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C9300 | Here are a few examples where unstructured data is being used in analytics today. Classifying image and sound. Using deep learning, a system can be trained to recognize images and sounds. The systems learn from labeled examples in order to accurately classify new images or sounds. | |
C9301 | The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. | |
C9302 | A perceptron is a simple model of a biological neuron in an artificial neural network. Perceptron is also the name of an early algorithm for supervised learning of binary classifiers. | |
C9303 | IBM has been a leader in the field of artificial intelligence since the 1950s. Its efforts in recent years are around IBM Watson, including an a AI-based cognitive service, AI software as a service, and scale-out systems designed for delivering cloud-based analytics and AI services. | |
C9304 | Here are 7 examples of clustering algorithms in action.Identifying Fake News. Fake news is not a new phenomenon, but it is one that is becoming prolific. Spam filter. Marketing and Sales. Classifying network traffic. Identifying fraudulent or criminal activity. Document analysis. Fantasy Football and Sports. | |
C9305 | 1.1 The Role of Logic in Artificial Intelligence Logic, for instance, can provide a specification for a programming language by characterizing a mapping from programs to the computations that they license. | |
C9306 | The k-means problem is finding the least-squares assignment to centroids. There are multiple algorithms for finding a solution. There is an obvious approach to find the global optimum: enumerating all k^n possible assignments - that will yield a global minimum, but in exponential runtime. | |
C9307 | A unimodal distribution only has one peak in the distribution, a bimodal distribution has two peaks, and a multimodal distribution has three or more peaks. Another way to describe the shape of histograms is by describing whether the data is skewed or symmetric. | |
C9308 | Divide the number of events by the number of possible outcomes.Determine a single event with a single outcome. Identify the total number of outcomes that can occur. Divide the number of events by the number of possible outcomes. Determine each event you will calculate. Calculate the probability of each event.More items• | |
C9309 | An easy guide to choose the right Machine Learning algorithmSize of the training data. It is usually recommended to gather a good amount of data to get reliable predictions. Accuracy and/or Interpretability of the output. Speed or Training time. Linearity. Number of features. | |
C9310 | Nonresponse in sample surveys (see Survey Sampling ) may be defined as the failure to make measurements or obtain observations on some of the listing units selected for inclusion in a sample. | |
C9311 | It has been successfully used for many purposes, but it works particularly well with natural language processing (NLP) problems. Naive Bayes is a family of probabilistic algorithms that take advantage of probability theory and Bayes' Theorem to predict the tag of a text (like a piece of news or a customer review). | |
C9312 | The example of reinforcement learning is your cat is an agent that is exposed to the environment. The biggest characteristic of this method is that there is no supervisor, only a real number or reward signal. Two types of reinforcement learning are 1) Positive 2) Negative. | |
C9313 | Each feature, or column, represents a measurable piece of data that can be used for analysis: Name, Age, Sex, Fare, and so on. Features are also sometimes referred to as “variables” or “attributes.” Depending on what you're trying to analyze, the features you include in your dataset can vary widely. | |
C9314 | Artificial intelligence is imparting a cognitive ability to a machine. The idea behind machine learning is that the machine can learn without human intervention. The machine needs to find a way to learn how to solve a task given the data. Deep learning is the breakthrough in the field of artificial intelligence. | |
C9315 | Definition LT Linear Transformation A linear transformation, T:U→V T : U → V , is a function that carries elements of the vector space U (called the domain) to the vector space V (called the codomain), and which has two additional properties. T(u1+u2)=T(u1)+T(u2) T ( u 1 + u 2 ) = T ( u 1 ) + T ( u 2 ) for all u1,u2∈U. | |
C9316 | If two random variables X and Y are independent, then they are uncorrelated. Proof. Uncorrelated means that their correlation is 0, or, equivalently, that the covariance between them is 0. | |
C9317 | A posterior probability, in Bayesian statistics, is the revised or updated probability of an event occurring after taking into consideration new information. In statistical terms, the posterior probability is the probability of event A occurring given that event B has occurred. | |
C9318 | The degrees of freedom in a multiple regression equals N-k-1, where k is the number of variables. The more variables you add, the more you erode your ability to test the model (e.g. your statistical power goes down). | |
