A Brief Introduction to Machine Learning and Supervised Learning
By: Ashutosh Virwani
Tags: Regression, Supervised Learning, Basic Machine Learning, Classification Algorithms, Machine Learning

What is Machine Learning?

Machine Learning is the study of algorithms and techniques that are used to train the computer using examples so as to make it capable of taking decisions.

These examples are also called “training data-sets”.

Machine learning is a part or a sub-set of Artificial Intelligence (AI). It is an implementation of AI technology. Although Artificial Intelligence is a broader concept that aims to create machines intelligent enough to take human-like decisions.

Machine Learning is used in data analytics to form algorithms that take better decisions using predictive models, also known as predictive analysis. This type of analysis takes historical data to analyse the trends in data.

Applications of Machine Learning

1. Image Recognition

This is one of the most common application of machine learning. As the name suggests the algorithms used in this are for identification of people, places, objects, etc.

2. Speech Recognition

This application is yet another useful one in day-to-day life. Apple’s Siri and Google’s Alexa come under speech recognition, these help us carry out a lot of tasks like setting a timer on your phone or making a phone call, without having to unlock our phones.

3. Recommendation Systems

This application is predominant in e-commerce companies like Amazon and also entertainment companies like Netflix, as these systems are used to suggest products to the users. You must’ve observed that after a few days of searching a particular product, you get recommendations of that products or related products on Instagram.

4. E-mail Spam Detection

When we receive an e-mail, it is automatically divided into important or spam. This is done with the help of algorithms like Decision Trees and Naïve Bayes classifiers.

5. Online Customer Support

Sometimes when we visit a site, we see a chat-box pop-up, this usually a chatbot which helps us navigate through that particular site and also solve the queries we have. These chatbots use all of the information available on the site to provide with the best responses to our queries.

6. Video Surveillance

It is difficult for a single person to monitor a number of CCTVs at the same time, and even tougher to remember everything that they have observed. With the help of machine learning algorithms, we can teach the computers to create alerts when they observe a usual activity in an area to avoid to avoid any accident that could possibly happen.

7. Self-driving Cars

Tesla cars are a great example of self-driving cars. These cars have systems that use unsupervised learning algorithms to detect the objects or people around it while driving. It is a science fictional dream come true.

8. Fraud Detection

Machines keep a track of every transaction and every ID when there is an online transaction taking place. The Feed Forward Neural network helps us determine whether the transaction that is taking place is a genuine one or a fraud.

What are the types of Machine Learning?

There are three main types of Machine Learnings namely Supervised Learning, Unsupervised Learning and Reinforcement Learning.

· In Supervised Learning, the machine learns how to calculate the input values in the dataset when outputs are provided.

· In Unsupervised Learning, the machine doesn’t have a labelled dataset to learn from. This means that in unsupervised learning the machine learns to form new labels on its own for that dataset.

· Reinforcement Learning is a type of machine learning that looks after the quality of decisions taken by an algorithm to get the best out of the data provided.

Supervised Learning

Supervised machine learning is a very common and largely used subbranch of machine learning; and a big part of practical machine learning is using supervised learning techniques. This algorithm uses historical data for analysis and form trends from it to gain valuable information and insights. The basic goal is to approximate the mapping functions with accuracy so high that when we provide the algorithm with a new input value (x), it should be able to predict the output value (y) for that provided data. The mathematical equation for that is Y = f(x).

How does Supervised Machine Learning work?

In the process for supervised learning the stages of learning and testing are the two brief stages involved. Learning process in supervised learning means learning a model using the training data or to train a model using the training data. Testing process is to test the model using new data in order to calculate the model accuracy.

The steps followed during this are-

· Preparing data for analysis because messy data does not provide us with accurate results.

· Choosing an algorithm to work with and solve crucial problems we face during the working of that project.

· Fitting a model involves three steps- the function, the error function and parameters so that we get minimum difference.

· Choosing a validation method according to the type or nature of the data.

· TUUM or test update use model which basically involves examining the model fitting and updating it until we get satisfactory results.

· Output of the model in what decides the actual accuracy of the model when provided with new input-output variables as training data.

Supervised learning can be divided into two main subcategories namely Classification and Regression.

Classification

In classification algorithm we give data points with an assigned category during training and the job of the algorithm is to take an input value and assign that in a class or category, that it fits into, and this is based on the training data that is provided to our algorithm.

The most common application of classification is determining whether an email is spam or not spam. When training the model, we give the algorithm both spam and not spam emails so that it can find features within the given data and correlate those to create a mapping function based on the function we saw earlier Y = f(x). And when we provide this algorithm with unseen emails it uses the data from previous emails to classify the received emails as spam or not spam.

There are a number of ways in which a classification problem can be solved but a few of these algorithms are-

· K-nearest neighbor (KNN)

The KNN algorithm is based on the assumption that similar things or objects are close to or next to each other. This algorithm is simple and easy to implement.

· Decision Trees

In decision trees we create a model that can be used to predict the class or value of the desired variable by learning simple decision rules using data from the prior observations (training data).

· Naïve Bayes

This classification algorithm based on the Bayes’ Theorem and with the assumption of predictors have independence amongst them. This technique is based on the assumption that the feature(s) present in class is/are not related to any other feature(s) present in that same class.

· Support Vector Machines

In this algorithm we plot data items as a point in n-dimensional space where n denotes the number of features and the value of every features is the value of a particular coordinate. Then by performing classification we find the hyperplane that differentiates the two classes.

· Random Forest

In random forest we have a number of decision trees that work as an ensemble and with each individual tree giving out a class predictor, the class with the maximum number of votes becomes our model’s predictor.

Regression

Regression models are used to predict responses that are continuous for example the temperature change, the sales of a particular product or the test scores of an individual student. These algorithms use models which attempt to find the important relationship between the dependent and the independent variables.

For multiple features the mathematical equation is-

y = w [0] * x [0] + w [1] * x [1] + …. + w [i] * x [i] + b

In this equation x [i] represents the feature(s) of the data while w [i] and b are the parameters that are emerged when training the model.

For single feature in data the mathematical equation is-

y = wx + b

In this equation w represents the slope, x is the feature and b represent the y-intercept.

There are quite a few regression techniques but the three most common ones are-

· Linear Regression

In linear regression the model attempts to determine the relationship between two variables, one of them is a dependent one and the other one is an independent variable. This is done by fitting a linear equation to th observed data.

· Logistic Regression

Logistic regression is used when the dependent variable in the data is binary. In this regression we find out the relation between one binary variable with other variable of any type.

· Polynomial Regression

In polynomial regression the value of the relationship between the dependent and the independent variable is the modeled as the n’th degree polynomial. This is a modified version of linear model to get more accurate results.

Summary

In this blog you learned the definition and the applications of machine learning. You learned the definitions of the three types of machine learning. This blog also told you about the basics of Supervised Learning and the types of supervised learning. You know about-

· Classification techniques

· Regression techniques