Introduction To Machine Learning:

Machine learning is the sub-field of computer science, focuses on the development of computer programs that can change when exposed to new data. Machine learning system searches through data for a pattern and adjust program actions accordingly.

Facebook's News Feed uses machine learning to personalize each member's feed. If a member frequently stops scrolling in order to read or "like" a particular friend's posts, the News Feed will start to show more of that friend's activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user's data and use to patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend's posts, that new data will be included in the data set and the News Feed will adjust accordingly.

Machine learning is a method of:

       ·    data analysis that automates analytical model building.

       ·   Using algorithms that iteratively learn from data,

       ·   find hidden insights without being explicitly programmed where to look. 

Traditional Programming: Data and program is run on the computer to produce the output. Machine Learning: Data and output is run on the computer to create a program. This program can be used in traditional programming.

Applications of Machine Learning:        

• Web search: ranking page based on what you are most likely to click on.
• Computational biology: rational design drugs in the computer based on past experiments.
• Finance: decide who to send what credit card offers to. Evaluation of risk on credit offers. How to decide where to invest money.
• E-commerce:  Predicting customer churn. Whether a transaction is fraudulent.
• Space exploration: space probes and radio astronomy
• Robotics: how to handle uncertainty in new environments. Autonomous. Self-driving car.
• Information extraction: Ask questions over DB across the web.
• Social networks: Data on relationships and preferences. Machine learning to extract value from data.
• Debugging: Use in computer science problems like debugging. Labour intensive process. Could suggest where the bug could be.

Machine Language is a computer program that learns from Experience (E) with respect to some class of Task (T) and Performance measure (P). If its performance with task T as measured by P improves with E.

Example: playing checkers

E —> experience of playing many games of checkers.

T —> Task of playing checkers.

P —> Probability program will win next game.

History Of Machine Learning:

What are the differences between data mining, machine learning and deep learning?

Basic Workflow Model Of Machine Learning:

ML has three phases – training, validation, and testing. To decide which is best model of the alternatives, the selection should be based on the performance of the model against validation data and not on the accuracy on test data set. The following steps are performed:

   1.     Identify the features from training data

   2.     Identify subset of attributes necessary for classification (dimensional reduction).

   3.     Learn model using training data

   4.     Use trained model to classify unknown data, and predict the result accurately.

What is required is, the machine learning (ML) system to train on dataset and act on data which fits a particular pattern find alternate models and select the best fit model. 

Undercutting is result of excessively simple model. 

Overfitting is result of excessively complicated model.

This is first issue of this machine learning tutorial that has been published. In the next issue Machine Learning Algorithms will be discussed. Hope you have a good exposure on what is machine learning in this issue. stay