learning techniques:

1. Linear Regression

2. Logistic Regression

3. Decision Tree

4. Support Vector Machine (SVM)

5. Naive Bayes

6. K-Nearest Neighbors (KNN)

7. K-Means

8. Random Forest

9. Dimensionality Reduction

10. Gradient Boost and Adaboost

*Check the attached IPython notebook for in detail exploration of these Algorithms!*