To satisfy all the needs of the growing number of consumers and process enormous data chunks, data science algorithms are vital. Let’s consider several of widespread and efficient data science use cases in the travel industry.
TensorFlow.js brings TensorFlow and Keras to the the JavaScript ecosystem, supporting both Node.js and browser-based applications. Read a summary of the paper which describes the design, API, and implementation of TensorFlow.js.
The many standard tasks in computer vision all require special consideration: classification, detection, segmentation, pose estimation, enhancement and restoration, and action recognition. Let me show you how to do everything in Computer Vision with Deep Learning!
Google’s BERT algorithm has emerged as a sort of “one model to rule them all.” BERT builds on two key ideas that have been responsible for many of the recent advances in NLP: (1) the transformer architecture and (2) unsupervised pre-training.
Your current ML workflow probably chains together several functions executed linearly. Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG.
Real world data is messy and needs to be cleaned before it can be used for analysis. Industry experts say the data preprocessing step can easily take 70% to 80% of a data scientist's time on a project.
We outline the importance of asking yourself the questions you need to ask to effectively produce something that the business wants. Once you start asking questions, it’ll become second nature and you’ll immediately see the value and find yourself asking even more questions as you gain more experience.
This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset
Word embeddings such as Word2Vec is a key AI method that bridges the human understanding of language to that of a machine and is essential to solving many NLP problems. Here we discuss applications of Word2Vec to Survey responses, comment analysis, recommendation engines, and more.
Check out this collection of six books which tackle the hard skills required to make sense of the changing field known as open data and muse on the ethical implications of a digitally connected world.
Python’s syntax is very clean and short in length. Python is open-source and a portable language which supports a large standard library. Buy why Python for data science? Read on to find out more.
In our free guide, we show you how and where you can use extracted data from PDFs, and explain the necessary qualities you should be looking for when evaluating extraction tools.
We take a look at the arguments against implementing a machine learning solution, and the occasions when the problems faced are not ML problems and can perhaps be solved using optimization, exploratory data analysis tasks or problems that can be solved with simple statistics.
The Jupyter Project began in 2014 for interactive and scientific computing. Fast forward 5 years and now Jupyter is one of the most widely adopted Data Science IDE's on the market and gives the user access to Python and R
This is a short analysis of the interpretability of BERT contextual word representations. Does BERT learn a semantic vector representation like Word2Vec?
In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You’ll be able to get right down into the ML and never have to worry about installing packages ever again.
This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them.
In a constantly changing landscape and with many companies, the roles and responsibilities of data engineers, analysts, and data scientists are changing, forcing the introduction of a new role: The Analytics Engineer.
If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library.
AI represents a step change in humanity’s ability to rise to its greatest challenges. We explore three areas in which AI can contribute to the UN’s Global Goals - and why we could fall short.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Natural Language Processing and how it is used in social media analytics.
We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks.
Feature engineering plays a key role in machine learning, data mining, and data analytics. This article provides a general definition for feature engineering, together with an overview of the major issues, approaches, and challenges of the field.
We reflect on some of the best examples of Data Visualization throughout 2018, before focussing on some of the not-so-good and how these can be improved.
Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.
The following is a method I developed, which is based on my personal experience managing a data-science-research team and was tested with multiple projects. In the next sections, I’ll review the different types of research from a time point-of-view, compare development and research workflow approaches and finally suggest my work methodology.
This article outlines 10 top trending technologies for 2019, a list which covers diverse topics such as security, IoT, reinforcement learning, energy sustainability, smart cities, and much more.
However despite its massive popularity, many professionals still use this algorithm as a black box. As such, the purpose of this article is to lay an intuitive framework for this powerful machine learning technique.
If you intend to use data visualizations in a presentation or publication, be certain that your audience will understand and trust the information. Here are six mistakes you will want to avoid.
Check out this visualization for outlier detection methods, and the Python project from which it comes, a toolkit for easily implementing outlier detection methods on your own.
This article uses direct marketing campaign data from a Portuguese banking institution to predict if a customer will subscribe for a term deposit. We’ll be working with R’s Caret package to achieve this.
Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.
We provide some reasoning behind the high cost factor of hiring a data scientist, including the increasing amount of data ready to be analyzed, the structural shortage of people with the appropriate skills, and more.
4 Reasons Why Your Machine Learning Code is Probably Bad
Python Data Science for Beginners
Top 10 Technology Trends of 2019
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