What are the two methods used for the calibration in Supervised Learning?
Answer: The two methods used for predicting good probabilities in Supervised...
Which method is frequently used to prevent overfitting?
Which method is frequently used to prevent overfitting?
Answer: When there is sufficient data 'Isotonic Regression' is used to prevent an overfitting...
What is the difference between heuristic for rule learning and heuristics for decision trees?
What is the difference between heuristic for rule learning and heuristics for decision trees?
Answer: The difference is that the heuristics for...
What is Perceptron in Machine Learning?
What is Perceptron in Machine Learning?
Answer: In Machine Learning, Perceptron is an algorithm for supervised classification of the input into...
Explain the two components of Bayesian logic program?
Explain the two components of Bayesian logic program?
Answer: Bayesian logic program consists of two components. The first component is a logical...
What are Bayesian Networks (BN) ?
What are Bayesian Networks (BN) ?
Answer: Bayesian Network is used to represent the graphical model for probability relationship among a set...
Why instance based learning algorithm sometimes referred as Lazy learning algorithm?
Why instance based learning algorithm sometimes referred as Lazy learning algorithm?
Answer: Instance based learning algorithm is also referred...
What are the two classification methods that SVM ( Support Vector Machine) can handle?
What are the two classification methods that SVM ( Support Vector Machine) can handle?
Answer:
a) Combining binary classifiers
b) Modifying binary...
What are the two paradigms of ensemble methods?
What are the two paradigms of ensemble methods?
Answer: The two paradigms of ensemble methods are
a) Sequential ensemble methods
b) Parallel...
What is ensemble learning?
What is ensemble learning?
Answer: To solve a particular computational program, multiple models such as classifiers or experts are strategically...
Why ensemble learning is used?
Why ensemble learning is used?
Answer: Ensemble learning is used to improve the classification, prediction, function approximation etc of a ...
When to use ensemble learning?
When to use ensemble learning?
Answer: Ensemble learning is used when you build component classifiers that are more accurate and independent...
What is the general principle of an ensemble method and what is bagging and boosting in ensemble method?
What is the general principle of an ensemble method and what is bagging and boosting in ensemble method?
Answer: The general principle of an...
What is an Incremental Learning algorithm in ensemble?
What is an Incremental Learning algorithm in ensemble?
Answer: Incremental learning method is the ability of an algorithm to learn from new data...
What is bias-variance decomposition of classification error in ensemble method?
What is bias-variance decomposition of classification error in ensemble method?
Answer: The expected error of a learning algorithm can be decomposed...
What are support vector machines?
What are support vector machines?
Answer: Support vector machines are supervised learning algorithms used for classification and regression ...
What is dimension reduction in Machine Learning?
What is dimension reduction in Machine Learning?
Answer: In Machine Learning and statistics, dimension reduction is the process of reducing the...
What are the components of relational evaluation techniques?
What are the components of relational evaluation techniques?
Answer: The important components of relational evaluation techniques are
a) Data...
What are the different methods for Sequential Supervised Learning?
What are the different methods for Sequential Supervised Learning?
Answer: The different methods to solve Sequential Supervised Learning problems...
What are the different categories you can categorized the sequence learning process?
What are the different categories you can categorized the sequence learning process?
a) Sequence prediction
b) Sequence generation
c) Sequence...
What is batch statistical learning?
What is batch statistical learning?
Answer: Statistical learning techniques allow learning a function or predictor from a set of observed data...
What are the areas in robotics and information processing where sequential prediction problem arises?
What are the areas in robotics and information processing where sequential prediction problem arises?
Answer: The areas in robotics and information...
What is PAC Learning?
What is PAC Learning?
Answer: PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to analyze learning...
What is PCA, KPCA and ICA used for?
What is PCA, KPCA and ICA used for?
Answer: PCA (Principal Components Analysis), KPCA ( Kernel based Principal Component Analysis) and ICA (...