C9319 | MATLABL-shaped membrane logoMATLAB R2015b running on Windows 10Initial release1984Stable releaseR2020b / September 17, 2020Written inC/C++, MATLAB8 more rows | |
C9320 | You now know that: Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance. | |
C9321 | Predictive analytics uses historical data to predict future events. Typically, historical data is used to build a mathematical model that captures important trends. That predictive model is then used on current data to predict what will happen next, or to suggest actions to take for optimal outcomes. | |
C9322 | Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Supervised learning allows you to collect data or produce a data output from the previous experience. Unsupervised machine learning helps you to finds all kind of unknown patterns in data. | |
C9323 | For a binary classification like our example, the typical loss function is the binary cross-entropy / log loss. | |
C9324 | Omitted variable bias occurs when a regression model leaves out relevant independent variables, which are known as confounding variables. This condition forces the model to attribute the effects of omitted variables to variables that are in the model, which biases the coefficient estimates. | |
C9325 | Mini-batch gradient descent is a variation of the gradient descent algorithm that splits the training dataset into small batches that are used to calculate model error and update model coefficients. Implementations may choose to sum the gradient over the mini-batch which further reduces the variance of the gradient. | |
C9326 | The law of averages is not a mathematical principle, whereas the law of large numbers is. In probability theory, the law of large numbers is a theorem that describes the result of performing the same experiment a large number of times. | |
C9327 | In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. In unsupervised feature learning, features are learned with unlabeled input data. | |
C9328 | The first four are: 1) The mean, which indicates the central tendency of a distribution. 2) The second moment is the variance, which indicates the width or deviation. 3) The third moment is the skewness, which indicates any asymmetric 'leaning' to either left or right. | |
C9329 | If you see a lowercase x or y, that's the kind of variable you're used to in algebra. It refers to an unknown quantity or quantities. If you see an uppercase X or Y, that's a random variable and it usually refers to the probability of getting a certain outcome. | |
C9330 | 12:2824:57Suggested clip · 118 secondsPoisson versus negative binomial regression in SPSS - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C9331 | Odds ratios are one of those concepts in statistics that are just really hard to wrap your head around. For example, in logistic regression the odds ratio represents the constant effect of a predictor X, on the likelihood that one outcome will occur. The key phrase here is constant effect. | |
C9332 | At its most basic, machine learning uses programmed algorithms that receive and analyse input data to predict output values within an acceptable range. As new data is fed to these algorithms, they learn and optimise their operations to improve performance, developing 'intelligence' over time. | |
C9333 | "Mean" usually refers to the population mean. This is the mean of the entire population of a set. It's more practical to measure a smaller sample from the set. The mean of the sample group is called the sample mean. | |
C9334 | Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. | |
C9335 | Give an example in which binning is useful. The purpose of binning is to analyze the frequency of quantitative data grouped into categories that cover a range of possible values. A useful example is grouping quiz scores with a maximum score of 40 points with 10-point bins. | |
C9336 | Exponential moving averages, or EMA, give more weighting to recent prices. They reduce the effect of the lag that comes from using previous price data and can help you identify a trend earlier, so it's a useful indicator for trading short-term contracts. | |
C9337 | Random search (RS) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized, and RS can hence be used on functions that are not continuous or differentiable. Such optimization methods are also known as direct-search, derivative-free, or black-box methods. | |
C9338 | Convolutional neural networks work because it's a good extension from the standard deep-learning algorithm. Given unlimited resources and money, there is no need for convolutional because the standard algorithm will also work. However, convolutional is more efficient because it reduces the number of parameters. | |
C9339 | An indicator random variable is a special kind of random variable associated with the occurence of an event. The indicator random variable IA associated with event A has value 1 if event A occurs and has value 0 otherwise. In other words, IA maps all outcomes in the set A to 1 and all outcomes outside A to 0. | |
