Introduction to machine learning

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 explicity programmed where to look.

what is machine learning used for ?

• Fraud detection
• web search results
• new pricing models
• financial modeling
• credit scoring and next best offers
• email spam filtering
• real-time ads on web pages
• pattern and image recognition
• predicting customer churn
• text sentiment analysis
• prediction of equipment failures
• customer segmentation
• recommendation engines

Machine learning process

First of all, you should understand the processes in machine learning by the following diagram.

Data acquisition

There are many ways to get a data set like configuring an API, internet, database, etc. To convert binary data into a useful data, we need to perform certain tasks which includes-Decompress files, Querying relational database, etc.

Data cleaning

The primary goal of data cleaning is to detect and remove errors and anomalies to increase the value of data in analytics and decision making. While it has been the focus of many researchers for several years, individual problems have been addressed separately.

Training and testing data set

The idea of dividing your data set into two subsets:

• training set—a subset to train a model.
• test set—a subset to test the trained model.

You could imagine slicing the single data set as follows:

Model deployment

Deployment is the method by which you integrate a machine learning model into an existing production environment in order to start using it to make practical business decisions based on data. It is one of the last stages in the machine learning life cycle and can be one of the most cumbersome. Often, an organization’s IT systems are incompatible with traditional model-building languages, forcing data scientists and programmers to spend valuable time and brainpower rewriting them.

Supervised learning

• supervised learning algorithms are trained using labeled examples, such as an input where the desired and output is known.
• For example, a piece of equipment could have data points labeled either “F”(Failed) or “R”(Runs).

Unsupervised learning

• Unsupervised learning is used against data that has no historical labels.
• The system is not told the “right answer.” The algorithm must figure out what is being shown.
• The goal is to explore the data and find some structure within.

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