Explain what is the function of 'Unsupervised Learning'?
Explain what is the function of 'Unsupervised Learning'?
a) Find clusters of the data
b) Find low-dimensional representations of the data
c) Find...
Mention the difference between Data Mining and Machine learning?
Mention the difference between Data Mining and Machine learning?
Answer: Machine learning relates with the study, design and development of the...
How would you simulate the approach AlphaGo took to beat Lee Sedol at Go?
How would you simulate the approach AlphaGo took to beat Lee Sedol at Go?
Answer: AlphaGo beating Lee Sedol, the best human player at Go, in...
How do you think Google is training data for self-driving cars?
How do you think Google is training data for self-driving cars?
Answer: Machine learning interview questions like this one really test your knowledge...
Where do you usually source datasets?
Where do you usually source datasets?
Answer: Machine learning interview questions like these try to get at the heart of your machine learning...
How would you approach the "Netflix Prize" competition?
How would you approach the "Netflix Prize" competition?
Answer: The Netflix Prize was a famed competition where Netflix offered $1,000,000 for...
What are your favorite use cases of machine learning models?
What are your favorite use cases of machine learning models?
Answer: The Quora thread above contains some examples, such as decision trees that...
Do you have research experience in machine learning?
Do you have research experience in machine learning?
Answer: Related to the last point, most organizations hiring for machine learning positions...
What are the last machine learning papers you've read?
What are the last machine learning papers you've read?
Answer: Keeping up with the latest scientific literature on machine learning is a must...
What do you think of our current data process?
What do you think of our current data process?
Answer: This kind of question requires you to listen carefully and impart feedback in a manner...
How can we use your machine learning skills to generate revenue?
How can we use your machine learning skills to generate revenue?
Answer: This is a tricky question. The ideal answer would demonstrate knowledge...
How would you implement a recommendation system for our company's users?
How would you implement a recommendation system for our company's users?
Answer: A lot of machine learning interview questions of this type will...
Which data visualization libraries do you use? What are your thoughts on the best data visualization tools?
Which data visualization libraries do you use? What are your thoughts on the best data visualization tools?
Answer: What's important here is...
Describe a hash table.
Describe a hash table.
Answer: hash table is a data structure that produces an associative array. A key is mapped to certain values through the...
What are some differences between a linked list and an array?
What are some differences between a linked list and an array?
Answer: An array is an ordered collection of objects. A linked list is a series...
Pick an algorithm. Write the pseudocode for a parallel implementation.
Pick an algorithm. Write the pseudocode for a parallel implementation.
Answer: This kind of question demonstrates your ability to think in parallelism...
Do you have experience with Spark or big data tools for machine learning?
Do you have experience with Spark or big data tools for machine learning?
Answer: You'll want to get familiar with the meaning of big data for...
How do you handle missing or corrupted data in a dataset?
How do you handle missing or corrupted data in a dataset?
Answer: You could find missing/corrupted data in a dataset and either drop those rows...
What's the "kernel trick" and how is it useful?
What's the "kernel trick" and how is it useful?
Answer: The Kernel trick involves kernel functions that can enable in higher-dimension spaces...
How would you evaluate a logistic regression model?
How would you evaluate a logistic regression model?
Answer: A subsection of the question above. You have to demonstrate an understanding of what...
What evaluation approaches would you work to gauge the effectiveness of a machine learning model?
What evaluation approaches would you work to gauge the effectiveness of a machine learning model?
Answer: You would first split the dataset...
How do you ensure you're not overfitting with a model?
How do you ensure you're not overfitting with a model?
Answer: This is a simple restatement of a fundamental problem in machine learning: the...
Name an example where ensemble techniques might be useful.
Name an example where ensemble techniques might be useful.
Answer: Ensemble techniques use a combination of learning algorithms to optimize...
When should you use classification over regression?
When should you use classification over regression?
Answer: Classification produces discrete values and dataset to strict categories, while regression...
How would you handle an imbalanced dataset?
