Supervised Learning Algorithms - MindMap

ML Task: 1 Identification of Fraudlent/ Fake User Ids in a Social Network

Identification of Fraudlent/ Fake User Ids in a Social Network

For Example: Facebook

Objective:
To identify fake user ids (anonymous, fake, uncommon names) in a network.

Briefly describe the task.

Choose Machine Learning algorithm on your own, and discuss briefly how it is effectively used in the identification.

Any Questions and Queries kindly let me know

Surprising Machine Learning Applications

Surprising Machine Learning Applications

1.      Medical Diagnosis

2.      Climate Change

3.      Robotics

4.      Automatic car driving

5.      Protecting Animals

6.      Mobile applications

7.      CyberSecurity

8.      detecting fraud at banks

9.      identifying which consumers are most likely to respond favorably to: direct mail, Groupon deals, Facebook advertisements

10.  trading stocks and derivatives

11.  pricing insurance premiums (Google recently funded a startup that helps natural disaster insurance companies determine prices based on weather patterns due to global warming)

12.  identifying human genes that make people more likely to develop cancer

13.  predicting housing prices for real estate companies

14.  predicting wine-tasting ratings

15.  programmatically reading text from a random photograph

16.  programmatically recognizing faces

17.  anti-virus software (packet inspection)

18.  factory maintenance diagnostics

19.  delayed airplane flights

20.  determining which voters to canvass during an election

21.  developing pharmaceutical drugs (combinatorial chemistry)

22.  predicting tastes in music (Pandora)

23.  predicting tastes in movies/shows (Netflix)

24.  search engines (Google)

25.  predicting interests (Facebook)

26.  predicting other books you might like (Amazon)

 Add more applications on any areas including NLP, Text Mining, Sentiment Analysis, E-Learning, etc. There are many tasks inside each application. Say for example,

Medical Diagnosis have

1.     Predicting Emergency Room Wait Times
2.   Identifying Heart Failure
3.    Predicting Strokes and Seizures
4.   Predicting Hospital Readmissions 

Expecting more applications from you people. Add more to this list. 

Machine Learning - Five different areas

Machine Learning - Introduction

As we all know the basics of Machine Learning (ML) and thus we directly continue with the five important and different areas of ML. The diagram below shows the five areas of ML.

Five different areas of Machine Learning

Let we discuss each area and its applications (different fields (real time) where each type is popular) in the future posts.