What is machine learning and how does it work ?
Machine Learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn and improve from experience without being explicitly programmed. Instead of following predefined rules, machine learning models analyze data, identify patterns, and make decisions or predictions based on that data. The goal of ML is to develop algorithms that can generalize from the data, allowing computers to handle new and unseen inputs efficiently. Visit more- Machine Learning Classes in Pune
Machine learning works by building models from data. Here’s a simplified explanation of the process:
Data Collection
Gathering Data: The first step is to collect a large amount of data relevant to the problem you’re trying to solve. This could be anything from images, text, or numerical data, to more complex data like time-series or audio recordings.
Data Preprocessing
Cleaning the Data: Raw data often has noise, missing values, or inconsistencies. This step involves cleaning and transforming the data into a format suitable for analysis.
Feature Selection: Identifying the most important variables (features) in the data that will help the model make accurate predictions.
Splitting the Data
Training Set: The majority of the data is used to train the machine learning model. The model learns patterns and relationships from this data.
Test Set: A smaller portion is used for testing the model’s performance, ensuring that it generalizes well to unseen data.
Selecting a Machine Learning Algorithm
Supervised Learning: Used when you have labeled data (input and correct output). Algorithms like linear regression, decision trees, and support vector machines (SVM) are popular choices.
Unsupervised Learning: Used when you have unlabeled data and the model tries to find hidden patterns. Examples include clustering (e.g., K-Means) and association algorithms.
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