Welcome to the Machine Learning Study Guide! This document provides a comprehensive overview of essential machine learning concepts, methods, and practices. It is designed to offer a solid foundation for both learning and practical application in machine learning. This will help you learn Machine Learning
- Types of Learning
- Data Splitting
- Descriptive Statistics
- Outliers
- Feature Scaling
- Feature Selection
- Correlation vs. Causation
- Normalization and Transformation
- Regression and Correlation
- References
Supervised learning utilizes labeled data to train models for tasks such as classification and regression.
- Example: Predicting house prices using historical data with known prices.
Unsupervised learning involves unlabeled data to identify patterns or groupings, including clustering and dimensionality reduction.
- Example: Segmenting customers based on purchasing behavior.
Semi-supervised learning combines labeled and unlabeled data to enhance model performance.
- Example: Training a model on a small set of labeled images and a large set of unlabeled images.
Reinforcement learning trains an agent to make decisions based on rewards or penalties in an environment.
- Example: Teaching an agent to play a game by maximizing its score.
Data is typically divided into:
- Training Set: For model training.
- Validation Set: For tuning model parameters and selecting the best model.
- Test Set: For evaluating model performance on unseen data.
Descriptive statistics provide insights into the main features of a dataset:
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Mean (Arithmetic Mean):
where ( n ) is the number of data points, and ( x_i ) are the values.
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Median: The middle value in an ordered dataset, not influenced by outliers.
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Mode: The most frequently occurring value.
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Min and Max: The smallest and largest values, respectively.
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Variance:
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Standard Deviation:
Outliers are data points significantly different from others. Their treatment depends on context:
- Include Outliers: If they provide valuable information.
- Exclude Outliers: If they are errors or distort results.
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Visual Inspection: Use plots like box plots or scatter plots.
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Statistical Methods:
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Z-score: Indicates how many standard deviations a point is from the mean.
where ( x ) is the data point, ( \mu ) is the mean, and ( \sigma ) is the standard deviation.
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IQR (Interquartile Range):
Outliers are points outside the range ([Q1 - 1.5 \times IQR, Q3 + 1.5 \times IQR]), where ( Q1 ) and ( Q3 ) are the first and third quartiles.
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Feature scaling ensures that features contribute equally to the model. Common methods include:
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Standard Scaling (Standardization):
where ( \mu ) is the mean and ( \sigma ) is the standard deviation.
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Min-Max Scaling:
Scales features to a range, typically [0, 1].
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Robust Scaling:
Uses median and interquartile range (IQR) to handle outliers.
Feature selection improves model performance by choosing relevant features:
- Variance-based: Remove features with low variance.
- Correlation-based: Remove features that are highly correlated to avoid redundancy.
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Correlation: Measures the relationship between two variables. A positive correlation means both variables increase together, while a negative correlation means one increases as the other decreases.
Correlation coefficient formula:
where ( Cov(X, Y) ) is the covariance between ( X ) and ( Y ), and ( \sigma_X ) and ( \sigma_Y ) are their standard deviations.
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Causation: Indicates that one variable directly affects another. Correlation does not imply causation.
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Normalization: Adjusts data to a specific range, such as [0, 1] or [-1, 1].
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Power Transformation: Helps stabilize variance and make the data more Gaussian-like, improving modeling performance.
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Regression: Predicts one variable based on another, quantifying the relationship between them.
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Correlation: Measures the strength and direction of the linear relationship between variables but does not imply causation.
For further reading and additional resources, please refer to:
This guide provides a solid foundation for understanding key machine learning concepts. Explore these topics further and apply them to your own projects and problems!
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