Machine Learning

Machine Learning: According to Arthur Samuel, It is a field of study that gives computers the ability to learn without being explicitly programmed. According to Tom Mitchell, It is a well-posed learning problem where he stated that a computer program is said to learn from experience E for some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Example: playing checkers.

E = the experience of playing many games of checkers

T = the task of playing checkers.

P = the probability that the program will win the next game.

In general, any machine learning problem can be assigned to one of two broad classifications:

Machine learning algorithms :

1. Supervised Learning
2. Unsupervised Learning

Supervised Learning

In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.

Supervised learning problems are categorized into “regression” and “classification” problems. In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.

Example 1:

Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.

We could turn this example into a classification problem by instead making our output about whether the house “sells for more or less than the asking price.” Here we are classifying the houses based on price into two discrete categories.

Example 2:

(a) Regression — Given a picture of a person, we have to predict their age based on the given picture

(b) Classification — Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

Unsupervised Learning

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.

We can derive this structure by clustering the data based on relationships among the variables in the data.

With unsupervised learning, there is no feedback based on the prediction results.

Example:

Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.

Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).

What is Supervised Machine Learning?

In Supervised learning, you train the machine using data that is well “labeled.” It means some data is already tagged with the correct answer. It can be compared to learning which takes place in the presence of a supervisor or a teacher.

A supervised learning algorithm learns from labeled training data, helps you to predict outcomes for unforeseen data. Successfully building, scaling, and deploying accurate supervised machine learning Data science models takes time and technical expertise from a team of highly skilled data scientists. Moreover, Data scientists must rebuild models to make sure the insights given remain true until its data changes.

What is Unsupervised Learning?

Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the unlabelled data.

Unsupervised learning algorithms allow you to perform more complex processing tasks compared to supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural learning deep learning and reinforcement learning methods.

Why Supervised Learning?

• Supervised learning allows you to collect data or produce a data output from the previous experience.
• Helps you to optimize performance criteria using experience
• Supervised machine learning helps you to solve various types of real-world computation problems.

Why Unsupervised Learning?

Here, are prime reasons for using Unsupervised Learning:

• Unsupervised machine learning finds all kinds of unknown patterns in data.
• Unsupervised methods help you to find features that can be useful for categorization.
• It takes place in real-time, so all the input data to be analyzed and labeled in the presence of learners.
• It is easier to get unlabeled data from a computer than labeled data, which needs manual intervention.

How Supervised Learning works?

For example, you want to train a machine to help you predict how long it will take you to drive home from your workplace. Here, you start by creating a set of labeled data. This data includes

• Weather conditions
• Time of the day
• Holidays

All these details are your inputs. The output is the amount of time it took to drive back home on that specific day.

You instinctively know that if it’s raining outside, then it will take you longer to drive home. But the machine needs data and statistics.

Let’s see now how you can develop a supervised learning model of this example which helps the user to determine the commute time. The first thing you are required to create is a training data set. This training set will contain the total commute time and corresponding factors like weather, time, etc. Based on this training set, your machine might see there’s a direct relationship between the amount of rain and time you will take to get home.

So, it ascertains that the more it rains, the longer you will be driving to get back to your home. It might also see the connection between the time you leave work and the time you’ll be on the road.

The closer you’re to 6 p.m. the longer time it takes for you to get home. Your machine may find some of the relationships with your labeled data.

This is the start of your Data Model. It begins to impact how rain impacts the way people drive. It also starts to see that more people travel during a particular time of day.

How Unsupervised Learning works?

Let’s take the case of a baby and her family dog.

She knows and identifies this dog. A few weeks later a family friend brings along a dog and tries to play with the baby.

Baby has not seen this dog earlier. But it recognizes many features (2 ears, eyes, walking on 4 legs) are like her pet dog. She identifies a new animal like a dog. This is unsupervised learning, where you are not taught but you learn from the data (in this case data about a dog.) Had this been supervised learning, the family friend would have told the baby that it’s a dog.

Types of Supervised Machine Learning Techniques

Classification v/s Regression

Regression:

The regression technique predicts a single output value using training data.

Example: You can use regression to predict the house price from training data. The input variables will be locality, size of a house, etc.

Classification:

Classification means to group the output inside a class. If the algorithm tries to label input into two distinct classes, it is called binary classification. Selecting between more than two classes is referred to as multiclass classification.

Example: Determining whether or not someone will be a defaulter of the loan.

Strengths: Outputs always have a probabilistic interpretation, and the algorithm can be regularized to avoid overfitting.

Weaknesses: Logistic regression may underperform when there are multiple or non-linear decision boundaries. This method is not flexible, so it does not capture more complex relationships.

Types of Unsupervised Machine Learning Techniques

Unsupervised learning problems further grouped into clustering and association problems.

Clustering

Clustering is an important concept when it comes to unsupervised learning. It mainly deals with finding a structure or pattern in a collection of uncategorized data. Clustering algorithms will process your data and find natural clusters(groups) if they exist in the data. You can also modify how many clusters your algorithms should identify. It allows you to adjust the granularity of these groups.

Association

Association rules allow you to establish associations amongst data objects inside large databases. This unsupervised technique is about discovering exciting relationships between variables in large databases. For example, people that buy a new home most likely to buy new furniture.

Other Examples:

• A subgroup of cancer patients grouped by their gene expression measurements
• Groups of shopper based on their browsing and purchasing histories
• Movie group by the rating given by movies viewers

Model Representation

To establish notation for future use, we’ll use x(i)x^{(i)}x(i) to denote the “input” variables (living area in this example), also called input features, and y(i)y^{(i)}y(i) to denote the “output” or target variable that we are trying to predict (price). A pair (x(i),y(i))(x^{(i)} , y^{(i)} )(x(i),y(i)) is called a training example, and the dataset that we’ll be using to learn — a list of m training examples (x(i),y(i));i=1,…,m{(x^{(i)} , y^{(i)} ); i = 1, . . . , m}(x(i),y(i));i=1,…,m — is called a training set. Note that the superscript “(i)” in the notation is simply an index into the training set, and has nothing to do with exponentiation. We will also use X to denote the space of input values, and Y to denote the space of output values. In this example, X = Y = ℝ.

To describe the supervised learning problem slightly more formally, our goal is, given a training set, to learn a function h: X → Y so that h(x) is a “good” predictor for the corresponding value of y. For historical reasons, this function h is called a hypothesis. Seen pictorially, the process is therefore like this:

When the target variable that we’re trying to predict is continuous, such as in our housing example, we call the learning problem a regression problem. When y can take on only a small number of discrete values (such as if, given the living area, we wanted to predict if a dwelling is a house or an apartment, say), we call it a classification problem