How would you handle an imbalanced dataset?
An imbalanced dataset is when you have, for example, a classification test and 90% of the data is in one...
What's the F1 score? How would you use it?
What's the F1 score? How would you use it?
Answer: The F1 score is a measure of a model's performance. It is a weighted average of the precision...
Big Data | True and False
Computerized support is only used for organizational decisions that are responses to external pressures, not for taking advantage of opportunities. T/F
Answer:...
All of the following are true about in-database processing technology EXCEPT
All of the following are true about in-database processing technology EXCEPT
A) it pushes the algorithms to where the data is.
B) it makes the response...
How does the use of cloud computing affect the scalability of a data warehouse?
How does the use of cloud computing affect the scalability of a data warehouse?
A) Cloud computing vendors bring as much hardware as needed to users'...
Which of the following statements is more descriptive of active data warehouses in contrast with traditional data warehouses?
Which of the following statements is more descriptive of active data warehouses in contrast with traditional data warehouses?
A) strategic decisions...
Active data warehousing can be used to support the highest level of decision making sophistication and power. The major feature that enables this in relation to handling the data is
Active data warehousing can be used to support the highest level of decision making sophistication and power. The major feature that enables this in...
Which of the following online analytical processing (OLAP) technologies does NOT require the precomputation and storage of information?
Which of the following online analytical processing (OLAP) technologies does NOT require the precomputation and storage of information?
A) MOLAP
B)...
When querying a dimensional database, a user went from summarized data to its underlying details. The function that served this purpose is
When querying a dimensional database, a user went from summarized data to its underlying details. The function that served this purpose is
A) dice.
B)...
When representing data in a data warehouse, using several dimension tables that are each connected only to a fact table means you are using which warehouse structure?
When representing data in a data warehouse, using several dimension tables that are each connected only to a fact table means you are using which warehouse...
All of the following are benefits of hosted data warehouses EXCEPT
All of the following are benefits of hosted data warehouses EXCEPT
A) smaller upfront investment.
B) better quality hardware.
C) greater control...
Data warehouses provide direct and indirect benefits to using organizations. Which of the following is an indirect benefit of data warehouses?
Data warehouses provide direct and indirect benefits to using organizations. Which of the following is an indirect benefit of data warehouses?
A)...
In which stage of extraction, transformation, and load (ETL) into a data warehouse are anomalies detected and corrected?
In which stage of extraction, transformation, and load (ETL) into a data warehouse are anomalies detected and corrected?
A) transformation
B) extraction
C)...
In which stage of extraction, transformation, and load (ETL) into a data warehouse are data aggregated?
In which stage of extraction, transformation, and load (ETL) into a data warehouse are data aggregated?
A) transformation
B) extraction
C) load
D)...
Which approach to data warehouse integration focuses more on sharing process functionality than data across systems?
Which approach to data warehouse integration focuses more on sharing process functionality than data across systems?
A) extraction, transformation,...
Which data warehouse architecture uses a normalized relational warehouse that feeds multiple data marts?
Which data warehouse architecture uses a normalized relational warehouse that feeds multiple data marts?
A) independent data marts architecture
B)...
Which data warehouse architecture uses metadata from existing data warehouses to create a hybrid logical data warehouse comprised of data from the other warehouses?
Which data warehouse architecture uses metadata from existing data warehouses to create a hybrid logical data warehouse comprised of data from the other...
Which of the following BEST enables a data warehouse to handle complex queries and scale up to handle many more requests?
Which of the following BEST enables a data warehouse to handle complex queries and scale up to handle many more requests?
A) use of the web by users...
A Web client that connects to a Web server, which is in turn connected to a BI application server, is reflective of a
A Web client that connects to a Web server, which is in turn connected to a BI application server, is reflective of a
A) one tier architecture.
B)...
All of the following statements about metadata are true EXCEPT
All of the following statements about metadata are true EXCEPT
A) metadata gives context to reported data.
