Getting Started with machine learning

Source: Internet
Author: User

What is machine learning?

Using computers to find out the rules of historical data and using them to make decisions about uncertain future scenarios

Learning mode?

Offline learning

Online learning

Typical application of machine learning?

Association rules: "Beer + diaper"

Cluster: Global (for frequent global travel people), dynamic zone (for students with more traffic in school), Shenzhou (for workers, white-collar often make long-distance calls)

Naive Bayes: Junk mail

Decision Tree: Credit card fraud

CTR estimate: Internet Advertising

Collaborative filtering: Recommended systems

Natural Speech processing: Emotion Analysis and entity recognition

Deep Learning: Image recognition

More applications:

Speech recognition

Personalised Medical

Sentiment analysis

Human Face recognition

Automatic driving

Smart Robot

Private Virtual Assistant

Gesture Control

Automatic video content recognition

Real-time Machine translation

Data features

Transaction data (using relational data) vs behavioral data (development of NOSQL data)

Small amount of data vs massive data

Mining volume analysis vs full-scale analysis

Data analysis (Reporting on past events), analytical methods (user-driven, interactive analysis), participants (data analyst: Data + algorithms)

Machine learning (predicting future events), analytical methods (data driven, automatic knowledge discovery), participants (algorithms)

Algorithm Classification (1):

Supervised learning: Classification algorithm, regression algorithm

Unsupervised Learning: Clustering

Semi-supervised learning: children walking

Algorithm Classification (2):

Classification and regression

Clustering

Marking

Algorithm Classification (3):

Build model

discriminant mode

  

Getting Started with machine learning

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