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• Top 7 Data Science Use Cases in Travel - Feb 28, 2019.
  
  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.

  Data Science, Travel, Use Cases

• TensorFlow.js: Machine learning for the web and beyond - Feb 28, 2019.
  
  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.

  Javascript, Keras, Neural Networks, TensorFlow

• How to do Everything in Computer Vision - Feb 27, 2019.
  
  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!

  Computer Vision, Convolutional Neural Networks, Image Classification, Image Recognition, Neural Networks, Object Detection

• Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters - Feb 27, 2019.
  
  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.

  Attention, BERT, NLP, Word Embeddings

• 4 Reasons Why Your Machine Learning Code is Probably Bad - Feb 26, 2019.
  
  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.

  Data Science, Machine Learning, Programming, Python, Workflow

• Simple Yet Practical Data Cleaning Codes - Feb 26, 2019.
  
  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.

  Data Cleaning, Data Preprocessing, Python

• Asking Great Questions as a Data Scientist - Feb 25, 2019.
  
  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.

  Data Science, Data Scientist

• What are Some “Advanced” AI and Machine Learning Online Courses? - Feb 22, 2019.
  
  Where can you find not-so-common, but high-quality online courses (Free) for ‘advanced’ machine learning and artificial intelligence?

  AI, Machine Learning, MOOC, Online Education

• Artificial Neural Network Implementation using NumPy and Image Classification - Feb 21, 2019.
  
  This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset

  Pages: 1 2

  Deep Learning, Machine Learning, Neural Networks, numpy, Python

• Word Embeddings in NLP and its Applications - Feb 20, 2019.
  
  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.

  Applications, NLP, Recommender Systems, Word Embeddings, word2vec

• State of the art in AI and Machine Learning – highlights of papers with code - Feb 20, 2019.
  
  We introduce papers with code, the free and open resource of state-of-the-art Machine Learning papers, code and evaluation tables.

  AI, Machine Learning, Multitask Learning, NLP, Papers with code, Recommender Systems, Semantic Segmentation, TensorFlow, Transfer Learning

• 6 Books About Open Data Every Data Scientist Should Read - Feb 20, 2019.
  
  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.

  Books, Data Science, Open Data

• Python Data Science for Beginners - Feb 20, 2019.
  
  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.

  Beginners, Data Science, Matplotlib, numpy, Pandas, Python, scikit-learn, SciPy

• PDF Data Extraction: What You Need to Know - Feb 19, 2019.
  
  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.

  Data Processing, Datalogics, PDF, Text Analysis

• Automatic Machine Learning is broken - Feb 19, 2019.
  
  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.

  Automated Machine Learning, AutoML, Data Preparation, Deployment

• Running R and Python in Jupyter - Feb 19, 2019.
  
  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

  IPython, Jupyter, Python, R

• Are BERT Features InterBERTible? - Feb 19, 2019.
  
  This is a short analysis of the interpretability of BERT contextual word representations. Does BERT learn a semantic vector representation like Word2Vec?

  BERT, Interpretability, NLP, Word Embeddings

• How to Setup a Python Environment for Machine Learning - Feb 18, 2019.
  
  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.

  Machine Learning, Programming, Python

• Deep Multi-Task Learning – 3 Lessons Learned - Feb 15, 2019.
  
  We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.

  Deep Learning, Deep Neural Network, Machine Learning, Neural Networks, Optimization, TensorFlow

• A comprehensive survey on graph neural networks - Feb 15, 2019.
  
  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.

  Graphs, Neural Networks

• Top 10 Data Science Use Cases in Telecom - Feb 14, 2019.
  
  In this article, we attempt to present the most relevant and efficient data science use cases in the field of telecommunication.

  Data Science, Telecom, Use Cases

• The Analytics Engineer – new role in the data team - Feb 13, 2019.
  
  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.

  Analytics, Analytics Team, Data Science Team, Engineer, Skills

• An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning - Feb 13, 2019.
  
  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.

  Machine Learning, Python, scikit-learn

• How AI can help solve some of humanity’s greatest challenges – and why we might fail - Feb 12, 2019.
  
  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.

  AI, Finance, Healthcare, Innovation, Social Good, United Nations

• Natural Language Processing for Social Media - Feb 12, 2019.
  
  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.

  Interview, NLP, Social Media

• Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 11, 2019.
  
  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.

  Alteryx, Data Science Platform, Dataiku, DataRobot, Gartner, Google, H2O, IBM, Knime, Machine Learning, Magic Quadrant, MathWorks, Microsoft, RapidMiner, SAS, TIBCO

• A Quick Guide to Feature Engineering - Feb 11, 2019.
  
  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.

  Feature Engineering, Feature Extraction, Feature Selection

• Data Science For Our Mental Development - Feb 11, 2019.
  
  In this blog, I aim to generalize how AI can help us with mental development in the future as well as discuss some of the present-day solutions.

  Data Science, Development, Emotion

• The Best and Worst Data Visualizations of 2018 - Feb 8, 2019.
  
  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.

  Advice, Best Practices, Data Visualization, Failure, Sankey

• Is Domain Knowledge a Hurdle to Start a Career in Data? - Feb 8, 2019.
  
  How would I decide which domain to choose, while starting my career in data? Is it an obstacle?

  Beginners, Career, Domain Knowledge

• Neural Networks – an Intuition - Feb 7, 2019.
  
  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.

  Explained, History, Machine Learning, Neural Networks, Perceptron

• Data-science? Agile? Cycles? My method for managing data-science projects in the Hi-tech industry. - Feb 7, 2019.
  
  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.

  Agile, Data Science, Development, Project

• Top 10 Technology Trends of 2019 - Feb 7, 2019.
  
  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.

  2019 Predictions, Automation, Cloud, Energy, IoT, Reinforcement Learning, Security, Trends

• How I used NLP (Spacy) to screen Data Science Resumes - Feb 6, 2019.
  
  A real life example of when using NLP can help filter down a list of candidates for a job opening, with full source code and methodology.

  Data Science, Hiring, NLP, Resume

• Understanding Gradient Boosting Machines - Feb 6, 2019.
  
  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.

  Adaboost, Decision Trees, Gradient Boosting, R

• 6 Data Visualization Disasters – How to Avoid Them - Feb 5, 2019.
  
  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.

  Advice, Data Visualization, Failure

• From Good to Great Data Science, Part 1: Correlations and Confidence - Feb 5, 2019.
  
  With the aid of some hospital data, part one describes how just a little inexperience in statistics could result in two common mistakes.

  Correlation, Data Science, Python, Statistics

• Intuitive Visualization of Outlier Detection Methods - Feb 5, 2019.
  
  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.

  Cheat Sheet, Outliers, Python

• The Essential Data Science Venn Diagram - Feb 4, 2019.
  
  A deeper examination of the interdisciplinary interplay involved in data science, focusing on automation, validity and intuition.

  Analytics, Data Science, Machine Learning, Statistics, Venn Diagram

• Using Caret in R to Classify Term Deposit Subscriptions for a Bank - Feb 4, 2019.
  
  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.

  Banking, Classification, R

• Five Ways Your Safety Depends on Machine Learning - Feb 2, 2019.
  
  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.

  AI, Eric Siegel, Machine Learning, Safety

• Data Scientists: Why are they so expensive to hire? - Feb 1, 2019.
  
  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.

  Data Scientist, Hiring, Salary

• Trending Deep Learning Github Repositories - Feb 1, 2019.
  
  Check these pair of resources for trending and top GitHub deep learning repositories for some new ideas on what to be looking out for.

  Deep Learning, GitHub, Trends