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Questions and Answers
What is the purpose of the training-validation-test split in machine learning?
Which of the following techniques can help prevent overfitting in machine learning?
Identify the kind of learning algorithm for 'facial identities for facial expressions'.
Which of the following is not a supervised machine learning algorithm?
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What’s the key benefit of using deep learning for tasks like recognizing images?
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Which algorithm is best suited for a binary classification problem?
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What is the primary difference between classification and regression in machine learning?
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What is feature engineering in machine learning?
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Which algorithm is used when an artificially intelligent car decreases its speed based on its distance from the car in front of it?
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What is the bias-variance tradeoff in machine learning?
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Which of the following statements is true about stochastic gradient descent?
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Is supervised learning always more accurate than unsupervised learning?
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Study Notes
Machine Learning Fundamentals
- The training-validation-test split in machine learning is used to prevent overfitting by evaluating the model's performance on unseen data.
Techniques to Prevent Overfitting
- Techniques to prevent overfitting include:
- Regularization
- Early stopping
- Data augmentation
- Ensembling
- Dropout
Learning Algorithm for Facial Expressions
- The kind of learning algorithm used for 'facial identities for facial expressions' is Deep Learning, specifically Convolutional Neural Networks (CNNs).
Supervised Machine Learning Algorithm
- K-Means is not a supervised machine learning algorithm, it's an unsupervised clustering algorithm.
Deep Learning for Image Recognition
- The key benefit of using deep learning for tasks like recognizing images is its ability to automatically learn and extract relevant features from the data.
Binary Classification Algorithm
- The algorithm best suited for a binary classification problem is Logistic Regression.
Classification vs Regression
- The primary difference between classification and regression in machine learning is that classification predicts categorical labels, while regression predicts continuous values.
Feature Engineering
- Feature engineering in machine learning is the process of selecting and transforming raw data into features that are more suitable for modeling.
Algorithm for Autonomous Vehicles
- The algorithm used when an artificially intelligent car decreases its speed based on its distance from the car in front of it is Reinforcement Learning.
Bias-Variance Tradeoff
- The bias-variance tradeoff in machine learning refers to the tradeoff between the error introduced by simplifying a model (bias) and the error introduced by fitting the noise in the data (variance).
Stochastic Gradient Descent
- Stochastic gradient descent is an optimization algorithm that updates model parameters based on a single example from the training dataset in each iteration.
Supervised vs Unsupervised Learning
- Supervised learning is not always more accurate than unsupervised learning; the choice of algorithm depends on the problem and the availability of labeled data.
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Description
Learn about the purpose of the training-validation-test split in machine learning for ensuring model generalization and preventing overfitting. Explore techniques like regularization to prevent overfitting in machine learning models.
