Introduction To Machine Learning

Before starting machine learning, let us discuss some terminologies frequently used in machine learning.

In a simple way, we shall say that learning is a process of converting experience into knowledge or expertise. Thus, in machine learning, we wish to write programs so that a computer can learn from the available inputs. Hence, the input to a learning algorithm is considered a training dataset. Once an algorithm is designed and tuned for the given input to achieve maximum accuracy, we say that the model is built. Hence, we can say

model = algorithm(input data)

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Why Machine Learning?

In machine learning, experts develop general-purpose algorithms that can be used on large classes of learning problems. To solve a specific task you only need to feed specific data to the algorithm, in such a way that you are programming by example.

A computer uses this data as its source of information and compares the output with the desired output and tries to maximize the accuracy.

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System Modeling

System modeling is very important while machine learning is applied in a field of study. Generally, the modeling phase consists of the following steps:

• Feature Engineering (feature normalization and feature selection etc.)
• Algorithm selection
• Training, model validation, and model selection
• Applying the trained model to unseen data

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Learning Problems

There are a large number of learning problems, which can be categorized based on the objectives of the problems. Here, we are categorizing the problems as follows:

Regression: If you are trying to predict a real value based on the given past performance, probably a new need to apply regression. For example, predict the value of a stock tomorrow given its past performance.

Binary Classification: If you are trying to predict a simple yes/no response, probably you need binary classification. For example, predict whether a user review of the new product is positive or negative about the product.

Multiclass Classification: If you are trying to put an example into one of a number of classes, you might apply multiclass classification. For example, predict whether a news story is about entertainment, sports, politics, religion, etc.

Ranking: If you are trying to put a set of objects in order of relevance. For instance, predicting what order to put web pages in, in response to a user query.

Happy Exploring!