Naveen Pandey
Bio
I'm a Data Scientist who loves working with numbers and trends | Ex- Jr. Data Scientist | Ex- Deep Learning Application Developer | Reader | Writer | Founder
Stories (8/0)
10 Common Data Science Interview Questions and How to Answer Them?
Data science has become a very competitive field and it is important to prepare for data science interviews if you are looking for your dream job. As part of the interview process, you can expect to be asked a number of questions to assess your knowledge, skills and experience in the field. In this blog post, we’ll cover 10 common data interview questions and provide tips on how to answer them. These questions cover a wide range of topics from basic statistics to machine learning algorithms. By the end of this article, you will have a better understanding of what to expect in a data science interview and how to prepare for it.
By Naveen Pandey10 months ago in 01
Facial Recognition: Applications and Challenges in Computer Vision
Have you ever wondered how your smartphone is able to unlocks when it recognizes your face? Or how law enforcement agencies identify suspects from surveillance footage? The answer lies is the remarkable technology, which is known as face recognition. In this article, we will talk about the facial recognition, its applications and the challenges it face in the field of computer vision.
By Naveen Pandey10 months ago in 01
10 Essential Tips for Building ML Models for Anomaly Detection
Anomaly detection is an important component of many data-driven applications. It enables us to efficiently identify anomalous behaviour and detect malicious activities that may otherwise be difficult to spot. In this blog post, we will discuss 10 essential tips for constructing machine learning models for anomaly detection with respect to data pre-processing, feature selection and engineering, model selection and parameter tuning, training the model, evaluating performance metrics, deployment and monitoring results. By following these tips, you will be able to develop effective models that properly detect anomalies within your datasets accurately
By Naveen Pandey10 months ago in 01
Understanding Image Processing: Basics and Techniques in Computer Vision
In present day digital generation, image plays a very important role in various domain, starting from social media to media diagnostics. The field of computer vision focuses on extracting the meaningful information from images. Image processing, a fundamental aspect of computer vision, includes manipulating and analyzing images to enhance their quality, extract valuable features, and enable automated interpretation.
By Naveen Pandey11 months ago in 01
Introduction to Computer Vision: Exploring the world of visual intelligence
Welcome to the first day of our computer vision course! We’re excited to have you join us on this fascinating journey into the world of visual intelligence. Throughout this course, we’ll explore the basic concepts, techniques, and practical applications of computer vision.
By Naveen Pandey11 months ago in 01
Building Neural Networks with TensorFlow: A Step-by-Step Tutorial
Neural networks have changed the field of machine learning, allowing computers to learn and make predictions based on complex data. TensorFlow is one of the most popular deep learning frameworks you can use to build and train neural networks. In this article, we will see a step-by-step tutorial for building neural networks using TensorFlow in Python.
By Naveen Pandey11 months ago in 01
A Comprehensive Guide to Tuples Comprehension in Python
Have you ever wanted to efficiently manipulate and iterate over many in Python? Tuple comprehension is a powerful technique that allows you to concisely create and manipulate tuples. In this article, we'll explore the concept of tuple comprehension, its syntax, and its Python code implementation.
By Naveen Pandey11 months ago in Education
Mastering Keras: Best Practices for Optimizing Your Python Models
The popularity of Keras, a Python library for building and training deep learning models, has increased as deep learning evolves. It is known for its user-friendly API, which allows developers to quickly build, test, and deploy deep learning models. This article will cover the best practices for optimizing Keras models, including understanding Python Keras libraries, preprocessing data, selecting appropriate activation functions, optimizing with regularization techniques, improving model performance, avoiding common mistakes, and using advanced Keras techniques for deep learning models.
By Naveen Pandey11 months ago in Education
