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What is ML?
Machine Learning (ML) is a subset of Artificial
Intelligence (AI) that focuses on building
systems that learn from data, identify
patterns, and make decisions without
explicit programming. It enables computers
to improve their performance over time by
analysing large sets of data and using that
information to make predictions or decisions.
Types of Machine Learning
ML is applied in various industries and can be categorised into three main types:
1. Supervised Learning: This type of ML uses labelled data to train the model. The algorithm learns from
the input-output pairs, and the goal is to make predictions based on new, unseen data. For example, spam
email detection and image classification, such as identifying objects in photos.
2. Unsupervised Learning: In this type, the algorithm is trained on data that is not labelled, and it tries to
find patterns or groupings in the data.
Applications include customer segmentation and anomaly detection, where the system identifies unusual
behaviour in a dataset.
3. Reinforcement Learning: This type of ML involves an agent that learns by interacting with an environment
and receiving feedback in the form of rewards or penalties. It is used in applications like game-playing AI
(e.g., AlphaGo) and robotics, where machines learn to complete tasks through trial and error.
Applications of Machine Learning
Machine Learning is being increasingly applied across various industries to enhance decision-making,
automate processes, and optimise performance. Here are some key applications of ML:
Healthcare: ML is used to analyse medical data, diagnose diseases, and predict patient outcomes. For
example, ML algorithms are helping radiologists detect diseases like cancer in medical imaging, and tools
like IBM Watson assist in personalised treatment recommendations.
Finance: In the finance industry, ML algorithms are used for fraud detection, credit scoring, and algorithmic
trading. For instance, credit card companies use ML to identify suspicious transactions, while hedge funds
use it for high-frequency trading strategies.
E-commerce and Retail: ML is applied to recommend products based on consumer preferences and past
purchases. E-commerce platforms like Amazon use ML to suggest products to customers, driving sales and
enhancing user experience.
Autonomous Vehicles: ML enables self-driving cars to analyse data from sensors and cameras, allowing
them to navigate and make real-time decisions. Companies like Tesla and Waymo use ML to improve their
autonomous driving technology.
Social Media and Content Recommendation: Platforms like Facebook, Instagram, and YouTube use ML
to personalise content and recommend posts, videos and advertisements based on user behaviour and
interactions.
Customer Service: ML-powered chatbots and virtual assistants help businesses automate customer service
tasks, providing immediate responses to queries, resolving issues, and improving customer experience.
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