2021

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11 Best Companies to Work for as a Data Scientist

KDnuggets

This list of best data science companies aims to go beyond the usual and expected. Some great and perhaps underrated options to get a job as a data scientist.

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Intent Classification with Convolutional Neural Networks

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction Text classification is a machine-learning approach that groups text into pre-defined categories. It is an integral tool in Natural Language Processing (NLP) used for varied tasks like spam and non-spam email classification, sentiment analysis of movie reviews, detection of hate speech in social […].

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Alternative Feature Selection Methods in Machine Learning

KDnuggets

Feature selection methodologies go beyond filter, wrapper and embedded methods. In this article, I describe 3 alternative algorithms to select predictive features based on a feature importance score.

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How AI/ML Technology Integration Will Help Business in Achieving Goals in 2022

KDnuggets

AI/ML systems have a wide range of applications in a variety of industries and sectors, and this article highlights the top ways AI/ML will impact your small business in 2022.

ML 400
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Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

Speaker: Tamara Fingerlin, Developer Advocate

Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.

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6 Predictive Models Every Beginner Data Scientist Should Master

KDnuggets

Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This post brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.

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Hands-On Reinforcement Learning Course, Part 1

KDnuggets

Start your learning journey in Reinforcement Learning with this first of two part tutorial that covers the foundations of the technique with examples and Python code.

Python 400

More Trending

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10 Key AI & Data Analytics Trends for 2022 and Beyond

KDnuggets

What AI and data analytics trends are taking the industry by storm this year? This comprehensive review highlights upcoming directions in AI to carefully watch and consider implementing in your personal work or organization.

Analytics 400
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Building a solid data team

KDnuggets

How do you put together a solid data science team when it comes to developing data-driven products? A variety of roles are available to consider, so which ones do you need and which are most crucial?

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Should You Become a Freelance Artificial Intelligence Engineer?

KDnuggets

Take the first step towards your machine learning engineering career and explore the UC San Diego Extension Machine Learning Engineering Bootcamp today. Those with prior software engineering or data science experience are encouraged to apply.

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Pokemon Prediction using Random Forest

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Overview This Pokemon will analyze the pokemon dataset and predict whether the Pokemon is legendary based on the features provided. We will discuss everything from scratch; we will go from CSV to model building with line by line explanation of code. Let’s get started. Image […].

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Agent Tooling: Connecting AI to Your Tools, Systems & Data

Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage

There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.

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Building an End- to-End Data Science App with Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. [link] Overview In this article, we will detail the need for data scientists to quickly develop a Data Science App, with the objective of presenting to their users and customers, the results of Machine Learning experiments. We have detailed a roadmap for the […]. The post Building an End- to-End Data Science App with Python appeared first on Analytics Vidhya.

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Deep Neural Networks Don’t Lead Us Towards AGI

KDnuggets

Machine learning techniques continue to evolve with increased efficiency for recognition problems. But, they still lack the critical element of intelligence, so we remain a long way from attaining AGI.

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How do Neural Networks really work?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon The math behind Neural Networks Neural networks form the core of deep learning, a subset of machine learning that I introduced in my previous article. People exposed to artificial intelligence generally have a good high-level idea of how a neural network works?—?data is passed […].

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How to Get Certified as a Data Scientist

KDnuggets

If you are early in your journey to becoming a Data Scientist, an interesting option is to earn certification by DataCamp, and this guide offers tips that will help beginners complete the challenges.

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How to Modernize Manufacturing Without Losing Control

Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives

Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri

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Why Machine Learning Engineers are Replacing Data Scientists

KDnuggets

The hiring run for data scientists continues along at a strong clip around the world. But, there are other emerging roles that are demonstrating key value to organizations that you should consider based on your existing or desired skill sets.

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Most Common SQL Mistakes on Data Science Interviews

KDnuggets

Sure, we all make mistakes -- which can be a bit more painful when we are trying to get hired -- so check out these typical errors applicants make while answering SQL questions during data science interviews.

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3 Differences Between Coding in Data Science and Machine Learning

KDnuggets

The terms ‘data science’ and ‘machine learning’ are often used interchangeably. But while they are related, there are some glaring differences, so let’s take a look at the differences between the two disciplines, specifically as it relates to programming.

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Using Data Visualization to Explore the Human Space Race!

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Humankind has always looked up to the stars. Since the dawn of civilization, we have mapped constellations, named planets after Gods and so on. We have seen signs and visions in celestial bodies. In the previous century, we finally had the technology to […]. The post Using Data Visualization to Explore the Human Space Race!

