A Brief Understanding of Machine Learning

Machine Learning is a sub-branch of Artificial Intelligence (AI) that is one of the fast-evolving fields of computer science. It is simply the study of computer algorithms and related data, which automatically improve through experience and learns to imitate the way a human learns and acts, with high accuracy. The great mind of this field is Arthur Samuel, the researcher who coined the term and is one of the pioneers of AI.

As seen in the above image, the study includes researching computer data and algorithms, using data mining processes and tools, uncovering key insights that shall help in the decision-making process and its implementation in business, and in turn impacting the key growth metrics to get in-hand automated results.

Approaches to Machine Learning

The different approaches to Machine Learning are divided into five categories-

• Supervised Learning: It is defined by its use of labeled datasets to train algorithms that classify data or predict outcomes very accurately. It includes active learning, classification and regression.
• Unsupervised Learning: It uses machine learning algorithms to analyze and cluster unlabeled datasets. The algorithms, therefore, learn from the test data that has not been labeled or classified.
• Semi-supervised Learning: As the name suggests, semi-supervised learning falls in between supervised and unsupervised learning. This type of learning uses the smaller labeled figures to guide classification and feature extraction from larger, unlabeled figures.
• Reinforcement Learning: This is a behavioral machine learning model which is similar to supervised learning. This model learns by using trial and error methods. It is used in various disciplines likegame theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms, etc.
• Dimensionality Reduction: Dimensionality reduction techniques can be considered as feature elimination or extraction. It is simply the reduction of the number of random variables under the consideration by obtaining a set of principal variables.

Real-life Applications of ML

Machine Learning in your everyday life

• Automated Speech Recognition: This computerized speech recognition uses natural language processing (NLP) to process written text formats and convert them into human speech. Many mobile and laptop devices have this in-built feature that makes texting very much easier.
• Customer Service: Online chatbots and pre-recorded customer service calls use machine learning to automatically answer FAQs, suggestions, personalized advices, customer engagements, etc.
• Search Engine Learning and Recommendation Systems: Websites, applications and search engines constantly use ML to improvise the recommendations and personalization problems. For example- Google, Netflix, Uber, Amazon, etc.
• Education: Gamified Learning is a very efficient way of providing education. The algorithm is programmed to display only the correct answer of the user at the very end and the questions that are incorrectly answered will repeat again so that the user shall thoroughly remember the correct answers.

Not only the above mentioned, but ML has many other uses like predicting illness in healthcare cases, checking the credit-worthiness of a bank's clients, self-driving cars, ranking of social media posts, computer vision in agriculture, targeted e-mails