Tag Archives: machine learning
QA – Testing Features of Machine Learning Models
In this post, you will learn about different types of test cases which you could come up for testing features of the data science/machine learning models. Testing features are one of the key set of QA tasks which needed to be performed for ensuring the high performance of machine learning models in a consistent and sustained manner. Features make the most important part of a machine learning model. Features are nothing but the predictor variable which is used to predict the outcome or response variable. Simply speaking, the following function represents y as the outcome variable and x1, x2 and x1x2 as predictor variables. y = a1x1 + a2x2 + a3x1x2 + e In the above function, …
QA of Machine Learning Models with PDCA Cycle
The primary goal of establishing and implementing Quality Assurance (QA) practices for machine learning/data science projects or, projects using machine learning models is to achieve consistent and sustained improvements in business processes making use of underlying ML predictions. This is where the idea of PDCA cycle (Plan-Do-Check-Act) is applied to establish a repeatable process ensuring that high-quality machine learning (ML) based solutions are served to the clients in a consistent and sustained manner. The following diagram represents the details. The following represents the details listed in the above diagram. Plan Explore/describe the business problems: In this stage, product managers/business analyst sit with data scientist and discuss the business problem at hand. The outcome of this …
QA & Data Science – How to Test Features Relevance
In this post, I intend to present a perspective on the need for QA / testing team to test the feature relevance when testing the machine learning models as part of data science QA initiatives, and, different techniques which could be used to test or perform QA on feature relevance. Feature relevance can also be termed as feature importance. Simply speaking, a feature is said to be relevant or important if it adds real predictive value to the underlying model. The relevant features must display a stable statistical relationship or association with the outcome variable. Well, an association does not imply a causation. However, a relevant feature or a feature …
Quality Assurance / Testing the Machine Learning Model
This is the first post in the series of posts related to Quality Assurance & Testing Practices and Data Science / Machine Learning Models which I would release in next few months. The goal of this and upcoming posts would be to create a tool and framework which could help you design your testing/QA practices around data science/machine learning models. Why QA Practices for testing Machine Learning Models? Are you a test engineer and want to know about how you could make difference in AI initiative being undertaken by your current company? Are you a QA manager and looking for or researching tools and frameworks which could help your team perform QA with …
AI – Three Different types of Machine Learning Algorithms
This post is aimed to help you learn different types of machine learning algorithms which forms the key to artificial intelligence (AI). Machine learning algorithms Representation or Feature learning algorithms Deep learning algorithms The following represents different types of learning algorithms in form of a Venn diagram. What are Machine Learning (ML) Algorithms? Machine learning algorithms are the most simplistic class of algorithms when talking about AI. ML algorithms are based on the idea that external entities such as business analysts and data scientists need to work together to identify the features set for building the model. The ML algorithms are, then, trained to come up with coefficients for each of the features and how are they …
8 Machine Learning Javascript Frameworks to Explore
Javascript developers tend to look out for Javascript frameworks which can be used to train machine learning models based on different machine learning algorithms. The following are some of the machine learning algorithms using which models can be trained using different javascript frameworks listed in this article: Simple linear regression Multi-variate linrear regression Logistic regression Naive-bayesian K-nearest neighbour (KNN) K-means Support vector machine (SVM) Random forest Decision tree Feedforward neural network Deep learning network In this post, you will learn about different Javascsript framework for machine learning. They are some of the following: Deeplearn.js Propel ConvNetJS ML-JS KerasJS STDLib Limdu.js Brain.js DeepLearn.js Deeplearn.js is an open-source machine learning Javascript library …
Sentiment Analysis Examples using Google Cloud NLP API
Sentiment analysis of a text document such as speech, articles on websites etc is about assessing sentiments associated with the document as a function of overall emotions expressed in form of different words. Sentiment analysis is primarily used for tracking voice of customer (VOC) by analyzing customer reviews, survey responses, etc., in social media websites such as Facebook, Twitter etc. The VOC can be related to products in general, an event, movies etc. In this post, you will learn about how to use Google Cloud NLP API for performing sentiment analysis of a text document. Java code is used for programming the sentiment analysis. Google NLP API – Sentiment Analysis Metrics …
Data Science – What are Machine Learning (ML) Models?