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Automation, Evolved: Your New Playbook for Smarter Knowledge Work

Speaker: Frank Taliano

Documents are the backbone of enterprise operations, but they are also a common source of inefficiency. From buried insights to manual handoffs, document-based workflows can quietly stall decision-making and drain resources. For large, complex organizations, legacy systems and siloed processes create friction that AI is uniquely positioned to resolve.

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Creating ChatBot Using Natural Language Processing in Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Are you fed up with waiting in long lines to speak with a customer support representative? Can you recall the last time you interacted with customer service? There’s a chance you were contacted by a bot rather than human customer support professional. We […]. The post Creating ChatBot Using Natural Language Processing in Python appeared first on Analytics Vidhya.

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Where NLP is heading

KDnuggets

Natural language processing research and applications are moving forward rapidly. Several trends have emerged on this progress, and point to a future of more exciting possibilities and interesting opportunities in the field.

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Building an end-to-end Polynomial Regression Model in R

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Overview. Regression analysis is used to solve problems of prediction based on data statistical parameters. In this article, we will look at the use of a polynomial regression model on a simple example using real statistic data. We will analyze the relationship between […]. The post Building an end-to-end Polynomial Regression Model in R appeared first on Analytics Vidhya.

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Build Face Recognition Attendance System using Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction In this article, you will learn how to build a face-recognition system using Python. Face recognition is a step further to face detection. In face detection, we only detect the location of the human face in an image but in face recognition, we […]. The post Build Face Recognition Attendance System using Python appeared first on Analytics Vidhya.

Python 400
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The 2nd Generation of Innovation Management: A Survival Guide

Speaker: Chris Townsend, VP of Product Marketing, Wellspring

Over the past decade, companies have embraced innovation with enthusiasm—Chief Innovation Officers have been hired, and in-house incubators, accelerators, and co-creation labs have been launched. CEOs have spoken with passion about “making everyone an innovator” and the need “to disrupt our own business.” But after years of experimentation, senior leaders are asking: Is this still just an experiment, or are we in it for the long haul?

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10 AI Project Ideas in Computer Vision

KDnuggets

The field of computer vision has seen the development of very powerful applications leveraging machine learning. These projects will introduce you to these techniques and guide you to more advanced practice to gain a deeper appreciation for the sophistication now available.

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Top 4 Data Integration Tools for Modern Enterprises

KDnuggets

Maintaining a centralized data repository can simplify your business intelligence initiatives. Here are four data integration tools that can make data more valuable for modern enterprises.

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Here’s How to use Sankey Diagrams for Data Visualization

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction to Sankey Diagram for Data Visualization Very often, we are in a situation where we would have to visualize how data flows between entities. For example, let’s take the case of how residents have migrated from one country to another within the […]. The post Here’s How to use Sankey Diagrams for Data Visualization appeared first on Analytics Vidhya.

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A Guide to Automated Deep/Machine Learning for Natural Language Processing: Text Prediction

Analytics Vidhya

This article was published as a part of the Data Science Blogathon This article starts by discussing the fundamentals of Natural Language Processing (NLP) and later demonstrates using Automated Machine Learning (AutoML) to build models to predict the sentiment of text data. Other applications of NLP are for translation, speech recognition, chatbot, etc.

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Optimizing The Modern Developer Experience with Coder

Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

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On-Device Deep Learning: PyTorch Mobile and TensorFlow Lite

KDnuggets

PyTorch and TensorFlow are the two leading AI/ML Frameworks. In this article, we take a look at their on-device counterparts PyTorch Mobile and TensorFlow Lite and examine them more deeply from the perspective of someone who wishes to develop and deploy models for use on mobile platforms.

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A Quick Tutorial on Clustering for Data Science Professionals

Analytics Vidhya

This is article was published as a part of the Data Science Blogathon. Welcome to this wide-ranging article on clustering in data science! There’s a lot to unpack so let’s dive straight in. In this article, we will be discussing what is clustering, why is clustering required, various applications of clustering, a brief about the […].

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Top Stories, Nov 15-21: 19 Data Science Project Ideas for Beginners

KDnuggets

Also: How I Redesigned over 100 ETL into ELT Data Pipelines; Where NLP is heading; Don’t Waste Time Building Your Data Science Network; Data Scientists: How to Sell Your Project and Yourself.

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Getting Started with Data Analysis using Power BI

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. What is Power BI? Microsoft‘s business analytics product, Power BI, delivers interactive data visualization BI capabilities that allow users to see and share data and insights throughout their organisation. Power BI provides insight data by using data interactively and exploring it by visualizations. […].

Power BI 400
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15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?