B) there may be ethical issues involved...
Which kind of data warehouse is created separately from the enterprise data warehouse by a department and not reliant on it for updates?
Which kind of data warehouse is created separately from the enterprise data warehouse by a department and not reliant on it for updates?
A) sectional...
Operational or transaction databases are product oriented, handling transactions that update the database. In contrast, data warehouses are
Operational or transaction databases are product oriented, handling transactions that update the database. In contrast, data warehouses are
A) subject-oriented...
The "single version of the truth" embodied in a data warehouse such as Capri Casinos' means all of the following EXCEPT
The "single version of the truth" embodied in a data warehouse such as Capri Casinos' means all of the following EXCEPT
A) decision makers get to...
Big Data often involves a form of distributed storage and processing using Hadoop and MapReduce. One reason for this is
Big Data often involves a form of distributed storage and processing using Hadoop and MapReduce. One reason for this is
A) centralized storage creates...
Which of the following statements about Big Data is true?
Which of the following statements about Big Data is true?
A) Data chunks are stored in different locations on one computer.
B) Hadoop is a type of...
Prescriptive BI capabilities are viewed as more powerful than predictive ones for all the following reasons EXCEPT
Prescriptive BI capabilities are viewed as more powerful than predictive ones for all the following reasons EXCEPT
A) prescriptive BI gives actual...
How are descriptive analytics methods different from the other two types?
How are descriptive analytics methods different from the other two types?
A) They answer "what-if?" queries, not "how many?" queries.
B) They answer...
Today, many vendors offer diversified tools, some of which are completely preprogrammed (called s). How are these shells utilized?
Today, many vendors offer diversified tools, some of which are completely preprogrammed (called s). How are these shells utilized?
A) They are used...
What has caused the growth of the demand for instant, on-demand access to dispersed information?
What has caused the growth of the demand for instant, on-demand access to dispersed information?
A) the increasing divide between users who focus...
If a company's strategy is properly aligned with DW and BI initiatives, and if the company's IS organization can be made capable of playing its role in such a project, and if the requisite user community is in place and has the proper motivation, then
If a company's strategy is properly aligned with DW and BI initiatives, and if the company's IS organization can be made capable of playing its role...
What can the BI users in an organization help guide and direct?
What can the BI users in an organization help guide and direct?
A) how to implement and deploy a BI initiative that can be lengthy, expensive, and...
The very design that makes an OLTP system efficient for transaction processing makes it inefficient for what?
The very design that makes an OLTP system efficient for transaction processing makes it inefficient for what?
A) end-user ad hoc reports, queries,...
Online transaction processing (OLTP) systems handle a company's routine ongoing business. In contrast, a data warehouse is typically
Online transaction processing (OLTP) systems handle a company's routine ongoing business. In contrast, a data warehouse is typically
A) the end result...
When middles look across an organization to ensure that project priorities reflect the needs of the entire business, what is their main concern?
When middles look across an organization to ensure that project priorities reflect the needs of the entire business, what is their main concern?
A)...
Once a data warehouse is in place, the general process of intelligence creation begins with
Once a data warehouse is in place, the general process of intelligence creation begins with
A) end-user examinations of decision-making impacts.
B)...
When Sabre developed their Enterprise Data Warehouse, they chose to use near-real time updating of their database. The main reason they did so was
When Sabre developed their Enterprise Data Warehouse, they chose to use near-real time updating of their database. The main reason they did so was
A)...
In answering the question "Which customers are likely to be using fake credit cards?" you are most likely to use which of the following analytic applications?
In answering the question "Which customers are likely to be using fake credit cards?" you are most likely to use which of the following analytic applications?
A)...
In answering the question "Which customers are most likely to click on my online ads and purchase my goods?" you are most likely to use which of the following analytic applications?
In answering the question "Which customers are most likely to click on my online ads and purchase my goods?" you are most likely to use which of the...
