What Is Machine Learning (ML)?

Machine learning is a subset of Artificial Intelligence that deals with self-programming applications, which use existing data to profoundly improve their accuracy without any human intervention.

Machine Learning provides the system with the capability to interpret training datasets, find patterns, and then automatically learn from experience. These models are capable of forecasting future outcomes from present observations. In the province of ML, there’s deep learning, algorithms, big data, and a lot of self-learning.

An ‘Algorithm’ in Data Science is a set of sequential and statistical steps. Algorithms, developed by data scientists, train the machine to find patterns and features recurring in a massive amount of data. The accuracy of these algorithms increases with time, thus increasing an organization’s core operation’s efficacy.

How Does Machine Learning Work?

Typically performed by data scientists, these steps are done by working closely with the professionals, for whom the model is intended. The steps are as follows:

Step 1Data Collection and Normalization 

This step includes collecting a set of data for the machine to use as primary data, for solving the problems that it is designed for. This data is called ‘Training Data’. Data Normalization, generally a daunting task for data scientists, includes separating relevant and unbiased data from data silos, keeping in mind the intended problem to be solved. The training data is divided into two subsets: the training subset, which will be used to train the application, and the evaluation subset, used to test and refine it.

Step 2Training and Testing the Model 

In this step, the model puts to use, the training data with different variables, to test the model and get an output. Then, this output is compared to the expected output to check the model’s efficiency. This process is repeated several times, to train the model for different variables and data, till we get an accurate output most of the time.

Step 3Deployment and Improvement of the Model 

The final step is to deploy the model for use in the real world for it to improve in accuracy and effectiveness working on new data over time. The new data is generally taken from the vendors whose problems you intend to solve. For example, an ML model designed to identify spam will ingest email messages, whereas an ML model that is intended for personalization will ingest the search history of the organization’s target audience. With time, errors spotted can be corrected resulting in improvement in the algorithm’s efficiency.

How Can ML Prove to be a Boon?

Technologies like ML, AI, Big Data, and Deep Learning have only raised the bar for efficiency, ever since they got into existence. From Tech Giants like Google and Apple using ML for image and voice recognition, the technology now has its roots in various domains and applications. Key areas where ML can help are:

1. Customer Acquisition:  

  • Deals Recommendation Tools: These tools provide personalized recommendations to users based on their past product preferences. Ex: ‘Collaborative Filtering’ tool, that filters out all people who have the same preference as you under one category and then serves you with the items that most of them liked.
  • Contextual Online Advertising: ML models can interpret the content of a web page, including the reader’s reviews and the author’s attitude. It then helps in serving the target audience with advertisements tailored to the visitor’s previous searches and interests.
  • Data Mining: Utilization of specialized data mining algorithms to analyze and improve business processes. SEO, a popular tactic these days, depends heavily upon data mining. The process involves pulling data from Google Analytics, Webtrends, and other tools. A famous application of it is Product Search Ranking.

2. Customer Support:

  • Speech Analytics Software: Speech analytics software transcribes data from phone calls, e-mails, and text conversations and processes it to interpret the real-time emotions of the customers. This helps companies to gain an insight into how customers feel or think about their products or services.
  • Virtual Customer Assistant (VCA): VCA’s can be thought of as advanced chatbots, that won’t just respond to messages but crawl through existing data and offer assistance on a wide range of requests. These ML-powered assistants use a combination of pattern recognition, natural language processing, and deep neural networks to interpret input text and provide a relevant response.
  • Translations and Voice Analysis: Siri, Google Assistant, Cortana are intelligent assistants that fall into the category of Conversational AI. These are powered by Natural Language Processing (NLP), which is an ML application. It enables computers to process text and voice data and respond to us as normal humans would. Translation software uses a fraction of deep learning known as Neural Machine Translation (NMT), which processes sentences as a whole to translate them.

3. Employee Help:

  • Payroll Management Systems: The existing manual system of payroll had collection and distribution of a large amount of data and large sums of money, done at the hands of the risk of humans. By automating the process using ML algorithms, there is a decrease in discrepancies, an increase in speed, and cost reduction.
  • Predictive Analytics Tools: This branch of analytics uses ML to predict the kind of problems that could arise in near future based on the data of the past. It can also be used by sales strategists to foresee the public’s purchasing trends and thus make data-driven decisions.
  • Job Advertising: ML helps in the interviewing procedure, by tracking the visitors of the job posting website. It also automates the candidate sourcing procedure by filtering resume databases to analyze suitable candidates.

4. Security:

  • Fraud Detection: ML regression and classification models process a large amount of data to find hidden correlations between user behaviour and fraudulent actions. This makes the process easier and more reliable than the existing one.
  • Cybersecurity: ML can extract intelligence from a big pile of incident reports to identify potential threats, advise security analysts, and accelerate the response process. To such tasks, spam filters are the first ML approach.

5. Customer Modelling:

  • Customer Segmentation: ML algorithms like clustering and classification algorithms can be used to identify potential customers. This is done by feeding information like the demographics, browsing behaviour, and affinity of visitors to the algorithm in the form of data to be worked on. This personalized marketing tactic has proved to be more effective at boosting sales.
  • Lifetime Value Modelling: These models use Linear Regression to identify the most spending and/or loyal customers from the list of all customers. This helps in retaining customers that add value and profit to the organization. These models also help in predicting the future revenue that these customers might bring.
  • Churn Modelling: These models help in identifying the customers that are expected to stop engaging with your organization. This might help in devising policies that would help you retain customers by giving discounts or by email marketing.

Conclusion:

ML holds a chest of benefits for those who understand its application in their businesses. As Big Data continues to grow and data scientists continue to develop more powerful algorithms, ML will be able to analyze more complex data and provide faster and accurate results. In the end, ML can only help organizations to augment the ‘Customer Centric Reality’ and optimize the decision-making process, and not in the facets where the human brain is needed.

 “The Greatest benefit of machine learning may ultimately be not what the machines learn but what we learn by teaching them.” ― Pedro Domingos, author of the book namely ‘The Master Algorithm.’