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C10200 | Specific Jobs in AIMachine Learning Researchers.AI Engineer.Data Mining and Analysis.Machine Learning Engineer.Data Scientist.Business Intelligence (BI) Developer. | |
C10201 | Formally, calibration is the documented comparison of the measurement device to be calibrated against a traceable reference device. The reference standard may be also referred as a “calibrator.” Logically, the reference is more accurate than the device to be calibrated. | |
C10202 | The power of AI and robotics combined In theory, if you combine AI and a robot, you get an artificially intelligent robot with a high level of autonomy, able to optimize tasks it is assigned to do and “learn”. In this case, AI serves as the “brain” of the robot, while the sensors and mechanical parts act as the “body”. | |
C10203 | Simple linear regression has only one x and one y variable. Multiple linear regression has one y and two or more x variables. For instance, when we predict rent based on square feet alone that is simple linear regression. | |
C10204 | As the area under an ROC curve is a measure of the usefulness of a test in general, where a greater area means a more useful test, the areas under ROC curves are used to compare the usefulness of tests. The term ROC stands for Receiver Operating Characteristic. | |
C10205 | If you want to solve some real-world problems and design a cool product or algorithm, then having machine learning skills is not enough. You would need good working knowledge of data structures. So you've decided to move beyond canned algorithms and start to code your own machine learning methods. | |
C10206 | The main difference between stratified sampling and cluster sampling is that with cluster sampling, you have natural groups separating your population. In stratified sampling, a sample is drawn from each strata (using a random sampling method like simple random sampling or systematic sampling). | |
C10207 | In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected clusters are sampled. A common motivation of cluster sampling is to reduce costs by increasing sampling efficiency. | |
C10208 | The null hypothesis is generally denoted as H0. It states the exact opposite of what an investigator or an experimenter predicts or expects. It basically defines the statement which states that there is no exact or actual relationship between the variables. The alternative hypothesis is generally denoted as H1. | |
C10209 | Matplotlib is a plotting library for Python. It is used along with NumPy to provide an environment that is an effective open source alternative for MatLab. | |
C10210 | See the section on order statistics. One of the most important properties of the beta distribution, and one of the main reasons for its wide use in statistics, is that it forms a conjugate family for the success probability in the binomial and negative binomial distributions. | |
C10211 | 7:0910:40Suggested clip · 88 secondsInterpreting SPSS Output for Factor Analysis - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C10212 | 0:3910:15Suggested clip · 118 secondsConducting a Multiple Regression using Microsoft Excel Data YouTubeStart of suggested clipEnd of suggested clip | |
C10213 | At a bare minimum, collect around 1000 examples. For most "average" problems, you should have 10,000 - 100,000 examples. For “hard” problems like machine translation, high dimensional data generation, or anything requiring deep learning, you should try to get 100,000 - 1,000,000 examples. | |
C10214 | To test for non-time-series violations of independence, you can look at plots of the residuals versus independent variables or plots of residuals versus row number in situations where the rows have been sorted or grouped in some way that depends (only) on the values of the independent variables. | |
C10215 | To learn this course one needs to have enough knowledge in Python and its libraries such as NumPy, Matplotlib, Jupyter, and TensorFlow. Also, this course requires Python 3.5 or Python 3.6. Click here to learn. | |
C10216 | General steps to calculate the mean squared error from a set of X and Y values:Find the regression line.Insert your X values into the linear regression equation to find the new Y values (Y').Subtract the new Y value from the original to get the error.Square the errors.Add up the errors.Find the mean. | |
C10217 | A log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the parameters of the model, which makes it possible to apply (possibly multivariate) linear regression. | |
C10218 | To use the more formal terms for bias and variance, assume we have a point estimator ˆθ of some parameter or function θ. Then, the bias is commonly defined as the difference between the expected value of the estimator and the parameter that we want to estimate: Bias=E[ˆθ]−θ. | |
C10219 | Rejecting or failing to reject the null hypothesis If our statistical analysis shows that the significance level is below the cut-off value we have set (e.g., either 0.05 or 0.01), we reject the null hypothesis and accept the alternative hypothesis. | |