Business intelligence (BI) can be characterized as a transformation of
Business intelligence (BI) can be characterized as a transformation of
A) data to information to decisions to actions.
B) Big Data to data to information...
Organizations counter the pressures they experience in their business environments in multiple ways. Which of the following is NOT an effective way to counter these pressures?
Organizations counter the pressures they experience in their business environments in multiple ways. Which of the following is NOT an effective way...
Which of the following is NOT an example that falls within the four major categories of business environment factors for today's organizations?
Which of the following is NOT an example that falls within the four major categories of business environment factors for today's organizations?
A)...
In the Magpie Sensing case study, the automated collection of temperature and humidity data on shipped goods helped with various types of analytics. Which of the following is an example of predictive analytics?
In the Magpie Sensing case study, the automated collection of temperature and humidity data on shipped goods helped with various types of analytics....
In the Magpie Sensing case study, the automated collection of temperature and humidity data on shipped goods helped with various types of analytics. Which of the following is an example of prescriptive analytics?
In the Magpie Sensing case study, the automated collection of temperature and humidity data on shipped goods helped with various types of analytics....
Statistical Data Variable Type: A variable that contains the values of either Yes or No would best be categorized as which of the following variable types?
Statistical Data Variable Type: A variable that contains the values of either Yes or No would best be categorized as which of the following variable...
Statistical Data Variable Type: A variable that contains a countable number of distinct values would best be categorized as which of the following variable types?
Statistical Data Variable Type: A variable that contains a countable number of distinct values would best be categorized as which of the following variable...
Which of the following measures of central location would be best to use when the Skewness is approximately zero?
Which of the following measures of central location would be best to use when the Skewness is approximately zero?
Mean
Median
Mode
2nd Quartil...
Which of the following is a common way of visualizing the frequency distribution of data points over a range of possible values?
Which of the following is a common way of visualizing the frequency distribution of data points over a range of possible values?
Quantitative...
If you have survey results from 100 people and the average response is 40% with a standard deviation of 5. Which of the following can you approximate from the results
If you have survey results from 100 people and the average response is 40% with a standard deviation of 5. Which of the following can you approximate...
Which of the following is NOT a qualitative data type?
Which of the following is NOT a qualitative data type?
Conversations
Surveys with numerical answers
Magazine articles
Media broadcasts
Answer: Surveys...
There is a web-based survey that asks you, "On a rating of 1(hated it) to 5(loved it), how much did you like the movie." This value is stored in your database and you need to categorize the statistical variable type. Which of the following variable types would be best?c
There is a web-based survey that asks you, "On a rating of 1(hated it) to 5(loved it), how much did you like the movie." This value is stored in your...
For a column in your dataset, your data analysis tool is telling you that the standard deviation is zero. What does this say about the data in that column?
For a column in your dataset, your data analysis tool is telling you that the standard deviation is zero. What does this say about the data in that...
Which is more important to you- model accuracy, or model performance?
Which is more important to you- model accuracy, or model performance?
Well, it has everything to do with how model accuracy is only a subset of model...
How is a decision tree pruned?
How is a decision tree pruned?
Pruning is what happens in decision trees when branches that have weak predictive power are removed in order to reduce...
What cross-validation technique would you use on a time series dataset?
What cross-validation technique would you use on a time series dataset?
Instead of using standard k-folds cross-validation, you have to pay attention...
What's the difference between a generative and discriminative model?
What's the difference between a generative and discriminative model?
Answer: A generative model will learn categories of data while a discriminative...
What is deep learning, and how does it contrast with other machine learning algorithms?
What is deep learning, and how does it contrast with other machine learning algorithms?
Answer: Deep learning is a subset of machine learning...
What's the difference between probability and likelihood?
What's the difference between probability and likelihood?
Discrete Random Variables
Suppose that you have a stochastic process that takes discrete...
What's a Fourier transform?
What's a Fourier transform?
Answer: A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric...
What's the difference between Type I and Type II error?
