How machine learning solves problems ?
Machine learning solves problems through a process of learning from data and adapting to patterns in that data. Here's a breakdown of how machine learning works in practice to solve problems: 1. Data Collection Problem Framing: The first step is defining the problem in a way that can be solved with data. For example, if you're trying to predict customer churn, you collect relevant data such as customer interactions, transactions, and demographics. Data Gathering: Machine learning models require large amounts of data. The data can come from historical records, real-time sensors, databases, or the web. 2. Data Preprocessing Cleaning: Real-world data often contains errors, missing values, or noise. The data is cleaned to make it usable for the model. Normalization/Standardization: Scaling features so they have the same range or distribution, which is crucial for models that depend on the relationships between variables. Feature Engineering: Creating meaningful input variable...