C10220 | An experimental group is the group in a scientific experiment where the experimental procedure is performed. A control group is a group separated from the rest of the experiment where the independent variable being tested is not exposed. You just studied 4 terms! | |
C10221 | 2:5910:12Suggested clip · 118 secondsCalculating fractal dimensions - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C10222 | This cross-sectional sample provides us with a snapshot of that population, at that one point in time. Panel data differs from pooled cross-sectional data across time, because it deals with the observations on the same subjects in different times whereas the latter observes different subjects in different time periods. | |
C10223 | Unconscious racial stereotypes are a major example of implicit bias. In other words, having an automatic preference for one race over another without even being aware of this bias. | |
C10224 | Statistically, the presence of an interaction between categorical variables is generally tested using a form of analysis of variance (ANOVA). If one or more of the variables is continuous in nature, however, it would typically be tested using moderated multiple regression. | |
C10225 | The minimum sample size is 100 Most statisticians agree that the minimum sample size to get any kind of meaningful result is 100. If your population is less than 100 then you really need to survey all of them. | |
C10226 | Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. | |
C10227 | Definition: Quota sampling is a sampling methodology wherein data is collected from a homogeneous group. It involves a two-step process where two variables can be used to filter information from the population. It can easily be administered and helps in quick comparison. | |
C10228 | Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples. In supervised learning, a machine learning algorithm builds a model by examining many examples and attempting to find a model that minimizes loss; this process is called empirical risk minimization. | |
C10229 | Percent Error Calculation StepsSubtract one value from another. Divide the error by the exact or ideal value (not your experimental or measured value). Convert the decimal number into a percentage by multiplying it by 100.Add a percent or % symbol to report your percent error value. | |
C10230 | Some popular examples of unsupervised learning algorithms are: k-means for clustering problems. Apriori algorithm for association rule learning problems. | |
C10231 | LDA assumes documents are produced from a mixture of topics. Those topics then generate words based on their probability distribution. Given a dataset of documents, LDA backtracks and tries to figure out what topics would create those documents in the first place. LDA is a matrix factorization technique. | |
C10232 | Scales effectively with data: Deep networks scale much better with more data than classical ML algorithms. With classical ML algorithms this quick and easy fix doesn't work even nearly as well and more complex methods are often required to improve accuracy. | |
C10233 | Image compression with principal component analysis is a frequently occurring application of the dimension reduction technique. As the number of principal components used to project the new data increases, the quality and representation compared to the original image improve. | |
C10234 | Positive feedback helps motivation, boosts confidence, and shows people you value them. It helps people to understand and develop their skills. And all this has a positive impact on individual, team, and organisational performance. | |
C10235 | 1 AnswerTake as central point of your confidence interval the sum of central points of every confidence interval (45+70+35=150 minutes).Take as radius of your interval the square root of the sum of the squares of the radius of every confidence interval √52+102+52=12.25. | |
C10236 | Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). | |
C10237 | Cosine similarity measures the similarity between two vectors of an inner product space. It is measured by the cosine of the angle between two vectors and determines whether two vectors are pointing in roughly the same direction. It is often used to measure document similarity in text analysis. | |
C10238 | Now we'll check out the proven way to improve the accuracy of a model:Add more data. Having more data is always a good idea. Treat missing and Outlier values. Feature Engineering. Feature Selection. Multiple algorithms. Algorithm Tuning. Ensemble methods. | |
C10239 | To check for heteroscedasticity, you need to assess the residuals by fitted value plots specifically. Typically, the telltale pattern for heteroscedasticity is that as the fitted values increases, the variance of the residuals also increases. | |
C10240 | In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. | |
