What precision and recall are? After the predictive model has been finished, the most important question is: How good is it? Does it predict well? Evaluating the model is one of the most important tasks in the data science project, it indicates how good predictions are. Very often for classification problems we look at metrics called precision and recall, to define them in detail let’s quickly introduce confusion matrix first. Confusion Matrix for binary classification is made of four simple ratios: True Negative(TN): case was true negative and predicted negative True Positive(TP): case was true positive and predicted positive False […]

## Data Science News – handpicked articles, news, and stories from Data Science world.

Data Science News – handpicked articles, news, and stories from Data Science world. NEWS Experts Predict When Artificial Intelligence Will Exceed Human Performance – Artificial intelligence is changing the world and doing it at breakneck speed. The promise is that intelligent machines will be able to do every task better and more cheaply than humans. Rightly or wrongly, one industry after another is falling under its spell, even though few have benefited significantly so far. This app uses artificial intelligence to turn design mockups into source code – While traditionally it has been the task of front-end developers […]

## Validate-test a predictive model.

Validate-test a predictive model. Why evaluate/test model at all? Evaluating the performance of a model is one of the most important stages in predictive modeling, it indicates how successful model has been for the dataset. It enables to tune parameters and in the end test the tuned model against a fresh cut of data. Below we will look at few most common validation metrics used for predictive modeling. The choice of metrics influences how you weight the importance of different characteristics in the results and your ultimate choice of which machine learning algorithm to choose. Before we move on to […]

## Machine Learning with TensorFlow Intro

Machine Learning with TensorFlow Intro. What is TensorFlow? The shortest definition would be, TensorFlow is a general-purpose library for graph-based computation. But there is a variety of other ways to define TensorFlow, for example, Rodolfo Bonnin in his book – Building Machine Learning Projects with TensorFlow brings up definition like this: “TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) passed between them.” To quote the TensorFlow website, TensorFlow is an “open source software library for numerical computation using data […]

## Data Science Update – News

Data Science News Digest – handpicked articles, news, and stories from Data Science world. NEWS CUDA 9 Features Revealed – At the GPU Technology Conference, NVIDIA announced CUDA 9, the latest version of CUDA’s powerful parallel computing platform and programming model. Explaining How End-to-End Deep LearninSteers a Self-Driving Car – As part of a complete software stack for autonomous driving, NVIDIA has created a deep-learning-based system, known as PilotNet, which learns to emulate the behavior of human drivers and can be deployed as a self-driving car controller. Microsoft Build 2017: Microsoft AI – Amplify human ingenuity – Thanks to the convergence of […]

## Learn TensorFlow for free.

Below a list of free resources to learn TensorFlow: TensorFlow website: www.tensorflow.org Udacity free course: www.udacity.com Google Cloud Platform: cloud.google.com Coursera free course: www.coursera.org Machine Learning with TensorFlow by Nishant Shukla : www.tensorflowbook.com ‘First Contact With TensorFlow’ by Prof. JORDI TORRES: jorditorres.org or you can order from Amazon: First Contact With Tensorflow Kadenze Academy: www.kadenze.com OpenShift: blog.openshift.com Tutorial by pkmital : github.com Tutorial by HyunsuLee : github.com Tutorial by orcaman : github.com Stanford CS224d: Lecture 7 Was the above useful? Please share with others on social media. If you want to look for more information, check some free online courses available at coursera.org, edx.org or udemy.com. Recommended reading […]

## What is Supervised Learning?

Supervised Learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and the desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data […]

## What is Statistical Significance?

Statistical Significance in statistical hypothesis testing is attained whenever the observed p-value of a test statistic is less than the significance level defined for the study. The p-value is the probability of obtaining results at least as extreme as those observed, given that the null hypothesis is true. The significance level, α, is the probability of rejecting the null hypothesis, given that it is true. In any experiment or observation that involves drawing a sample from a population, there is always the possibility that an observed effect would have occurred due to sampling error alone. But if the p-value of […]

## What is Statistical Power?

Statistical Power of any test of statistical significance is defined as the probability that it will reject a false null hypothesis. Statistical power is inversely related to beta or the probability of making a Type II error. The power is a function of the possible distributions, often determined by a parameter, under the alternative hypothesis. As the power increases, there are decreasing chances of a Type II error, which are also referred to as the false negative rate (β) since the power is equal to 1−β, again, under the alternative hypothesis. A similar concept is Type I error or the […]

## What is Sentiment Analysis?

Sentiment Analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. Generally speaking, sentiment analysis aims to determine the attitude of a speaker, writer, or other subjects with respect to some topic or the overall contextual polarity or emotional reaction to a document, interaction, or event. The […]