A glimpse on Machine Learning

Introduction

Machine learning (ML) is the next instrument in the AI symphony. So what exactly is Machine Learning?

Machine Learning is a subset of AI that provides machines the capability to learn automatically and improve from experience without being explicitly programmed.

In simple words, it helps machines to solve a given problem by gaining the ability to think.

Knowing the ML Terms

Before diving deep into ML, you have to be aware of the following terms.

Algorithm

Algorithm is a set of rules and statistical techniques used to learn and derive insights from data patterns. e.g., Decision tree, Linear Regression, and Random Forest.

ML Model

ML model is a mathematical model trained by an algorithm to predict the patterns in the data.

Predictor Variable

A predictor variable is a variable used to predict another variable/output.

Response Variable

Response Variable is the target variable or the output variable that needs to be predicted.

Training Data

A model is built using training data.

Testing Data

The model is evaluated using testing data.

Predictor vs Response Variable

In the scenario where the height of the individual is predicted based on age, the predictor variable will be Age, and the response variable will be Height.

Defining Your ML Problem

If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions. - Albert Einstein

The following questions will aid you in defining a problem:

What kind of problem are you facing?
How much or how many? (Regression)
Which category? (Classification)
Which group? (Clustering)
Is this an outlier? (Anomaly detection)
Which option to opt for? (Recommendation)

Once you have defined your problem, you need to strike the data science chord to get the data prepped up for further processing.

Exploring ML

Let's now dive into the process of Machine Learning!

The ML steps involved in predicting an outcome for a given problem are listed below.

Build the Model
Model Evaluation
Prediction

1. Build the model

This step starts by splitting the data, which is ideally split into

Training data, and
Testing data

Data Splitting follows the rule-of-thumb 80/20 rule. 80% of data contributes to Training set while the remaining 20% is assigned to the Testing set.

Training data is used to train (build) the model using algorithms such as Linear regression and Decision Tree. If you feed more data in the training phase, the model will perform better in the testing phase.

2. Model Evaluation

It's time to put the model you just built to test.

In this step, using the testing data, you will check the predictions of your model, based on which you can tune the accuracy and efficiency of the model.

Parameter Tuning is a popular technique that helps in improving the efficiency of the model.

3. Prediction

In this stage, the model is all set to give predictions for the input you provide.

A Sample Scenario

Using examples to back up statements of fact can add value to your concept.

Predict the occurrence of rain in your area with the ML process by studying the weather condition.

Define the Problem

Possibility of rain (yes or no - Response Variable)

Temperature, Humidity condition (Predictor Variable:)

Data Gathering and Cleaning

Data Source: Meteorological Research Center

Exploratory Data Analysis

Insight 1: High probability of rain, if the temperature has fallen low.

Insight 2: When it rains, the humidity is at 100%.

Build the Model

Use suitable ML algorithms to train the model.

Evaluate the Model and then you are good to predict.

Types of ML

Machine Learning can be broadly categorized into three types, as listed below.

Supervised
Unsupervised
Reinforcement Learning

1. Supervised Learning vs Unsupervised Learning

Supervised Learning - A technique that uses labeled data for training the model. Unsupervised Learning - A technique that uses unlabeled data for training the model.

The GIF shown here portrays this difference.

In supervised learning, upon giving the labelled pictures as input, the model is trained to produce two outputs, namely Popeye and Olive.

In unsupervised learning, when unlabeled pictures are given in, they try to extract the prominent features of the images and group them accordingly.

Imagine unsupervised learning as a type of mathematical version of making “birds of the same feather flock together.”

2. Reinforcement Learning

Imagine yourself dropped off at an island all alone! What would you do? Panic? Yes, of course, I bet you will be.

As time passes, you will learn to live in the environment by exploring the island, climatic conditions, food available, threats, and safety measures. As a result of the learning, you will get adapted to the environment by finding out which fruits to eat and what not to do.

This is an example of Reinforcement Learning, which is defined as, A part of Machine Learning where an agent is put in an environment where he learns to behave by performing actions that will either be rewarded or punished.

AlphaGo is Google's AI Reinforcement Learning example.