C10241 | In statistics, a two-tailed test is a method in which the critical area of a distribution is two-sided and tests whether a sample is greater than or less than a certain range of values. It is used in null-hypothesis testing and testing for statistical significance. | |
C10242 | A heuristic is a mental shortcut that allows people to solve problems and make judgments quickly and efficiently. These rule-of-thumb strategies shorten decision-making time and allow people to function without constantly stopping to think about their next course of action. | |
C10243 | Regression is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables). | |
C10244 | Multivariate Regression is a method used to measure the degree at which more than one independent variable (predictors) and more than one dependent variable (responses), are linearly related. A mathematical model, based on multivariate regression analysis will address this and other more complicated questions. | |
C10245 | Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. Factor analysis aims to find independent latent variables. | |
C10246 | The independent variable is the variable the experimenter changes or controls and is assumed to have a direct effect on the dependent variable. The dependent variable is the variable being tested and measured in an experiment, and is 'dependent' on the independent variable. | |
C10247 | Variance measures how far a set of data is spread out. A variance of zero indicates that all of the data values are identical. A high variance indicates that the data points are very spread out from the mean, and from one another. Variance is the average of the squared distances from each point to the mean. | |
C10248 | In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). In PCR, instead of regressing the dependent variable on the explanatory variables directly, the principal components of the explanatory variables are used as regressors. | |
C10249 | 3 Answers. Since your response is ordinal then you should use ordinal regression. At a very high level, the main difference ordinal regression and linear regression is that with linear regression the dependent variable is continuous and ordinal the dependent variable is ordinal. | |
C10250 | The formula for response rate is to take the number of responses returned and divide it by the number of surveys sent out, and multiply the result by 100. | |
C10251 | Time Series analysis is “an ordered sequence of values of a variable at equally spaced time intervals.” It is used to understand the determining factors and structure behind the observed data, choose a model to forecast, thereby leading to better decision making. | |
C10252 | Sample size refers to the number of participants or observations included in a study. This number is usually represented by n. The size of a sample influences two statistical properties: 1) the precision of our estimates and 2) the power of the study to draw conclusions. | |
C10253 | For every time domain waveform there is a corresponding frequency domain waveform, and vice versa. For example, a rectangular pulse in the time domain coincides with a sinc function [i.e., sin(x)/x] in the frequency domain. Waveforms that correspond to each other in this manner are called Fourier transform pairs. | |
C10254 | Definition : A random experiment is an experiment or a process for which the outcome cannot be predicted with certainty. Definition : The sample space (denoted S) of a random experiment is the set of all possible outcomes. | |
C10255 | A curve that represents the cumulative frequency distribution of grouped data on a graph is called a Cumulative Frequency Curve or an Ogive. Representing cumulative frequency data on a graph is the most efficient way to understand the data and derive results. | |
C10256 | Sampling is used any time data is to be gathered. Data cannot be collected until the sample size (how much) and sample frequency (how often) have been determined. Sampling should be periodically reviewed. | |
C10257 | A sequence of random variables X1, X2, X3, ⋯ converges in probability to a random variable X, shown by Xn p→ X, if limn→∞P(|Xn−X|≥ϵ)=0, for all ϵ>0. | |
C10258 | In Computer science (especially Machine learning) Pruning means simplifying/compressing and optimizing a Decision tree by removing sections of the tree that are uncritical and redundant to classify instances. | |
C10259 | The reality is that stepwise regression is less effective the larger the number of potential explanatory variables. Stepwise regression does not solve the Big-Data problem of too many explanatory variables. Big Data exacerbates the failings of stepwise regression. | |
C10260 | Sequential is the easiest way to build a model in Keras. It allows you to build a model layer by layer. Each layer has weights that correspond to the layer the follows it. We use the 'add()' function to add layers to our model. We will add two layers and an output layer. | |
C10261 | Log loss, aka logistic loss or cross-entropy loss. This is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of a logistic model that returns y_pred probabilities for its training data y_true . | |
