Machine learning is a field of artificial intelligence, one of the fastest-growing areas of information technology. It is simply the study of computer algorithms and associated data that automatically evolve according to experience and learn to mimic the way humans learn and act, with a high degree of accuracy. A major figure in this field is Arthur Samuel, the scientist who coined the term and is one of the pioneers of artificial intelligence.
It involves exploring computer data and algorithms, using data mining processes and tools, uncovering key insights that help in the decision making process and applying them to the business, and then influencing key growth indicators to have automated results at your fingertips.
Machine learning approaches
The different approaches to Machine Learning fall into five categories.
Supervised learning: This type of Machine Learning uses labeled datasets to train algorithms that classify data or predict outcomes with high accuracy. It includes active learning, regression and classification.
Unsupervised learning: This type of machine learning approach uses machine learning algorithms to analyze and cluster unlabeled datasets. Algorithms learn from unlabeled or unclassified test data.
Partly supervised learning: as the name suggests, partly supervised learning is a cross between supervised and unsupervised learning. In this type of learning, smaller numbers with labels are used to classify and extract features from the larger unlabeled numbers.
Reinforcement learning: this is a behavioral model of machine learning similar to supervised learning. This model learns by trial and error. It is used in various fields such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics, genetic algorithms, etc.
Dimensionality reduction: dimensionality reduction techniques can be thought of as the removal or elimination of features. It is a simple reduction in the number of random variables considered in order to obtain a set of principal variables.
Applications of ML in real life
Machine learning in everyday life
Automatic Speech Recognition: this computer speech recognition uses natural language processing (NLP) to process written text and convert it into human speech. Many mobile phones and laptops have this function built-in, making text processing much easier.
Customer Service: Search engine learning and recommender systems: websites, apps and search engines are constantly using ML to improve recommendation and personalization. Examples include Google, Netflix, Uber, Amazon, etc.
Training: gamified training is a very effective way of delivering training. The algorithm is programmed to show only the correct answer to the user at the very end and the questions with wrong answers are repeated again so that the user remembers the correct answers well.
In addition to the above, ML has many other applications such as disease prediction in healthcare, credit checking of bank customers, self-driving cars, classification of social media posts, computer vision in agriculture, targeted emails.