What are Machine Learning (ML) and its use?

Machine Learning (ML) is a type of artificial intelligence that allows systems to learn and improve from experience
automatically without being explicitly programmed. ML algorithms use statistical models to analyze and
understand data and can be used to make predictions or decisions without human intervention.
There are several types of machine learning, including:
• Supervised Learning: learning from labelled data, where the system is trained on a labelled dataset and then
makes predictions on new, unseen data.
• Unsupervised Learning: learning from unlabeled data, where the system is not given explicit instructions on
what to learn, but instead must find patterns and structure in the data on its own.
• Semi-Supervised Learning: learning from a partially labelled dataset, where the system is given some labelled
data but also must find patterns in the unlabeled data.
• Reinforcement Learning: learning from a system’s interactions with an environment, where the system
receives rewards or penalties for certain actions and must learn to optimize its behaviour to maximize
rewards.
ML has a wide range of applications in various industries, such as:
• Healthcare: ML is used to analyze medical images, predict disease risk and progression, and assist in drug
discovery.
• Finance: ML is used to detect fraudulent transactions, analyze market trends, and make trading decisions.
• Retail: ML is used to personalize recommendations and pricing, optimize inventory, and improve customer
service through chatbots.
• Transportation: ML is used in self-driving cars, traffic prediction and control, and logistics optimization.
• Manufacturing: ML is used to predict equipment failure, improve process control, and optimize supply chain
management.
Overall, Machine Learning has a wide range of applications and it’s becoming a key technology in many industries,
it’s allowing organizations to make more accurate predictions, improve decision-making, and automate
repetitive tasks.