What is Machine Learning?

What is Machine Learning?

 

Artificial Intelligence has brought the computer closer to humans by inventing Machine Learning. Machine Learning as the term itself suggests refers to providing a machine with the capability to learn. It develops programs to later understand the data by itself and run accordingly. 

Major Divisions of Machine Learning 

Supervised Learning: 

1. 
   

   Labelled or categorised data is fed to the machine.

   
   


2. 
   

   Both inputs and outputs are fed to the machine for future learning and prediction

   
   

Unsupervised Learning:

1. 
   

   Unlabelled or raw data is fed to the machine.

   
   


2. 
   

   Only, the input is fed to the machine and in future, the machine is expected to associate and deliver results. 

   
   

Semi-Supervised Learning:

1. 
   

   A small amount of labelled and a large amount of unlabelled data is fed to the machine.

   
   


2. 
   

   The machine with the help of labelled data trains the unlabelled data and produces pseudo-labelled data.

   
   

Reinforcement Learning:

1. 
   

   No data is fed to the machine.

   
   


2. 
   

   The machine is programmed to run on trial and error method and is also expected to learn from its mistakes.  

   
   

 

Commonly used ML Algorithms

Firstly, divided into two categories:

Regression: ML uses various algorithms as a statistical approach to getting the desired results. And; one such category is Regression where a link is established between the variables obtained from the data-set to predict a numeric output. This approach is highly applied while predicting results on stock market price, weather etc.. 

 

Classification: As the name suggests, the classification category will cater to the variables that are presented in the form of categories and the results drawn from this form of approach will highlight conclusion/ conclusions as outputs. For eg: if the presented data belongs to the category “mails” then the machine may further provide results in forming sub-categories like “spam” or “not spam” or how many of them are spam or not. 

 

These two ML algorithms are further subdivided as:

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  Linear Regression: It is a linear approach to model the relationship between two variables by fitting a linear equation to observed data.

  
  


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  Logistic Regression: It is used to describe the relationship between a binary response variable and a set of predictor variables.

  
  


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  Decision Tree: It is a data structure consisting of internal nodes as attributes, branches as outcomes, and leaf nodes as decisions.

  
  


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  SVM: It is a supervised machine learning model to maximize the margin between the data points and the hyperplane.

  
  


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  Naive Bayes: These algorithms are based on Bayes’ Theorem used for binary and multi-class classification problems.

  
  


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  kNN: The k-nearest neighbour algorithm is used to solve classification and regression problems. It assumes that similar things exist in close proximity.

  
  


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  K-Means: K-means clustering is an iterative algorithm that aims to partition n observations into k clusters. It is a method of vector quantization.

  
  


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  Random Forest: It is a classification algorithm consisting of a large number of decision trees that operate as an ensemble.

  
  


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  Dimensionality Reduction Algorithms: These algorithms reduce the number of random variables under consideration by obtaining a set of principal variables.

  
  


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  Gradient Boosting algorithms: It is a greedy algorithm and one of the most powerful techniques for building predictive models.

  
  

 

Why does Machine Learning matter?

Potential to create an impact on the business world

Helping hand for a social cause

Low gestation period

Leading digital innovation

Answering complex questions using data sets

Real-World Applications of Machine Learning:

Fake News Analysis: Many times, a hoax- news is spread like a wild-fire and people generally start believing it without checking the reliability status of the spread information. Machine Learning has made it possible to detect fake news but the accuracy of the result still depends on the database fed to the machine.

Autonomous Driving: An autonomous unmanned ground vehicle or a self-driving vehicle is a vehicle capable of driving by itself with negligible human intervention. Machine Learning algorithms have the potential to harness this technology.

Product Recommendation: A Product Recommendation System is a software tool designed to provide suggestions for content a specific user would like to purchase or engage with. These systems are one of the most successful and widespread applications of machine learning in business.

Intelligent Process Automation: Intelligent Process Automation is a set of emerging technologies that combines fundamental process redesign with Robotic Process Automation and Machine Learning. It is a way to manage, automate, and integrate digital processes.

Facial Recognition: Need a software tool to enhance any security system through face recognition? AI and ML have got you covered. It is benefiting not only the organisations but is also approved for personal security. 

 

Benefits of Machine Learning:

For Everyday Personal Use

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  Virtual Personal Assistant

  
  

Connect to your machine who acts as a personal assistant for you. 

 

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  Predictions making your commuting easy

  
  

Two major ways by which Machine Learning is making your commuting easy are traffic prediction and online cab booking with price details. 

 

 

For Giving New Heights to Your Business

 

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  Building a Secure Environment

  
  

Contributing to increasing security levels by taking one step forward with ML security assured products.

 

 

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  AI Chatbots

  
  

Making your business services user-friendly by being available to solve first-level queries.  

 

5 Steps:

1. 
   

   First of all, data is collected.

   
   


2. 
   

   Performing dimensionality reduction on collected data.

   
   


3. 
   

   Learning stage of the algorithm.

   
   


4. 
   

   Model testing for performance evaluation.

   
   


5. 
   

   Maximizing performance through tuning

   
   

Where does India stand?

The count of our total research papers in ML is 745 which is even less than the number of papers publishedAted by a world-class university in China. India needs to improve its learning capabilities to become a significant contributor in this field. It doesn’t require much infrastructure. A laptop with appropriate software installed is all that it takes. Startups in India are the front leads in business innovation. They have to keep track of their speed of innovation and disruption. A lot is required to be invested in academic and industrial research. Time to run faster now!

Technology that provides you support with ML Algorithms:

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  Keras, PyTorch, Tensorflow, Azure ML, Amazon SageMaker, SAS, R, MatLab

  
  

 

ML Platforms:

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  Azure Machine Learning, Data Preparation, Google Machine Learning