Podcast
Questions and Answers
In traditional programming, what does the equation determine?
In traditional programming, what does the equation determine?
- The inputs to be used
- The programming language to be used
- The outputs based on the inputs (correct)
- The data type of the variables
Machine learning always requires explicit programming for every possible scenario.
Machine learning always requires explicit programming for every possible scenario.
False (B)
What is the primary goal of machine learning, in terms of inputs, outputs, and the relationship between them?
What is the primary goal of machine learning, in terms of inputs, outputs, and the relationship between them?
To determine the equation that maps inputs to outputs.
In machine learning, if the output takes continuous values, like predicting prices, it falls under the category of ______.
In machine learning, if the output takes continuous values, like predicting prices, it falls under the category of ______.
Match the following machine learning types with their descriptions:
Match the following machine learning types with their descriptions:
What does the equation $y = x + 5$ represent in traditional programming?
What does the equation $y = x + 5$ represent in traditional programming?
In machine learning, the output is fixed and cannot be adjusted based on the input data.
In machine learning, the output is fixed and cannot be adjusted based on the input data.
What type of problem is breast cancer detection (malignant or benign) considered in machine learning?
What type of problem is breast cancer detection (malignant or benign) considered in machine learning?
The learning rate ($\alpha$) determines the ______ of steps taken to reach a minimum in gradient descent.
The learning rate ($\alpha$) determines the ______ of steps taken to reach a minimum in gradient descent.
Match the following linear regression terms with their representations:
Match the following linear regression terms with their representations:
What is the purpose of the Cost Function, $J(\theta_0, \theta_1)$, in linear regression?
What is the purpose of the Cost Function, $J(\theta_0, \theta_1)$, in linear regression?
In linear regression, having a smaller training set always results in a more accurate model.
In linear regression, having a smaller training set always results in a more accurate model.
Explain the concept of 'Mean Normalization' in feature scaling.
Explain the concept of 'Mean Normalization' in feature scaling.
In logistic regression, the ______ function is used to map predicted values between 0 and 1.
In logistic regression, the ______ function is used to map predicted values between 0 and 1.
Match the components with respect to Logistic Regression:
Match the components with respect to Logistic Regression:
In Logistic Regression, what is the purpose of the 'Threshold'?
In Logistic Regression, what is the purpose of the 'Threshold'?
Feature Engineering involves selecting random features from a dataset to improve model performance.
Feature Engineering involves selecting random features from a dataset to improve model performance.
How does KNN (K-Nearest Neighbors) classify a new data point?
How does KNN (K-Nearest Neighbors) classify a new data point?
In Naive Bayes, it calculates the ______ of a sample belonging to a certain class based on its Data Points.
In Naive Bayes, it calculates the ______ of a sample belonging to a certain class based on its Data Points.
Match each Algorithm to scenario that matches it:
Match each Algorithm to scenario that matches it:
Which scenerio best fits using the mean normalization?
Which scenerio best fits using the mean normalization?
One example of K mean Neighbors is weather forcasting.
One example of K mean Neighbors is weather forcasting.
If an Data Point that is entered into one is KNN 2/3 points Bad, what would the third say?
If an Data Point that is entered into one is KNN 2/3 points Bad, what would the third say?
Supervised Learning “right answers" or ______ given
Supervised Learning “right answers" or ______ given
Match Machine learning and type of programming
Match Machine learning and type of programming
If a linear line is not on all points in the Data Set, is it still possible to predict the value?
If a linear line is not on all points in the Data Set, is it still possible to predict the value?
With 2 features in the data set, you can determine the point when the decision boundary is
With 2 features in the data set, you can determine the point when the decision boundary is
Why is Sigmoid Fucntion so important?
Why is Sigmoid Fucntion so important?
With the linear line being drawn, is called the [Blank] Hypothsis
With the linear line being drawn, is called the [Blank] Hypothsis
Match term to symbol relating to Decision Boundary's
Match term to symbol relating to Decision Boundary's
Where are parameters (\theta_0) and (\theta_1) found?
Where are parameters (\theta_0) and (\theta_1) found?
If there are not the same point on each feature, that can be bad
If there are not the same point on each feature, that can be bad
What is KNN?
What is KNN?
Regression and ______ can be used for a wide range
Regression and ______ can be used for a wide range
Can each of the following classify
Can each of the following classify
How can KNN detemine what it needs to be?
How can KNN detemine what it needs to be?
In Regression each value is continuous
In Regression each value is continuous
If there is nothing near it , would KNN work?
If there is nothing near it , would KNN work?
The more [Blank] KNN has in that section, the more likely what you're looking for is that section
The more [Blank] KNN has in that section, the more likely what you're looking for is that section
How do you test machine learning
How do you test machine learning
Why is the data value needed in machine learning
Why is the data value needed in machine learning
Flashcards
Traditional vs. Machine Learning?
Traditional vs. Machine Learning?
Traditional: inputs + equation = outputs; Machine learning: inputs + outputs = equation.
Machine Learning?
Machine Learning?
A field of study that gives computers the ability to learn without explicit programming.
Regression?
Regression?
Where the output takes continuous value.
Classification?
Classification?
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Unsupervised Learning?
Unsupervised Learning?
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Clustering?
Clustering?
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Linear Regression?
Linear Regression?
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Training Set
Training Set
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Error?
Error?
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Cost Function?
Cost Function?
