Machine Learning

How To Find Correlation Value Of Categorical Variables.

Reading Time: 4 minutes Hey folks, In this blog we are going to find out the correlation of categorical variables. What is Categorical Variable? In statistics, a categorical variable has two or more categories.But there is no intrinsic ordering to the categories. For example, a binary variable(such as yes/no question) is a categorical variable having two categories (yes or no), and there is no intrinsic ordering to the categories. Continue Reading

Dealing with Missing Values in Python

Reading Time: 4 minutes For any Data Scientist, its very normal to deal with data sets having missing terms and still be able to manage and create a good predictive model out of it. Here we will discuss some techniques to handle missing data in a given data set. Missing Value occur when no data is stored for a variable or feature. It could be represented as “?”, “NA”, Continue Reading

Getting Familiar with Activation Function and Its Types.

Reading Time: 7 minutes Hey Folks, In this blog we are going to discuss activation function in Artificial Neural Networks and their different types. Before going there, let’s get some idea about what is an artificial neural network? Artificial Neural Network(i.e., ANN) Artificial Neural Network refers to a biologically inspired sub-field of Artificial Intelligence modeled after the brain. ANN is a computational network based on a biological neural network Continue Reading

Pandas for Data Analysis

Reading Time: 4 minutes Why Pandas for data Analysis? Real ‘raw’ data needs a lot of ‘wrangling’ operations before it can be ready for dissection by a data scientist one of the popular tools for data wrangling in python is Pandas. Because of the availability of widespread packages of Pandas for almost every possible function. The library Pandas is one such package that makes life easier especially for data analysis. Through Continue Reading

Java in Machine Learning

Reading Time: 2 minutes Overview Machine Learning(ML) projects can be done in Java but there are some reasons why Java is not as popular as Python. Java is not the preferred first choice of Data Scientists and Machine Learning engineers for creating ML models. Java is mainly used in large data processing and engineering parts of a typical ML life cycle. The processed and engineered data is used by Continue Reading

How to build Face Detection system using Viola Jones Algorithm

Reading Time: 5 minutes Object Detection is to locate the presence of objects and types or classes of the located objects in an image. Face detection is a particular case of Object Detection. The objective of face detection is to find and locate faces in an image. It is the first step in automatic face recognition applications. Face detection has been well studied for frontal and near frontal faces. Continue Reading

Basics of Machine Learning and it’s Algorithms -You Need to Know

Reading Time: 6 minutes Machine Learning and it’s Algorithms Hi folks! Are you intrigued about Machine Learning and its Algorithms? If yes, Welcome. You have come to the right place. In this blog you will learn about machine learning and it’s algorithms. By the end of the blog, you will have the basic understanding of this field Machine Learning The term is self-explanatory enough that there is going to Continue Reading

CD4ML

Continuous delivery for machine learning (CD4ML)

Reading Time: 7 minutes Getting machine learning applications into production is hard In modern software development, we’ve grown to expect that new software features and enhancements will simply appear incrementally, on any given day. This applies to consumer applications such as mobile, web, desktop apps as well as modern enterprise software. We’re no longer tolerant of big, disruptive, deployments of software. Knoldus has been a pioneer in Continuous Delivery Continue Reading

Product demand forecasting with Knime

Reading Time: 5 minutes In this blog, we are going to see, Importance of demand forecasting and how we can easily create these forecasting workflows with Knime. Market request forecasting is a basic procedure for any business, however maybe none more so than those in buyer packaged products. Stock, production, storage, delivering, showcasing – each aspect of CPG and retail organizations’ activities are influenced by accurate forecasting. Identifying shoppers’ Continue Reading

MachineX: Run ML model prediction faster with Hummingbird

Reading Time: 3 minutes In this blog, we will see how to make our machine learning model’s prediction faster with a recently open-sourced library Hummingbird. Nowadays, we can see a lot of frameworks for deploying or serving the machine learning model into production. As a result, It is a headache for a data scientist to choose between these frameworks, keeping in mind how their model either Sklearn or LightGBM Continue Reading

Knoldus-blog-AI-ML

Finding the nexus between intelligent technologies and business

Reading Time: 2 minutes Artificial Intelligence & Machine Learning has indeed taken over our lives at every step of the way. For example: Starting out for your holiday tomorrow? Your smartphone will automatically send you weather reports and suggest itineraries. Ever seen your email software suggest smart replies while you’re drafting emails or replying to one? That’s also AI at work.

Knime Analytics Platform: A dream for a data scientist

Reading Time: 3 minutes In this blog, we are going to see, what is the Knime analytics platform and its important features to create an analytics workflow in an easy way. Introduction to Knime Analytics Platform KNIME is a platform built for powerful analytics on a GUI based workflow. This means you do not have to know how to code to be able to work using KNIME and derive Continue Reading

MachineX: performance metrics for Model Evaluation

Reading Time: 6 minutes In this blog, we are going to see how to choose the right metrics for model evaluation in different kinds of applications. There are different metric categories based on the ML model/application, and we are going to cover the popular metrics used in the following problems: Classification Metrics (accuracy, precision, recall, F1-score, ROC, AUC) Regression Metrics (MSE, MAE) there are more metrics like Computer Vision Continue Reading