What is Machine Learning?

Machine Learning

When computing problem is broken down without explicit coding, Machine learning happens.This deals with a lot of data. The data has to be looked for patterns that should result in better decisions. Although Machine Learning Algorithms works with massive amounts of data giving accurate and faster results, the training may take a considerable amount of time. Self-driven cars, automatic speech recognition, smart web search are some of the results of machine learning.

Machine Learning Algorithm is categorized into the following

1.Supervised Learning

2.Unsupervised Learning

3.Semi-Supervised Learning

4.Reinforcement Learning

A mathematical model is built out of a set of input data and the expected outputs. The model has a matrix that is identified as the training data. The iterative optimization of an objective function results in supervised learning algorithms that learn a function that can be used to predict the output associated with new inputs. The main two categories of the problem which would be encountered are Regression and Classification. While Regression is for continuous data, Classification has a discrete set. Unsupervised Learning is more about finding a pattern or deriving a structure from the input data rather than calculating an output. A common application is density estimation in statistics.

As the term suggests, semi-supervised learning is a mix of both supervised and unsupervised learning. The training data contains mostly unlabeled data with a few labeled inputs. The co-occurrence of usage of a small amount of unlabeled data with a considerable amount of labeled data ended up in a great improvement in the learning accuracy. So as to maximize some notion of cumulative reward, the extent of machine learning perturbed with the way software representative must take actions in an environment is what reinforcement learning is all about. The commonness of this algorithm takes it to be studied in many other disciplines, which includes game theory, control theory, operations research, etc.

In order to learn in-depth about Machine Learning and its significant concepts, you can also register for Intellipaat’s Machine Learning Course in which you will learn about the various tools and technologies of Machine Learning and become proficient in it.

Machine Learning Tools and Techniques:

Scikit Learn is one of the most commonly used software for classification, regression, clustering and pre-processing. The platforms supported are Linux, Mac OS, windows. The software is an open-source one and can be written in python, c++ and python. Easily understandable documentation is provided. There is a provision of changing the parameters when an object is called, for any specific algorithm. Pytorch is another machine learning tool that is open-sourced with the ability to run on Linux, Windows and Mac OS. It has an Auto grad module and optimum module. It helps creating computational graphs. Ease of use because of hybrid front-end. TensorFlow provides a library for dataflow programming. It also can even help for human pose estimation. Weka, knime are some other programs used for the same purpose.

The process of Machine Learning consists of the following processes.

1. Collecting data samples for training and testing.

2. Cleaning and pre-processing of data

3. Building the model

4.Prediction and evaluation

5.Deploying the model

6.Machine Learning Models

Logical Model-Tree models and Rule models

Logical models make use of the logical expression to separate the instance space into segments and hence construct grouping models. An expression that returns back a Boolean output is termed as logical expression, i.e., a True or False outcome. Once the data is collected together using a logical expression, the data is divided into homogeneous groupings for the problem we are trying to solve. For example, for a classification problem, all the instances in the group belong to one class.

Geometric models

The instance space is separated or partitioned using a logical expression similar to decision trees. The similarity of two different examples results when they end up in the identical logical segment. This part of the section deals with models that define resemblance by considering the geometry of the instance space. Features are described as points in two dimensions (x- and y-axis) or a three-dimensional space (x, y, and z) in geometrical modelling. Features can be modelled in a geometric way even when they are not congenitally geometric(for example, The temperature as a function of time can be modelled in two axes). Similarity can be imposed in two main ways in geometric modelling.

Commonly used Machine Learning Algorithms:

Machine Learning Algorithms

Linear Regression

Linear regression is more about finding a linear function closest to the scattered data. The approach of modeling is linear. The unknown model parameters are calculated from the datasets and the relationship is built using linear predictor functions. Minimizing errors, predictions, and forecasting usually use linear regression models.

Decision tree

Decision trees are supervised learning algorithms usually used for classification types of problems. Surprisingly, it works for both classification and non-discrete dependent variables. Homogenous sets are built out of the population data. The most significant data and independent variables are grouped into as distinct as possible data sets.

Support Vector  Machine:

The SVM algorithm is a classification method. Each item is plotted as a point in n-dimensional space (where n is the number of features you have) with the value of each feature being the value of a particular coordinate.Some can be better understood with an instance of it. A food item having an expiry duration and price if plotted on the graph , the coordinates of it would represent the support vectors.

Naive Bayes

This algorithm is based on Bayes’ theorem. It goes with a supposition of predictors being not dependent on each other. In simple terms, a Naive Bayes classifier considers that a particular feature in a class is unrelated to any other feature. An example would be taken as a car model

Consider a particular car, Maruti alto having features as power-steering, power-brake, four wheels. These features of the car depend on each other. But in this technique of naïve Bayes, the features are independently taken.

Machine Learning has a lot of pros which makes it widely applicable in many fields but it has got its cons too due to the lack of proper data and issues of confidential data. This area of research has mushroomed up to various applications of artificial intelligence and has more to grow. The level of higher accessibility of data would add up the pros of it. If you wish to know more about Machine Learning then visit this Machine Learning Tutorial and for Interview preparation, you can go through this Machine Learning Interview Questions.

Machine Learning Application in Retail Sector:

Machine Learning Application

So, putting Machine Learning in a simple way, it is a technology that is used to construct automated machines. We built these machines for performing the iterative process on the real-time data in order to predict some business outcomes. Since the amount of data generated per day is in quintillion bytes, these Machine Learning models prove to be an asset for the companies. Machine Learning majorly helps in building recommendation systems for e-commerce industries. These recommendation systems prove to be a profit-enhancing tool for businesses. Further, it is also widely used for smart digital marketing for increasing sales. Hence it is very helpful in Retail Sector.

Machine Learning Application in Retail:

Gathering Data for Training the Machine: By the collection of data corresponding to the preference of merchandise and their respective price range the pricing mannequin is pre trained.

Using an Algorithm: Now the retailer also desires to use an algorithm for inspecting the elements of the merchandise referred to in the training statistics and come with the precise prediction about the right rate of the product.

Training the Model for Pricing Optimisation: Now the Pricing optimization model of the algorithm tests the predictions about the proper fee for the patron against the actual product prices.

Changing the Prediction Mechanism: The retail algorithm outfitted with the Machine Learning science continues to alternate and modify the prediction mechanism over time.

Feedback Loop: Whenever a product is offered the fee of the product in that respective sale is viewed as a fresh enter in the feedback loop for training the pricing model to come with more accurate prices.

New Data Inputs: To make use of the pricing optimization model to the benefit of product marketing on a non-stop basis, new product facts is constantly included for the model to refine the fee predictions further.

Machine learning use cases in Retail:

1.Demand Prediction

2.Price Formation



5.Personalized Offers

6.Fraud Detection

7.Churn Prediction

8.Location Optimization

9.Sentiment Analysis

10.Document Work Automation

About the Author

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