Machine learning (ML) models is the most commonly used in a data science project. In this post, you will learn about different definitions of a machine learning model to get a better understanding of what are machine learning models? A model is the relationship between features and the label. (Tensorflow – Getting Started for ML Beginners) An ML model is a mathematical model that generates predictions by finding patterns in your data. (AWS ML Models) ML Models generate predictions using the patterns extracted from the input data (Amazon Machine learning – Key concepts) Learning in the supervised model entails creating a function that can be trained by using a training …
Niramai uses AI / Thermal Imaging for Breast Cancer Screening
Niramai Health Analytix, a Bengaluru-based startup is creating an AI-powered software system for breast cancer screening. Niramai is using following technologies to achieve the objective of breast cancer screening: Thermal image processing using thermal sensing device (thermal camera) Machine learning algorithm Hardware devices integrated with real-time cloud-based diagnostics; These hardware devices are capable of capturing thermal images What/How of Thermal Image Processing? Thermal image processing, also termed as thermal imaging, is a method of improving visibility of objects in a dark environment by detecting the objects’ infrared radiation and creating an image based on that information. source: techtarget. The key to capturing thermal images of an object is a heat sensor (also called as thermal camera) which is …
Tutorials – Building Machine Learning Models for Predicting Cancer
In this article, I would introduce different aspects of the building machine learning models to predict whether a person is suffering from malignant or benign cancer while emphasizing on how machine learning can be used (predictive analysis) to predict cancer disease, say, Mesothelioma Cancer. The approach such as below can as well be applied to any other diseases including different types of cancers. Predicting Mesothelioma Cancer – Supervised Learning Problem Machine learning problems are classified into different kinds of learning problem. Most important of them are following: Supervised learning Unsupervised learning Supervised Learning In supervised learning, you have a history of data with each record being labeled. Thus, in case of predictive analysis of Mesothelioma cancer, there is …
Neural Networks Interview Questions – Set 2
This quiz represents practice test on artificial neural networks. These questions and answers can be as well used for your upcoming interviews for the position of machine learning engineer or data scientist. These questions can prove to be very useful for testing your neural networks knowledge from time-to-time. Also, these will be useful for interns / freshers / beginners of machine learning / data science. The topics covered in this practice test are following: Introduction to different types of neural networks such as Radial Basis Network, Recurrent neural network etc. Difference between multilayer perceptron (MLP) and Radial basis function network Practice Test on Neural Networks [wp_quiz id=”6000″]
K-Means Clustering Interview Questions – Set 1
This is a practice test on K-Means Clustering algorithm which is one of the most widely used clustering algorithm used to solve problems related with unsupervised learning. This can prove to be helpful and useful for machine learning interns / freshers / beginners planning to appear in upcoming machine learning interviews. This practice tests consists of interview questions and answers in relation with following: Introduction to K-Means Clustering Cost function Practice Test on K-Means Clustering [wp_quiz id=”5961″]
Top 8 Neural Networks and Deep Learning Tutorials
Here is a list of top 8 neural networks tutorials (web pages) for getting started on neural networks and deep learning. Introduction to Deep Neural Networks Neural Networks and Deep Learning: Free online book to learn concepts related with neural networks and deep learning. Very good for beginners. Concepts explained using Handwritten digits. The book is authored by Michael Nielsen. Neural Networks: The page explains and demonstrates various types of neural networks along with applications of neural networks like ANNs in medicine. Coursera Course on Neural Networks for Machine Learning: This can be used to learn fundamentals related with artificial neural networks and how they’re being used for machine learning, …
Martin Ford on Impact of AI & Robots on Society
This is a featured post on (Martin Ford), a futurist and author focusing on the impact of artificial intelligence (AI) and robotics on society and the economy. What Martin Ford has been saying / talking about? Here are some news feeds on Martin Ford which features his thoughts on AI and related topics: Who’s enjoying fruits of Innovation: In this article, he pointed out that AI is benefitting business owners, managers and investors more than the average workers. Earlier, workers knowing how to operate machines used to make them valuable enough to help them earn their livelihood. In the current age, machines are becoming autonomous and moving ahead in the …
70 Regression Analysis Interview Questions & Practice Tests
This page lists down practice tests (questions and answers), links to PDF files (consisting of interview questions) on Linear / Logistic Regression for machine learning / data scientist enthusiasts. These questions can prove to be useful, especially for machine learning / data science interns / freshers / beginners to check their knowledge from time-to-time or for upcoming interviews. Practice Tests on Linear / Multilinear Regression These are a set of four practice tests (consisting of 40 questions) covering linear (univariate) and multilinear (multivariate) regression in detail. Linear, Multiple regression interview questions and answers – Set 1 Linear, Multiple regression interview questions and answers – Set 2 Linear, Multiple regression interview …
Logistic Regression Interview Questions & Practice Tests
This page lists down a set of 30 interview questions on Logistic Regression (machine learning/data science) in form of objective questions and also provides links to a set of three practice tests that would help you test / check your knowledge on an ongoing basis. These questions and practice tests are intended to primarily help interns/freshers/beginners to help them brush up their knowledge in logistic regression from time to time. The following is a list of topics covered on this page. Introduction to logistic regression Logistic regression examples Evaluating performance of logistic regression and related techniques including AIC, deviance, ROC etc. Difference between linear and logistic regression Here is another post on …
I found it very helpful. However the differences are not too understandable for me