Machine Learning

Fundamentals Of Classification Models Part-2

Reading Time: 3 minutes This article is the continuation of “Fundamentals of Classification Models Part – 1” You need to go through this part before going to learn about Classifier Models. Classifier Models As discussed in the previous article ” We prepare the data for training the algorithm” the first step is to pre-process and clean the data The cleaning we need for this dataset is to change the Continue Reading

Convolutional Neural Network in TensorFlow

Reading Time: 3 minutes Introduction: As you might know, Neural networks reflect the behaviour of the human brain, allowing computer programs to recognise patterns and solve common problems in the fields of AI, machine learning, and deep learning. Neural networks are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has Continue Reading

Google BigQuery: An Introduction to Big Data Analytics Platform.

Reading Time: 6 minutes Hey Folks, Today we going to discuss Google BigQuery, an enterprise data warehouse with built-in machine learning capabilities. Before going to BigQuery, let’s understand what is Google Cloud Platform?Google Cloud Platform is a suite of public cloud computing services offered by Google. The platform includes a range of hosted services for compute, storage and application development that run on Google hardware. Google Cloud protects your data, applications, Continue Reading

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