What is Machine Learning (ML)?

Machine Learning is a branch of artificial intelligence (AI) that focuses on building systems that learn from data to make predictions or decisions without being explicitly programmed. It involves using algorithms to identify patterns and make decisions based on data.

Key Concepts in Machine Learning

• Supervised Learning: The model is trained on labeled data (input-output pairs) and learns to predict the output from new inputs.

• Unsupervised Learning: The model is trained on unlabeled data and tries to find hidden patterns or intrinsic structures.

• Reinforcement Learning: The model learns by interacting with its environment and receiving feedback in the form of rewards or punishments.

• Features and Labels: Features are the input variables used to make predictions, while labels are the output variables the model aims to predict.

Applications of Machine Learning

• Spam Detection:Identifying spam emails.

• Image Recognition:Recognizing objects in images.

• Recommendation Systems:Recommending products or content based on user preferences.

• Predictive Maintenance:Predicting equipment failures before they happen.

Examples

• Netflix:Uses machine learning to recommend movies and TV shows based on user preferences.

• Google Photos:Uses machine learning to automatically categorize and tag photos.

Activity

• Think of a problem you face daily. How could machine learning help solve this problem? Write down your thoughts and share them with a friend or classmate.

Quiz