What's the difference between Type I and Type II error?
Type I error is a false positive, while Type II error is a false negative. Briefly stated,...
What's your favorite algorithm, and can you explain it to me in less than a minute?
What's your favorite algorithm, and can you explain it to me in less than a minute?
Answer: This type of question tests your understanding of...
Explain the difference between L1 and L2 regularization.
Explain the difference between L1 and L2 regularization.
L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse,...
Why is "Naive" Bayes naive?
Why is "Naive" Bayes naive?
Despite its practical applications, especially in text mining, Naive Bayes is considered "Naive" because it makes an assumption...
What is Bayes' Theorem? How is it useful in a machine learning context?
What is Bayes' Theorem? How is it useful in a machine learning context?
Bayes' Theorem gives you the posterior probability of an event given what...
Define precision and recall?
Define precision and recall?
Recall is also known as the true positive rate: the amount of positives your model claims compared to the actual number...
Explain how a ROC curve works.
Explain how a ROC curve works.
Answer: The ROC curve is a graphical representation of the contrast between true positive rates and the false...
How is KNN different from k-means clustering?
How is KNN different from k-means clustering?
Answer: K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering...
What is the difference between supervised and unsupervised machine learning?
What is the difference between supervised and unsupervised machine learning?
Answer: Supervised learning requires training labeled data. For...
What's the trade-off between bias and variance?
What's the trade-off between bias and variance?
Bias is error due to erroneous or overly simplistic assumptions in the learning algorithm you're using....
Give a popular application of machine learning that you see on a day-to-day basis?
Give a popular application of machine learning that you see on a day-to-day basis?
Answer: The recommendation engine implemented by major ecommerce...
What is Genetic Programming?
What is Genetic Programming?
Answer: Genetic programming is one of the two techniques used in machine learning. The model is based on the testing...
In what areas is pattern recognition used?
In what areas is pattern recognition used?
Pattern Recognition can be used in
a) Computer Vision
b) Speech Recognition
c) Data Mining
d) Statistics
e)...
What are the advantages of Naive Bayes?
What are the advantages of Naive Bayes?
Answer: In Naïve Bayes classifier will converge quicker than discriminative models like logistic regression,...
What is a classifier in machine learning?
What is a classifier in machine learning?
Answer: A classifier in a Machine Learning is a system that inputs a vector of discrete or continuous...
What is the difference between artificial learning and machine learning?
What is the difference between artificial learning and machine learning?
Answer: Designing and developing algorithms according to the behaviours...
What is algorithm independent machine learning?
What is algorithm independent machine learning?
Answer: Machine learning in where mathematical foundations is independent of any particular...
Explain what is the function of 'Supervised Learning'?
Explain what is the function of 'Supervised Learning'?
Answer:
a) Classifications
b) Speech recognition
c) Regression
d) Predict time series
e)...
What is the function of unsupervised learning?
What is the function of unsupervised learning?
Answer:
a) Find clusters of the data
b) Find low-dimensional representations of the data
c) Find...
What is 'Training set' and 'Test set'?
What is 'Training set' and 'Test set'?
Answer: In various areas of information science like machine learning, a set of data is used to discover...
What is the standard approach to supervised learning?
What is the standard approach to supervised learning?
Answer: The standard approach to supervised learning is to split the set of example into...
What are the three stages to build the hypotheses or model in machine learning?
What are the three stages to build the hypotheses or model in machine learning?
Answer:
a) Model building
b) Model testing
c) Applying the m...
What are the different Algorithm techniques in Machine Learning?
What are the different Algorithm techniques in Machine Learning?
The different types of techniques in Machine Learning are
a) Supervised Learning
b)...
What are the five popular algorithms of Machine Learning?
What are the five popular algorithms of Machine Learning?
a) Decision Trees
b) Neural Networks (back propagation)
c) Probabilistic networks
d)...
What is inductive Machine Learning?
What is inductive Machine Learning?
