Introduction To Machine Learning

Content

• Introduction

• Terminology

• Process

• Machine Learning Approaches

Introduction

What Is Machine Learning?

Machine Learning is the science  of programming computers so they can learn from data.

Here is a slightly more general definition:

Machine Learning is  field of study that gives computers the ability to learn without being explicitly programmed.

Arthur Samuel, 1959

And a more engineering-oriented one:

A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Tom Mitchell, 1997

For example, your spam filter is a Machine Learning program that can learn to flag spam given examples of spam emails (e.g., flagged by users) and examples of regular (nonspam, also called “spam”) emails. The examples that the system uses to learn are called the training set. Each training example is called a training instance (or sample). In this case, the task T is to flag spam for new emails, the experience E is the training data, and the performance measure P needs to be defined; for example, you can use the ratio of correctly classified emails. This particular performance measure is called accuracy and it is often used in classification tasks.

Terminology

• Data set :  A set of example which have some important features which helps for solving problems.
• Features : Features is an important part of data which help to understand a problem.and because of features algorithm learn correctly and gives proper output.
• Model : The representation (internal model) of a phenomenon that a Machine Learning algorithm has learnt. It learns this from the data it is shown during training. The model is the output you get after training an algorithm. For example, a decision tree algorithm would be trained and produce a decision tree model.

  Process

• Get Data : Collect the raw data from real world so that algorithm learn form that data.

• Clean Data : Make raw data in proper format remove outlier,deal with empty fields,perform EDA, make data balance so that accuracy of prediction increase.

• Train Model :  Also known as the fitting stage, this is where the Machine Learning algorithm actually learns by showing it the data that has been collected and prepared.

• Test Data : Check the trained model gives correct output or not on given test data.

• Improve Model : Improve our train model by tuning parameters so that it perform well with high accuracy.

Machine Learning Approaches

There are so many different types of Machine Learning systems that it is useful to classify them in broad categories based on:

• Whether or not they are trained with human supervision (supervised, unsupervised, semisupervised, and Reinforcement Learning)
• Whether or not they can learn incrementally on the fly (online versus batch learning)
• Whether they work by simply comparing new data points to known data points, or instead detect patterns in the training data and build a predictive model, much like scientists do (instance-based versus model-based learning)