What is Machine Learning (ML)?

About Machine Learning (ML):

• ML is a branch of Artificial Intelligence (AI) focused on building computer systems that learn from data.
• ML allows computer systems to continuously adjust and enhance themselves as they accrue more “experiences.” 
• ML algorithms are trained to find relationships and patterns in data.
• Using historical data as input, these algorithms can make predictions, classify information, cluster data points, reduce dimensionality and even generate new content. 
  • Examples of the latter, known as generative AI, include OpenAI's ChatGPT, Anthropic's Claude and GitHub Copilot.
• Training ML algorithms often demands large amounts of high-quality data to produce accurate results.
• Applications:
  • ML is widely applicable across many industries. For example, e-commerce, social media and news organizations use recommendation enginesto suggest content based on a customer's past behavior. 
  • In self-driving cars, ML algorithms and computer vision play a critical role in safe road navigation.
  • In healthcare, ML can aid in diagnosis and suggest treatment plans. 
  • Other common ML use cases include fraud detection, spam filtering, malware threat detection, predictive maintenance, and business process automation.

What is Deep Learning?

• It is a method in AI that teaches computers to process data in a way that is inspired by the human brain.
• It is a subset of ML which simulates the complex decision-making power of the human brain.
• Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.
• Deep learning methods are used to automate tasks that typically require human intelligence, such as describing images or transcribing a sound file into text.