C9340 | How to Use K-means Cluster Algorithms in Predictive AnalysisPick k random items from the dataset and label them as cluster representatives.Associate each remaining item in the dataset with the nearest cluster representative, using a Euclidean distance calculated by a similarity function.Recalculate the new clusters' representatives.More items | |
C9341 | A factorial distribution happens when a set of variables are independent events. In other words, the variables don't interact at all; Given two events x and y, the probability of x doesn't change when you factor in y. | |
C9342 | 0:005:03Suggested clip · 117 secondsPCA 5: finding eigenvalues and eigenvectors - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C9343 | As the formula shows, the standard score is simply the score, minus the mean score, divided by the standard deviation. | |
C9344 | Examples of unstructured data are:Rich media. Media and entertainment data, surveillance data, geo-spatial data, audio, weather data.Document collections. Invoices, records, emails, productivity applications.Internet of Things (IoT). Sensor data, ticker data.Analytics. Machine learning, artificial intelligence (AI) | |
C9345 | A residual neural network (ResNet) is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections, or shortcuts to jump over some layers. | |
C9346 | Artificial intelligence can dramatically improve the efficiencies of our workplaces and can augment the work humans can do. When AI takes over repetitive or dangerous tasks, it frees up the human workforce to do work they are better equipped for—tasks that involve creativity and empathy among others. | |
C9347 | Specifically, you learned: That a key approach is to use word embeddings and convolutional neural networks for text classification. That a single layer model can do well on moderate-sized problems, and ideas on how to configure it. | |
C9348 | A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. | |
C9349 | gamma is a parameter for non linear hyperplanes. The higher the gamma value it tries to exactly fit the training data set gammas = [0.1, 1, 10, 100]for gamma in gammas: svc = svm.SVC(kernel='rbf', gamma=gamma).fit(X, y) | |
C9350 | A collinearity is a special case when two or more variables are exactly correlated. This means the regression coefficients are not uniquely determined. In turn it hurts the interpretability of the model as then the regression coefficients are not unique and have influences from other features. | |
C9351 | Exponential smoothing is a way to smooth out data for presentations or to make forecasts. It's usually used for finance and economics. If you have a time series with a clear pattern, you could use moving averages — but if you don't have a clear pattern you can use exponential smoothing to forecast. | |
C9352 | Discriminant analysis is statistical technique used to classify observations into non-overlapping groups, based on scores on one or more quantitative predictor variables. For example, a doctor could perform a discriminant analysis to identify patients at high or low risk for stroke. | |
C9353 | The "least squares" method is a form of mathematical regression analysis used to determine the line of best fit for a set of data, providing a visual demonstration of the relationship between the data points. | |
C9354 | In statistics, the likelihood function (often simply called the likelihood) measures the goodness of fit of a statistical model to a sample of data for given values of the unknown parameters. | |
C9355 | An AR(1) autoregressive process is one in which the current value is based on the immediately preceding value, while an AR(2) process is one in which the current value is based on the previous two values. An AR(0) process is used for white noise and has no dependence between the terms. | |
C9356 | Associations Software: commercial IBM SPSS Modeler Suite, includes market basket analysis. LPA Data Mining Toolkit supports the discovery of association rules within relational database. Magnum Opus, flexible tool for finding associations in data, including statistical support for avoiding spurious discoveries. | |
C9357 | The Minimax algorithm helps find the best move, by working backwards from the end of the game. At each step it assumes that player A is trying to maximize the chances of A winning, while on the next turn player B is trying to minimize the chances of A winning (i.e., to maximize B's own chances of winning). | |
C9358 | Relative Frequency Of A Class Is The Percentage Of The Data That Falls In That Class, While Cumulative Frequency Of A Class Is The Sum Of The Frequencies Of That Class And All Previous Classes. | |
C9359 | Compute the Total without disease by subtraction. Multiply the Total with disease by the Sensitivity to get the number of True positives. Multiply the Total without disease by the Specificity to get the number of True Negatives. | |
