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With the use of Machine Learning, systems can extract meaningful information from a set of raw data. It can be used to obtain solutions to various kinds of business complexities and predict complex behaviors of customers if and when made used to its potential.

What is Machine Learning?

Machine Learning (ML) is a scientific study that allows computers to learn without any external (human) interference. It is an application of artificial intelligence (AI) that provides a computer system the ability to learn patterns and inference automatically. Machine Learning improves the observation of the computer system and focuses on the development of computer programs to access data and use it to learn by themselves without any human/user supervising its action or telling it what to do. ML is one of the most exciting technologies in existence today as it allows a computer to act like humans by possessing the learning trait. However, there is a difference between Machine Learning and Artificial Intelligence. AI is a branch of computer science that emphasizes the creation of intelligent machines that behave and react like humans. Whereas, ML is an ability that enables a computer to learn by itself and use it as acquired knowledge or skill.

Algorithms of Machine Learning:

The varied machine learning algorithms differ in the type of data we input and output, the approach of the process and the type of challenge that is intended for solving. The algorithms are generically diversified as supervised and or unsupervised algorithms.

  1. Supervised and semi-supervised learning

    Supervised ML algorithms a mathematical model of training data that consists of one or more inputs and the desired output. As in the case of semi-supervised learning, the algorithms learn functions that are used to predict the desired output for the inputs provided.

  2. Unsupervised learning

    In unsupervised machine learning, the algorithms use a set of data that contains inputs only to find a structure in the data and learn from the unclassified test data to identify commonalities and react based on the absence and presence of the commonalities of new data.

  3. Reinforcement learning

    Reinforcement learning concerns with software agents and how they take actions in a scenario to increase optimization by discovering errors and rewards and reacting to it.

Most important ways of how ML can help your business are as follows:

  1. Customer Value Prediction

    With the use of Machine Learning, businesses can effectively use customer data and derive meaningful solutions and business insights. For, ML and Data Mining processes help in predicting customer behavior and identifies their taste and preferences by analyzing the purchase pattern. Hence, assembling the retrieved data and then using it to send individual customers with the best possible offers based purely on their history of browsing and purchasing history.

  2. Computer Vision

    Machine Learning can be used for image recognition that has the potential to produce numeric and symbolic information from the images and high-dimensional images that are provided. The method involves Machine Learning, Data Mining, Pattern Recognition and discovery of data. Prediction Management With the correct use of ML, businesses can discover meaningful insights and patterns hidden in the data factory and reduce the risks that are associated with unexpected failures and needless expenses.

  3. Eradicate Manual Data Entry Process

    Since Machine Learning primarily functions with the use of pre-existing and new input of data, it helps in reducing duplication and inaccurate entry of data. With ML’s predictive model algorithms, it has proven to avoid errors caused by manual data entry which in turn allows employees to invest the time productively on other business operations.

  4. Spam Detection

    Machine Learning has surpassed expectations from the previous process of detecting spam where a rule-based technique was followed. Now, with ML, spam filters are developing new rules with the use of neural networks to detect spam.

  5. Product-based Recommendation

    Unsupervised ML is used for developing a product-based recommendation for eCommerce websites using algorithms to track customers history of purchase and search options and providing them with relevant suggestions.

  6. Finance Analysis

    Machine Learning uses accurate volumes of quantitative data for efficient financial analysis. It is used for Portfolio Management, Algorithm Trading, Fraud Detection, and Loan Underwriting. Soon, ML will include various other features like Customer Service, Sentiment Analysis, Chatbots, and more.

  7. Medical Diagnosis

    Machine Learning has also proved to be highly effective in the medical scenario, with the use of superior diagnostics tools and effective medical treatment plans. Hence, improving patients health and reducing the cost with an accurate diagnosis, readmission prediction, identification of high-risk patients and medicinal recommendations by using the patient record data along with relevant health information.

  8. Enhance Customer Satisfaction

    Machine Learning helps in improving the loyalty of customers and offers superior customer experience. The goal is achieved by a thorough analysis of customer behavior in the past, customer requirement and feedback, all of which are assembled and examined in order to produce effective customer service. The use of ML in this sector helps in reducing cost and the time in managing the customer relationship.

  9. Cyber Security Improvement

    Cyber Security is a major problem faced by business enterprises that can be solved by Machine Learning. For, ML allows swift detection of a breach and builds better technologies to track and account for unknown threats.

To Surmise

Over the years, ML has grown prominent in the business sector with its advanced Artificial Intelligence (AI) applications. It has helped business enterprises and various other sectors with cost reduction solutions and effective end results. With time, its application will increase in quality and quantity whilst intensifying the growing popularity of AI.