Basic of Machine Learning

Machine learning

Machine learning is the foundation of modern AI. A basic understanding of how machines learn from and interpret data provides key insights into AI as a whole.

Machine learning is a subset of AI that enables systems to learn and improve from experience without being explicitly programmed. This allows machines to adapt and optimize their performance over time.

Key concepts in machine learning include:

• Training Data (used to teach the systems.)
• Algorithms (process data and make predictions or decisions)
• Models (the output of the learning process that can be applied to new data)

Types of Machine Learning:

Based on the data types there are two types of Machine Learning:

• Supervised Learning
• Unsupervised Learning

Supervised Learning

It involves traning a machine learning model on labeled data where the input and the corresponding correct output are pprovided, allowing the model to learn the relationship between them.

Example: Netflix uses supervised learning to recommend movies and TV shows to users by analyzing user’s history and search details.

Unsupervised Learning

It works with unlabeled data, focusing on identifying hidden patterns or intrinsic structures within the data without explicit instructions on what it should look for within the specified data sets.

Example: Google News uses unsupervised learning in their news articles into clusters based on their contents as it helps organizing news into categories like sports, politics, and technology etc.

Recommendation engines are another example of unsupervised learning. Using association rules, unsupervised machine learning can help explore transactional data to discover patterns or trends in turn can be used to drive personalized recommendations from online retailers.

Another example is customer segmentation in which unsupervised learning used to generate buyer personal profiles by clustering customer’s common trails or purchasing behaviors.