Machine Learning

Why Computer Vision Had Been So Popular Till Now?

Reading Time: 3 minutes What is computer vision? Computer vision(CV) is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data (images or videos) in the same way that humans do. The concept of computer vision is based on teaching computers to process an image at a pixel level and understand it. Technically, machines attempt to retrieve visual Continue Reading

Kubeflow: A Complete Solution to MLOps.

Reading Time: 5 minutes Hey Folks, In this blog we are going to put some light on Kubeflow, an open-source platform that enables us to orchestrate complicated workflows running on Kubernetes of machine learning pipelines. Kubeflow Emerges for ML Workflow Automation Many data scientists today find it burdensome to manually execute all of the steps in a machine learning workflow. Moving and transforming data, training models, then promoting them Continue Reading

Is TensorFlow a good fit for model optimisation?

Reading Time: 2 minutes In the realm of AI, a great deal of consideration is optimising training. There is a lot less information out there on optimising models. However serving models for forecast is the place where we make money out of ML. Without a doubt, the expense of serving forecasts might be a central point in the all out profit from speculation for a ML application. In this Continue Reading

TensorFlow Recommenders (TFRS): An Overview

Reading Time: 4 minutes Hey Guys, Aren’t you surprised, when you watch any video on youtube or any movie on Netflix or look for any product on an E-Commerce website?You start receiving similar kinds of videos, movies, and products suggestion on respective platforms.So, how do platforms do that?.Well, they use recommender systems, an important application of machine learning, surfacing new discoveries and helping users find what they love.In this Continue Reading

Let us know about TensorFlow Extended (TFX) components and Libraries?

Reading Time: 3 minutes In this blog, we will be learning about Tensorflow Extended (TFX) components and libraries. TFX is a Google-production-scale machine learning (ML) platform based on TensorFlow. It provides a configuration framework and shared libraries. Moreover, to integrate common components needed to define, launch, and monitor your machine learning system. How Tensorflow Extended (TFX) came up? Since the time Google has publicized Tensorflow, its application in Deep Continue Reading

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