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Questions and Answers
What is the primary goal of Supervised Learning?
What is the purpose of Cross-Validation in Machine Learning?
What is an example of a Feature Selection Method?
What is the role of an Activation Function in Neural Networks?
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What is a Hyperparameter in Machine Learning?
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What is the primary purpose of the Backpropagation algorithm in Machine Learning?
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Which of the following metrics is most appropriate for evaluating imbalanced classification problems?
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What is the primary goal of Principal Component Analysis (PCA) in Machine Learning?
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What is the primary purpose of Dropout in Neural Networks?
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What is the primary technique used to handle missing data in Machine Learning?
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Study Notes
Types of Machine Learning
- Supervised Learning uses labeled data to train models, aiming to predict output from labeled input data.
- Primary goal of Supervised Learning is to predict output from labeled input data.
Classification Algorithms
- Logistic Regression is a common algorithm used in Classification.
Clustering Algorithms
- K-Means Clustering is used to segment customers into groups.
Model Evaluation
- Cross-Validation ensures the model generalizes well to unseen data.
Neural Networks
- An Epoch is a single forward and backward pass of all the training examples.
Classification Model Metrics
- Mean Squared Error is not a metric used in Classification models.
Overfitting
- Overfitting occurs when a model performs well on training data but poorly on test data.
Activation Functions
- Activation Functions introduce non-linearity into the model.
Feature Selection Methods
- Principal Component Analysis (PCA) is an example of a Feature Selection Method.
Regression Algorithms
- Linear Regression is a common algorithm used in Regression.
Performance Evaluation
- Confusion Matrix is used to evaluate the performance of a classification algorithm.
Sequential Decision Making
- Reinforcement Learning is used for Sequential Decision Making.
Data Preprocessing
- Normalization is used to scale data to a standard range.
Deep Learning Models
- Deep Learning Models automatically learn feature representations.
- Convolutional Neural Networks (CNNs) are commonly used for Image recognition.
Hyperparameters
- Hyperparameters are set before the learning process begins.
Ensemble Learning
- Ensemble Learning uses multiple models to achieve better performance.
Unsupervised Learning
- K-Means Clustering is an example of Unsupervised Learning.
Curse of Dimensionality
- Curse of Dimensionality refers to the problem of overfitting in high-dimensional spaces.
Reinforcement Learning
- Policy is the strategy that the agent employs to determine its actions.
Imbalanced Classification
- Precision-Recall Curve is an appropriate metric for Imbalanced Classification.
Backpropagation Algorithm
- Backpropagation Algorithm updates the weights by minimizing the loss function.
Classification Errors
- False Positive occurs when a negative class is incorrectly identified as positive.
Handling Missing Data
- Imputation is a technique used to handle missing data.
k-Nearest Neighbors (k-NN) Algorithm
- k-NN Algorithm uses a distance metric to classify new points.
Bootstrapping
- Bootstrapping is a resampling technique used to estimate statistics on a population.
Preventing Overfitting
- Regularization techniques are used to prevent overfitting.
Dropout
- Dropout is a regularization method used to prevent overfitting in Neural Networks.
Principal Component Analysis (PCA)
- PCA transforms data into a lower-dimensional space.
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Description
This quiz covers the basics of machine learning, including types of learning, common algorithms, and model evaluation techniques. Test your understanding of supervised learning, classification algorithms, and more.