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Gradient Descent?
Gradient Descent?
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Learning Rate?
Learning Rate?
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Overshooting?
Overshooting?
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Logistic Regression?
Logistic Regression?
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Feature Scaling?
Feature Scaling?
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Min-Max Scaling?
Min-Max Scaling?
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Feature Engineering?
Feature Engineering?
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Threshold?
Threshold?
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Sigmoid Function?
Sigmoid Function?
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Decision Boundary?
Decision Boundary?
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KNN?
KNN?
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Distance?
Distance?
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Category?
Category?
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Naive Bayes?
Naive Bayes?
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Feature
Feature
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Instance?
Instance?
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Class?
Class?
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Probability
Probability
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Study Notes
- Machine Learning teaches computers to learn without explicit programming.
Traditional Programming
- Traditional programming inputs data and an equation to get an output, using a predefined equation.
- For example, with the equation y = x + 5, inputting x = 5 would produce an output of y = 10.
Machine Learning
- Instead provides input and output, and learns the equation.
- For example, if the input is 1 and the output is 2, the machine learns that y = 2x.
- Abbreviated as ML.
- It provides computers the ability to learn without being explicitly programmed.
- This definition was given by Arthur Samuel.
Types of Machine Learning
- Supervised Learning uses labeled input and output data to learn a function.
- Supervised learning has applications in regression and classification.
- Regression handles continuous output, such as predicting prices.
- It predicts continuous valued output and can have many possible outputs
- Classifications deals with discrete valued output (0 or 1).
- An example is predicitng Breast cancer, where tumor size can be used to predict whether the tumor is malignant or benign
- Values for y are particular.
Unsupervised Learning
- Unsupervised learning uses unlabeled input data and identifies common features to form groups.
- This is also known as Clustering.
Linear Regression With One Variable
- Finds the line of best fit for a data set.
- The data set includes the inputs (x) and outputs (y.)
- Both are continuous
- The input has only 1 variable.
- Training Set: a set of data used to train the model.
- Number of training set examples is M.
- One of training set: a single point of data.
- The goal is to find the best fit, but small errors are made.
- Equations are used to minimize Hypothsis errors.
- The Hypothesis Function is: h = θ0 + θ1x
- θ0 is the Y-intercept
- θ1 is the slope
- Goal is to select θ0 and θ1 so that the difference actual y is minimized
- The Mean Squared Error (MSE) is used
- Generaly it can be said that The training set is put into the learning algorythm and produces the estimated output
Minimizing Error
- Goal is to minimize the cost function to find the best fit line.
- An optimization algorithm, such as Gradient Descent, achieves this.
- Gradient Descent adjusts parameters to find the minimum cost.
- The formulas include θ0 new (for y intercept) and θ1 new (for the slope).
- Steps are repeated until the minimum error is found.
- The learning rate (a) sets the step size when trying to find the most efficient optimization strategy
- A to low and the process takes to long to optimize
- A to high and the process fluctuates to far
- When using MSE the function is convex
Multiple and Logistic Regression
- Regression can be extended to use multiple features (inputs), x1, x2 etc, instead of just one.
- The hypothesis function will include additional coefficients to factor in multiple inputs.
- Used of Cost function to evaluate performance for linear regression.
Normalization
- Feature Scaling aims to ensure all Features operate on the same range of values.
- Normalization scales values to a standard range.
- Mean Normalization subtracts the mean and Divides by (max- min.)
- Normalization values should equate from 0 to 1
- Normalization also aims to fit linear regression for more unique data features
Logistic Regression and Sigmoid Functions
- Logistic Regression is used for binary classification problems (0 or 1).
- Calculates to see if each output can be classified in classes.
- A threshold is set to determine whether the output is 0 or 1.
- The sigmoid function helps predict outputs restricted to 0 and 1 by converting outputs from Logistic regression
- The threshold is used again following conversion to determine wether the outpus can be classified by 0 or 1
Cost Functions for Logistic Regression
- Defined using log loss to penalize incorrect predictions.
- Cost functions are defined on whether the value does equate as anticipated
- A threshold for H values also exists to support in identifying
Decision Boundary
- The desicion boundary is a threshold line to determine output classes based on known values.
- In Logistic regression, this process uses sigmond functions and the defined threshold.
- In one dimention the desicion boundary is one the line, in two dimentions it is a straight.
Example
- Use the functions, determine values of the sigmoid function, and cost values based on what is known
KNN (K-Nearest Neighbors) and Naive Bayes
- KNN is a simple algorithm that classifies data based on the majority class of its nearest neighbors.
- It works by calculating the distance between data points and classifying new points based on a majority vote of the closest points.
KNN Steps
- Calculate the distance between the new point and all existing data points.
- Select the K nearest neighbors based on the calculated distances.
- Assign the new data point to the class that is most common among its K nearest neighbors.
Distance Calculation
- Euclidean Distance is a common metric: d=√(y2−y1)2+(x2−x1)2.
- KNN can be used for both classification and regression. For regression, the average of the neighbors is calculated instead of majority class.
- Features are used to predict bad or good circumstances. Evaluate data using the above Euclidian function
Naive Bayes
- A classification algorithm based on applying Bayes' theorem with naive assumptions of independence between features.
- The assumption for calculating outcomes is also independent, therefore has to be manually assessed
- Computes P(A|B) to classify each case.
- Compute probabilities and identify best potential ouctomes
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