C10262 | The Fundamental Counting Principle If one event has p possible outcomes, and another event has m possible outcomes, then there are a total of p • m possible outcomes for the two events. Rolling two six-sided dice: Each die has 6 equally likely outcomes, so the sample space is 6 • 6 or 36 equally likely outcomes. | |
C10263 | Multinomial logistic regression is used when you have a categorical dependent variable with two or more unordered levels (i.e. two or more discrete outcomes). One level of the dependent variable is chosen as the reference category. This is typically the most common or the most frequent category. | |
C10264 | Hashing provides a more reliable and flexible method of data retrieval than any other data structure. It is faster than searching arrays and lists. In the same space it can retrieve in 1.5 probes anything stored in a tree that will otherwise take log n probes. | |
C10265 | Dual boot is completely safe if the operating systems are installed properly with correct GRUB configuration. The main advantage of having multiple operating systems is that, you get the best performance for your work if you are working on the particular operating system's native platforms, tools, etc. | |
C10266 | You can get the feature importance of each feature of your dataset by using the feature importance property of the model. Feature importance gives you a score for each feature of your data, the higher the score more important or relevant is the feature towards your output variable. | |
C10267 | A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. | |
C10268 | The confidence interval (CI) is a range of values that's likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population means lies between an upper and lower interval. | |
C10269 | Mixed effect logistic regression is a type of multilevel model. "Mixed effect logistic" would usually refer to cases where the outcome has 2 levels. Multinomial logistic regression is used when the dependent variable has more than two levels and they cannot be ordered. | |
C10270 | Bias can creep into algorithms in several ways. AI systems learn to make decisions based on training data, which can include biased human decisions or reflect historical or social inequities, even if sensitive variables such as gender, race, or sexual orientation are removed. | |
C10271 | The major difference between the Mann-Whitney U and the Kruskal-Wallis H is simply that the latter can accommodate more than two groups. Both tests require independent (between-subjects) designs and use summed rank scores to determine the results. | |
C10272 | The false positive rate is calculated as FP/FP+TN, where FP is the number of false positives and TN is the number of true negatives (FP+TN being the total number of negatives). It's the probability that a false alarm will be raised: that a positive result will be given when the true value is negative. | |
C10273 | Statistics Needed for Data Science For example, data analysis requires descriptive statistics and probability theory, at a minimum. These concepts will help you make better business decisions from data. Key concepts include probability distributions, statistical significance, hypothesis testing, and regression. | |
C10274 | A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate. Normal distributions are important in statistics and are often used in the natural and social sciences to represent real-valued random variables whose distributions are not known. | |
C10275 | Simple linear regression is appropriate when the following conditions are satisfied. The dependent variable Y has a linear relationship to the independent variable X. To check this, make sure that the XY scatterplot is linear and that the residual plot shows a random pattern. | |
C10276 | Originally Answered: Why do we often make the i.i.d. assumption in machine learning? Plain and simple answer is faster computation and less messy. Training models take longer than people would like even with distributed computing. | |
C10277 | Here are applications of Reinforcement Learning: Robotics for industrial automation. Business strategy planning. Machine learning and data processing. | |
C10278 | There are three basic rules associated with probability: the addition, multiplication, and complement rules. The addition rule is used to calculate the probability of event A or event B happening; we express it as: P(A or B) = P(A) + P(B) - P(A and B) | |
C10279 | CONCLUSION. There are three primary goals of survival analysis, to estimate and interpret survival and / or hazard functions from the survival data; to compare survival and / or hazard functions, and to assess the relationship of explanatory variables to survival time. | |
C10280 | The nominator is the joint probability and the denominator is the probability of the given outcome. This is the conditional probability: P(A∣B)=P(A∩B)P(B) This is the Bayes' rule: P(A∣B)=P(B|A)∗P(A)P(B). | |