C9360 | An estimator attempts to approximate the unknown parameters using the measurements. In estimation theory, two approaches are generally considered. | |
C9361 | 0:243:37Suggested clip · 110 secondsFinding and Interpreting the Coefficient of Determination - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C9362 | Variables such as heart rate, platelet count and respiration rate are in fact discrete yet are considered continuous because of large number of possible values. Only those variables which can take a small number of values, say, less than 10, are generally considered discrete. | |
C9363 | An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set. Outliers can occur by chance in any distribution, but they often indicate either measurement error or that the population has a heavy-tailed distribution. | |
C9364 | Filter methods measure the relevance of features by their correlation with dependent variable while wrapper methods measure the usefulness of a subset of feature by actually training a model on it. Filter methods are much faster compared to wrapper methods as they do not involve training the models. | |
C9365 | An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance. | |
C9366 | When all the points on a scatterplot lie on a straight line, you have what is called a perfect correlation between the two variables (see below). A scatterplot in which the points do not have a linear trend (either positive or negative) is called a zero correlation or a near-zero correlation (see below). | |
C9367 | So, for 10% error, you need 100 hash functions. For 1% error, you need 10,000 hash functions. Yick. That's friggin expensive, and if that's all there were to MinHash, I'd simply go with the O(n log(n)) algorithm. | |
C9368 | The pdf represents the relative frequency of failure times as a function of time. The cdf is a function, F(x)\,\!, of a random variable X\,\!, and is defined for a number x\,\! | |
C9369 | To perform principal component analysis using the correlation matrix using the prcomp() function, set the scale argument to TRUE . Plot the first two PCs of the correlation matrix using the autoplot() function. | |
C9370 | Binary Cross-Entropy Loss Also called Sigmoid Cross-Entropy loss. It is a Sigmoid activation plus a Cross-Entropy loss. Unlike Softmax loss it is independent for each vector component (class), meaning that the loss computed for every CNN output vector component is not affected by other component values. | |
C9371 | How To Develop a Machine Learning Model From ScratchDefine adequately our problem (objective, desired outputs…).Gather data.Choose a measure of success.Set an evaluation protocol and the different protocols available.Prepare the data (dealing with missing values, with categorial values…).Spilit correctly the data.More items | |
C9372 | Supervised: Use the target variable (e.g. remove irrelevant variables).Wrapper: Search for well-performing subsets of features. RFE.Filter: Select subsets of features based on their relationship with the target. Feature Importance Methods.Intrinsic: Algorithms that perform automatic feature selection during training. | |
C9373 | You simply measure the number of correct decisions your classifier makes, divide by the total number of test examples, and the result is the accuracy of your classifier. It's that simple. The vast majority of research results report accuracy, and many practical projects do too. | |
C9374 | There are two forms of statistical inference:Hypothesis testing.Confidence interval estimation. | |
C9375 | In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression. | |
C9376 | Gradient boosting is a greedy algorithm and can overfit a training dataset quickly. It can benefit from regularization methods that penalize various parts of the algorithm and generally improve the performance of the algorithm by reducing overfitting. | |
C9377 | Backpropagation through time is the method to overcome decay in information through RNN. BPTT helps a practitioner to solve the sequence prediction problems for recurrent neural networks. It is used as a training algorithm which can update its weight in RNN. | |
C9378 | The regression effect causes an individual's expected post-test measurement to fall somewhere between her pre-test measurement and the mean pre-test measurement. Consider those subjects whose pre-test measurements are less than the overall mean (filled circles). | |
C9379 | In logistic regression, an odds ratio of 2 means that the event is 2 time more probable given a one-unit increase in the predictor. In Cox regression, a hazard ratio of 2 means the event will occur twice as often at each time point given a one-unit increase in the predictor. | |
C9380 | Maximum likelihood estimation is a method that will find the values of μ and σ that result in the curve that best fits the data. The goal of maximum likelihood is to find the parameter values that give the distribution that maximise the probability of observing the data. | |
