Basics of Machine Learning
06 Jun 2020
Machine learning is sub field of an artificial intelligence (AI). And AI is the study of agents, that perceive world around them, from plans and make a decisions to achieve their goals. It’s foundation include mathematics, probability, logic, philosophy, linguistics, decision theory, neurosciences. Many of the fields fall under umbrella of artificial intelligence. Such as machine learning, computer vision, machine learning, natural language processing.
As human learn from past experiences. And machines follow instructions given by humans. But what if humans can train the machine, that’s called machine learning. There are patterns in the data for during a task, we cannot respect them mathematically. That means we use machine learning to automatically learn those mathematical representation of pattern from data. We can say in other words, to give computer the ability to learn without being explicitly programmed.
ML is continuously developing field in this era, because there are some consideration to keep in mind as your work with machine learning methodologies, or ML process. Its tasks are classified into broad categories. These all categories are base on, how learning is received or how feedback on the learning is given to system developed.There are two most widely adopted machine learning methods. Which one is “Supervised learning” which trains algorithms based on example input and output data that is labeled by humans. And second one is “Unsupervised learning” which provides the algorithm with no labeled data in order to allow it to find structure within its input data.
The value of machine learning technology has been recognized by companies across several industries that deal with huge volumes of data.
Autonomous driving; (DARPA Grand challenge 2005) build a robot capable of navigating 175 miles through desert terrain in less than 10 hours, with no human intervention. Recommendation System; (Netflix prize) Predict how much someone is going to love a movie based on their movies preference. (Data: Over 100 million ratings that over 480,000 users gave to nearly 18,000 movies) and (Reward: $1,000,000 dollars if 10% improvement with respect to Netflix current system) Logical Rules Automatically learned from; Credit risks analysis in Market based analysis, customer relationship management (CRM), Finance, Manufacturing, Medicine, Telecommunications like Quality of services, Optimization, routing Bioinformatics, Search Engines, Fraud Detection, sentiment Analysis. So the main aim/purpose of ML is to determine and properly hidden in data. Then, theoretical distributions are applied to data sets to gain a better and better understanding. Every statistical models are backed by mathematically proven theories. However, ML is hugely based on the ability of computers to big deeper into the available data to an unleash a structure could looked like. As ML is iterative in nature, in terms of learning from data, learning process can be automated easily and data is analyzed until a clear pattern is identified.
You can read this Article at the Dawaat E-Magaziine.