Answer: The inductive machine learning involves the process of learning by examples, where a system, from...
How can you avoid overfitting?
How can you avoid overfitting?
Answer: By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small...
Why does overfitting happen?
Why does overfitting happen?
Answer: The possibility of overfitting exists as the criteria used for training the model is not the same as the...
What is 'Overfitting' in Machine learning?
What is 'Overfitting' in Machine learning?
Answer: In machine learning, when a statistical model describes random error or noise instead of...
Mention the difference between Data Mining and Machine learning?
Mention the difference between Data Mining and Machine learning?
Answer: Machine learning relates with the study, design and development of the...
What is Machine Learning?
What is Machine Learning?
Answer: Machine learning is a branch of computer science which deals with system programming in order to automatically...
Give a derivation of for a single example in batch gradient descent? (Gradient Descent For Linear Regression)
Give a derivation of for a single example in batch gradient descent? (Gradient Descent For Linear Regression)
Derivation of for a single example...
What is the algorithm for implementing gradient descent for linear regression?
What is the algorithm for implementing gradient descent for linear regression?
The algorithm for implementing gradient descent for linear regression
We...
How does gradient descent converge with a fixed step size alpha?
How does gradient descent converge with a fixed step size alpha?
How does gradient descent converge with a fixed step size alpha?
As we approach...
Why should we adjust the parameter alpha when using gradient descent?
Why should we adjust the parameter alpha when using gradient descent?
Why should we adjust the parameter alpha when using gradient descent?
To...
Why does gradient descent, regardless of the slope's sign, eventually converge to its minimum value?
Why does gradient descent, regardless of the slope's sign, eventually converge to its minimum value?
Answer:
The following graph shows that:
•...
Depict the graphical implementation of minimizing the cost function using gradient descent.
Depict the graphical implementation of minimizing the cost function using gradient descent.
Answer:
The graphical implementation of minimizing...
State the algorithm for gradient descent.
State the algorithm for gradient descent.
State the algorithm for gradient descent.
Repeat until convergence, where j=0,1 represents the feature...
How do we implement an iteration step when calculating Gradient Descent in code?
How do we implement an iteration step when calculating Gradient Descent in code?
Answer:
At each iteration j, one should simultaneously update...
What is the contour line of a two variable function?
What is the contour line of a two variable function?
What is the contour line of a two variable function?
A contour line of a two variable function...
What is a visual interpretation of the cost function?
What is a visual interpretation of the cost function?
Answer:
• The training data set is scattered on the X-Y plane.
• We are trying to make a...
What are alternative terms of a Cost Function?
What are alternative terms of a Cost Function?
Answer:
Squared error function.
Mean squared error....
Give a pictorial representation of what the cost function of a supervised learning problem does.
Give a pictorial representation of what the cost function of a supervised learning problem does.
Cost function of a supervised learning problem.
Give...
What is the definition of a cost function of a supervised learning problem?
What is the definition of a cost function of a supervised learning problem?
Definition of a cost function of a supervised learning problem.
Answer: Takes...
How do we measure the accuracy of a hypothesis function?
How do we measure the accuracy of a hypothesis function?
Answer: By using a cost function, usually denoted by ...
What do we call a learning problem, if the target variable can take on only a small number of values?
What do we call a learning problem, if the target variable can take on only a small number of values?
Answer: When y can take on only a small...
What do we call a learning problem, if the target variable is continuous?
What do we call a learning problem, if the target variable is continuous?
Answer: When the target variable that we're trying to predict is continuous,...
What is Gradient Decent used for? What are the basic steps of Gradient Decent?
What is Gradient Decent used for?
Gradient decent is used to find the minimized values for a function, in which we simultaneously update our theta values...
Give the pictorial process for a supervised learning problem. Explain Supervised Learning Problem.
Give the pictorial process for a supervised learning problem.
Supervised Learning Problem.
Give the pictorial process for a supervised learning...
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