AIHUB

Introduction Machine Learning is a large domain that drives today's smart systems. It is generally divided into supervised and unsupervised learning. If you are starting out, you may want to take a look at our comprehensive Introduction to Machine Learning blog, where we discuss the types, applications, and real-world applications in detail. In this era of huge datasets and real-time data creation, interpreting raw, unlabeled data is a serious challenge. Unsupervised learning comes into action here. In contrast to its opposite, supervised learning, which demands labeled data for training, unsupervised learning enables the machine to identify hidden patterns, clustering, or structures independently. It replicates how naturally humans collate information about training, Whether it’s identifying customer segments, detecting anomalies in financial transactions, or finding relationships in social networks, unsupervised learning has proven to be an essential tool in the machine lear...

Introduction In the data age, machines have grown more adept at making forecasts, spotting trends, and making choices. At the heart of this smart action is a very strong branch of artificial intelligence called supervised learning. From detecting junk mail to anticipating stock prices and diagnosing diseases, supervised learning is behind many real-world applications of AI. It allows machines to learn from labeled examples (i.e., each item in the dataset has a correct response) so they can make correct predictions on new data. One of the most popular techniques in machine learning, supervised learning is a must for anyone who wants to get started with intelligent systems. In this blog, we will inspect what is supervised learning. How does it work? What are the different types of it? The common algorithms and applications, and how to build your models using this approach. If you're new to AI, check out our Introduction to Machine Learning before diving into supervised learning. Tra...

Introduction to Machine Learning Machine Learning (ML) is perhaps the most revolutionary technology within Artificial Intelligence (AI). Systems can learn automatically, adapt, and make intelligent decisions without explicit programming. From recommendation systems on YouTube and Netflix and anti-fraud detection in banking, machine learning is behind much of the digital world we engage with today. What is Machine Learning? Machine Learning is one of the AI subsets that deals with creating algorithms that can learn patterns from data and make decisions or predictions from that learning. Rather than hard-coding rules, machine learning systems are trained on data. They identify patterns and modify their actions based on them, which is why they find extensive applications and data-intensive situations. Learn more about the history of Artificial Intelligence and how it led to the rise of Machine Learning in our detailed post on the History of AI . Machine Learning Clustering Types of Machin...

Do you know how human brains work? Have you ever wondered if animals also have a brain but can’t make decisions like humans? Why so? Humans do all this with the help of intelligence, so when a human puts intelligence into a machine, it is called artificial intelligence. The term artificial intelligence was first coined decades ago in 1956 by John McCarthy at the Dartmouth Conference. he defined artificial intelligence as “Artificial Intelligence is the science and engineering of making intelligent machines”. AI is the technique of getting machines to work just like humans. These machines are artificially incorporated with human-like intelligence to perform tasks as we do. This intelligence is built using complex algorithms and functions. The practical applications of AI include healthcare, robotics, business analytics, and marketing. AI has become so general that we don’t realize we use it in our daily lives, including smartphones, vehicles, social media, games, banking, and many...