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HomeHomework Helpcomputer-scienceMachine Learning Basics

Machine Learning Basics

The basic concepts and principles of machine learning, including definitions, formulas, and fundamental techniques that form the basis of more advanced machine learning models and algorithms

beginner
5 hours
Computer Science
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Overview

Machine learning is a powerful tool that allows computers to learn from data and make decisions without explicit programming. It has various applications, from spam detection to image recognition, making it a vital area of study in computer science. Understanding the foundations of machine learning,...

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Key Terms

Supervised Learning
A type of machine learning where the model is trained on labeled data.

Example: Predicting house prices based on historical data.

Unsupervised Learning
A type of machine learning where the model learns from unlabeled data.

Example: Grouping customers based on purchasing behavior.

Overfitting
When a model learns the training data too well, including noise, leading to poor performance on new data.

Example: A model that predicts training data perfectly but fails on test data.

Feature Selection
The process of selecting a subset of relevant features for model training.

Example: Choosing the most important variables in a dataset.

Training Set
A portion of the dataset used to train the model.

Example: 80% of the data used to teach the model.

Validation Set
A portion of the dataset used to tune the model's parameters.

Example: 10% of the data used to validate model performance.

Related Topics

Deep Learning
A subset of machine learning focused on neural networks with many layers.
advanced
Data Science
The field that combines statistics, data analysis, and machine learning.
intermediate
Artificial Intelligence
The broader field that encompasses machine learning and other technologies.
intermediate

Key Concepts

Supervised LearningUnsupervised LearningNeural NetworksOverfitting