C9381 | Geometrical meaning of integration is a statement so it must be true. Another way of analysing this statement is area of a curve or the volume of a curve of revolution or area of an implicit equation of x and y. The geometrical meaning of integration is to find the area under the corresponding curve. | |
C9382 | A probability distribution may be either discrete or continuous. A discrete distribution means that X can assume one of a countable (usually finite) number of values, while a continuous distribution means that X can assume one of an infinite (uncountable) number of different values. | |
C9383 | The traditional method of training AI models involves setting up servers where models are trained on data, often through the use of a cloud-based computing platform. Federated learning brings machine learning models to the data source, rather than bringing the data to the model. | |
C9384 | The main use of F-distribution is to test whether two independent samples have been drawn for the normal populations with the same variance, or if two independent estimates of the population variance are homogeneous or not, since it is often desirable to compare two variances rather than two averages. | |
C9385 | Two main types of fuzzy inference systems can be implemented: Mamdani-type (1977) and Sugeno-type (1985). These two types of inference systems vary somewhat in the way outputs are determined. Mamdani-type inference expects the output membership functions to be fuzzy sets. | |
C9386 | Exploratory Data Analysis is one of the important steps in the data analysis process. Exploratory Data Analysis is a crucial step before you jump to machine learning or modeling of your data. It provides the context needed to develop an appropriate model – and interpret the results correctly. | |
C9387 | The Z-distribution is a normal distribution with mean zero and standard deviation 1; its graph is shown here. Values on the Z-distribution are called z-values, z-scores, or standard scores. A z-value represents the number of standard deviations that a particular value lies above or below the mean. | |
C9388 | Rather than using the past values of the forecast variable in a regression, a moving average model uses past forecast errors in a regression-like model. While, the autoregressive model(AR) uses the past forecasts to predict future values. | |
C9389 | To calculate the mean of grouped data, the first step is to determine the midpoint (also called a class mark) of each interval, or class. These midpoints must then be multiplied by the frequencies of the corresponding classes. The sum of the products divided by the total number of values will be the value of the mean. | |
C9390 | 2:1510:12Suggested clip · 108 secondsHistograms In Photography - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C9391 | Sigma /ˈsɪɡmə/ (uppercase Σ, lowercase σ, lowercase in word-final position ς; Greek: σίγμα) is the eighteenth letter of the Greek alphabet. In the system of Greek numerals, it has a value of 200. In general mathematics, uppercase ∑ is used as an operator for summation. | |
C9392 | An estimator of a given parameter is said to be unbiased if its expected value is equal to the true value of the parameter. In other words, an estimator is unbiased if it produces parameter estimates that are on average correct. | |
C9393 | Parametric tests assume a normal distribution of values, or a “bell-shaped curve.” For example, height is roughly a normal distribution in that if you were to graph height from a group of people, one would see a typical bell-shaped curve. | |
C9394 | The term "running median" is typically used to refer to the median of a subset of data. | |
C9395 | For example, the Hamiltonian represents the energy of a system. The eigen functions represent stationary states of the system i.e. the system can achieve that state under certain conditions and eigenvalues represent the value of that property of the system in that stationary state. | |
C9396 | You can use the Lasso or elastic net regularization for generalized linear model regression which can be used for classification problems. Here data is the data matrix with rows as observations and columns as features. group is the labels. | |
C9397 | Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables. If the dependent variable is dichotomous, then logistic regression should be used. | |
C9398 | SVM tries to finds the “best” margin (distance between the line and the support vectors) that separates the classes and this reduces the risk of error on the data, while logistic regression does not, instead it can have different decision boundaries with different weights that are near the optimal point. | |
C9399 | As mentioned in the context of the gradient theorem, a vector field F is conservative if and only if it has a potential function f with F=∇f. Therefore, if you are given a potential function f or if you can find one, and that potential function is defined everywhere, then there is nothing more to do. |
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