C10281 | In probability, and statistics, a multivariate random variable or random vector is a list of mathematical variables each of whose value is unknown, either because the value has not yet occurred or because there is imperfect knowledge of its value. Normally each element of a random vector is a real number. | |
C10282 | Deep learning when data comes from different sources Multimodal learning suggests that when a number of our senses — visual, auditory, kinesthetic — are being engaged in the processing of information, we understand and remember more. By combining these modes, learners can combine information from different sources. | |
C10283 | Variance (σ2) in statistics is a measurement of the spread between numbers in a data set. That is, it measures how far each number in the set is from the mean and therefore from every other number in the set. | |
C10284 | An algorithm, for the non-programmers among us, is a set of instructions that take an input, A, and provide an output, B, that changes the data involved in some way. Algorithms have a wide variety of applications. In math, they can help calculate functions from points in a data set, among much more advanced things. | |
C10285 | The covariance between X and Y is defined as Cov(X,Y)=E[(X−EX)(Y−EY)]=E[XY]−(EX)(EY).The covariance has the following properties:Cov(X,X)=Var(X);if X and Y are independent then Cov(X,Y)=0;Cov(X,Y)=Cov(Y,X);Cov(aX,Y)=aCov(X,Y);Cov(X+c,Y)=Cov(X,Y);Cov(X+Y,Z)=Cov(X,Z)+Cov(Y,Z);more generally, | |
C10286 | Mean of General discrete uniform distribution The expected value of discrete uniform random variable is E ( X ) = a + b 2 . | |
C10287 | Cross Validation:Split randomly data in train and test set.Focus on train set and split it again randomly in chunks (called folds).Let's say you got 10 folds; train on 9 of them and test on the 10th.Repeat step three 10 times to get 10 accuracy measures on 10 different and separate folds.More items | |
C10288 | 1. The Canny edge detector is a linear filter because it uses the Gaussian filter to blur the image and then uses the linear filter to compute the gradient. Solution False. Though it does those things, it also has non-linear operations: thresholding, hysteresis, non-maximum suppression. | |
C10289 | According to my POV model accuracy is more important and its all depends on the training data. Model performance can be improved using distributed computing and parallelizing over the scored assets, whereas accuracy has to be carefully built during the model training process. | |
C10290 | The likelihood function is given by: L(p|x) ∝p4(1 − p)6. The likelihood of p=0.5 is 9.77×10−4, whereas the likelihood of p=0.1 is 5.31×10−5. | |
C10291 | Parametric tests are those that make assumptions about the parameters of the population distribution from which the sample is drawn. This is often the assumption that the population data are normally distributed. Non-parametric tests are “distribution-free” and, as such, can be used for non-Normal variables. | |
C10292 | If you don't know your population mean (μ) but you do know the standard deviation (σ), you can find a confidence interval for the population mean, with the formula: x̄ ± z* σ / (√n), Step 1: Subtract the confidence level (Given as 95 percent in the question) from 1 and then divide the result by two. | |
C10293 | The first, and most important limitation, which is present in all inferential statistics, is that you are providing data about a population that you have not fully measured, and therefore, cannot ever be completely sure that the values/statistics you calculate are correct. | |
C10294 | An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Depending upon the process to develop the network there are three main models of machine learning: Unsupervised learning. Supervised learning. | |
C10295 | The Q-Q plot, or quantile-quantile plot, is a graphical tool to help us assess if a set of data plausibly came from some theoretical distribution such as a Normal or exponential. If both sets of quantiles came from the same distribution, we should see the points forming a line that's roughly straight. | |
C10296 | When q-learning is performed we create what's called a q-table or matrix that follows the shape of [state, action] and we initialize our values to zero. We then update and store our q-values after an episode. This q-table becomes a reference table for our agent to select the best action based on the q-value. | |
C10297 | The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. It is also known as the Gaussian distribution and the bell curve. | |
C10298 | Regression analysis refers to assessing the relationship between the outcome variable and one or more variables. For example, a correlation of r = 0.8 indicates a positive and strong association among two variables, while a correlation of r = -0.3 shows a negative and weak association. | |
C10299 | Vector space model or term vector model is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers, such as, for example, index terms. The model is used to represent documents in an n-dimensional space. But a “document” can mean any object you're trying to model. |
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