Machine Learning
Machine Learning is a subset of AI that involves teaching computers to learn from data
Artificial Intelligence (AI) and Machine Learning (ML) are two closely related concepts that are transforming technology and how we interact with the world. AI and machine learning are powerful technologies that are changing many aspects of our lives. They enable computers to perform complex tasks, learn from experience, and improve over time, making them invaluable tools.
What is Artificial Intelligence (AI)?
AI refers to the ability of a computer or machine to perform tasks that typically require human intelligence. This includes understanding language, recognizing images, solving problems, and making decisions. AI is used in voice assistants like Siri and Alexa, recommendation systems on platforms like Netflix, and even in self-driving cars.
What is Machine Learning (ML)?
Machine Learning is a subset of AI that focuses on teaching computers to learn from data.
Instead of being explicitly programmed to perform a task, ML algorithms analyze data to identify patterns and make predictions. ML algorithms improve over time as they are trained by exposure to more data. ML models are what the computer program learns from running an algorithm on training data. The more data used, the better the model will get.
For example, if you want a computer to recognize pictures of cats, you would feed it many images of cats and non-cats. The machine learns from these examples and improves its accuracy over time.
Key Differences Between AI and ML
AI is the broader concept that encompasses any technique that enables machines to mimic human behavior. ML is one specific approach within AI that uses data to improve its performance.
While all machine learning is AI, not all AI involves machine learning. For instance, some AI systems may use rules or logic rather than learning from data. Machine learning, deep learning, and neural networks are all sub-fields of AI. However, neural networks is actually a sub-field of machine learning, and deep learning is a sub-field of neural networks.
Types of Machine Learning
- Supervised Learning: This involvestraining a model on a labeled dataset, where the desired output is known. For example, teaching a computer to classify emails as spam or not spam based on past examples.
- Unsupervised Learning: This type uses unlabeled data to find patterns or groupings without predefined categories. An example would be clustering customers based on their purchasing behavior.
- Reinforcement Learning: In this approach, an agent learns by interacting with its environment and receiving feedback in the form of rewards or penalties. It's often used in game playing or robotics.
Applications
- Healthcare: AI can analyze medical images to help diagnose diseases more accurately.
- Finance: Machine learning algorithms can detect fraudulent transactions by recognizing unusual patterns in spending.
- Marketing: Companies use AI to personalize recommendations for products based on customer behavior.
Links
ibm.com/topics/machine-learning
cloud.google.com/learn/artificial-intelligence-vs-machine-learning
ai.engineering.columbia.edu/ai-vs-machine-learning/
mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained
iso.org/artificial-intelligence/machine-learning
imd.org/blog/digital-transformation/deep-learning-vs-machine-learning/