Search results for How to Invest

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• Machine Learning and Deep Link Graph Analytics: A Powerful Combination

  We investigate how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.

  https://www.kdnuggets.com/2019/04/machine-learning-graph-analytics.html

• Sisense BloX – Go Beyond Dashboards

  Introducing Sisense BloX, the tool that allows you to integrate your business platforms inside your dashboards using prebuilt templates. Users stay within the dashboard environment and go from understanding insights to taking action—in one click.

  https://www.kdnuggets.com/2019/04/sisense-blox-beyond-dashboards.html

• Building a Flask API to Automatically Extract Named Entities Using SpaCy

  This article discusses how to use the Named Entity Recognition module in spaCy to identify people, organizations, or locations in text, then deploy a Python API with Flask.

  https://www.kdnuggets.com/2019/04/building-flask-api-automatically-extract-named-entities-spacy.html

• How can quantum computing be useful for Machine Learning

  We investigate where quantum computing and machine learning could intersect, providing plenty of use cases, examples and technical analysis.

  https://www.kdnuggets.com/2019/04/quantum-computing-machine-learning.html

• Advice for New Data Scientists">Advice for New Data Scientists

  We provide advice for junior data scientists as they begin their career, with tips and commentary from a tech lead at Airbnb.

  https://www.kdnuggets.com/2019/04/advice-new-data-scientists.html

• Training a Champion: Building Deep Neural Nets for Big Data Analytics

  Introducing Sisense Hunch, the new way of handling Big Data sets that uses AQP technology to construct Deep Neural Networks (DNNs) which are trained to learn the relationships between queries and their results in these huge datasets.

  https://www.kdnuggets.com/2019/04/sisense-deep-neural-nets-big-data-analytics.html

• How to DIY Your Data Science Education

  Some people find the path of formal education works well for them, but this may not work for everyone, in every situation. Here are eight ways that you can take a DIY approach to your data science education.

  https://www.kdnuggets.com/2019/04/diy-your-data-science-education.html

• 7 Qualities Your Big Data Visualization Tools Absolutely Must Have and 10 Tools That Have Them">7 Qualities Your Big Data Visualization Tools Absolutely Must Have and 10 Tools That Have Them

  Without the right visualization tools, raw data is of little use. Data visualization helps present the data in an interactive visual format. Here are the qualities to look for in a data visualization tool.

  https://www.kdnuggets.com/2019/04/7-qualities-big-data-visualization-tools.html

• Top 10 Coding Mistakes Made by Data Scientists">Top 10 Coding Mistakes Made by Data Scientists

  Here is a list of 10 common mistakes that a senior data scientist — who is ranked in the top 1% on Stackoverflow for python coding and who works with a lot of (junior) data scientists — frequently sees.

  https://www.kdnuggets.com/2019/04/top-10-coding-mistakes-data-scientists.html

• Checklist for Debugging Neural Networks

  Check out these tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.

  https://www.kdnuggets.com/2019/03/checklist-debugging-neural-networks.html

• Top 8 Data Science Use Cases in Manufacturing

  Data science is said to change the manufacturing industry dramatically. Let's take under consideration several data science use cases in manufacturing that have already become common and brought benefits to the manufacturers.

  https://www.kdnuggets.com/2019/03/top-8-data-science-use-cases-manufacturing.html

• Deep Compression: Optimization Techniques for Inference & Efficiency

  We explain deep compression for improved inference efficiency, mobile applications, and regularization as technology cozies up to the physical limits of Moore's law.

  https://www.kdnuggets.com/2019/03/deep-compression-optimization-techniques-inference-efficiency.html

• Deploy your PyTorch model to Production

  This tutorial aims to teach you how to deploy your recently trained model in PyTorch as an API using Python.

  https://www.kdnuggets.com/2019/03/deploy-pytorch-model-production.html

• AI: Arms Race 2.0

  An analysis of the current state of the competition between US, Europe, and China in AI, examining research, patent publications, global datasphere, devices and IoT, people, and more.

  https://www.kdnuggets.com/2019/03/ai-arms-race-20.html

• Who is a typical Data Scientist in 2019?">Who is a typical Data Scientist in 2019?

  We investigate what a typical data scientist looks like and see how this differs from this time last year, looking at skill set, programming languages, industry of employment, country of employment, and more.

  https://www.kdnuggets.com/2019/03/typical-data-scientist-2019.html

• Beating the Bookies with Machine Learning

  We investigate how to use a custom loss function to identify fair odds, including a detailed example using machine learning to bet on the results of a darts match and how this can assist you in beating the bookmaker.

  https://www.kdnuggets.com/2019/03/beating-bookies-machine-learning.html

• 3 Reasons Why AutoML Won’t Replace Data Scientists Yet

  We dispel the myth that AutoML is replacing Data Scientists jobs by highlighting three factors in Data Science development that AutoML can’t solve.

  https://www.kdnuggets.com/2019/03/why-automl-wont-replace-data-scientists.html

• 4 Reasons Why Your Machine Learning Code is Probably Bad">4 Reasons Why Your Machine Learning Code is Probably Bad

  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.

  https://www.kdnuggets.com/2019/02/4-reasons-machine-learning-code-probably-bad.html

• Asking Great Questions as a Data Scientist">Asking Great Questions as a Data Scientist

  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.

  https://www.kdnuggets.com/2019/02/asking-great-questions-data-scientist.html

• 6 Books About Open Data Every Data Scientist Should Read

  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.

  https://www.kdnuggets.com/2019/02/6-books-open-data-every-data-scientist-read.html

• How AI can help solve some of humanity’s greatest challenges – and why we might fail

  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.

  https://www.kdnuggets.com/2019/02/ai-help-solve-humanity-challenges.html

• Data Science For Our Mental Development

  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.

  https://www.kdnuggets.com/2019/02/data-science-mental-development.html

• Is Domain Knowledge a Hurdle to Start a Career in Data?

  How would I decide which domain to choose, while starting my career in data? Is it an obstacle?

  https://www.kdnuggets.com/2019/02/domain-knowledge-hurdle-career-data.html

• Neural Networks – an Intuition

  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.

  https://www.kdnuggets.com/2019/02/neural-networks-intuition.html

• Top 10 Technology Trends of 2019">Top 10 Technology Trends of 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.

  https://www.kdnuggets.com/2019/02/top-10-technology-trends-2019.html

• 6 Data Visualization Disasters – How to Avoid Them

  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.

  https://www.kdnuggets.com/2019/02/data-visualization-disasters-avoid.html

• From Good to Great Data Science, Part 1: Correlations and Confidence

  With the aid of some hospital data, part one describes how just a little inexperience in statistics could result in two common mistakes.

  https://www.kdnuggets.com/2019/02/good-great-data-science-correlations-confidence.html

• Past 2019 Meetings / Conferences on AI, Analytics, Big Data, Data Science, and Machine Learning

  Past | 2019 Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Read more »

  https://www.kdnuggets.com/meetings/past-meetings-2019.html

• Five Ways Your Safety Depends on Machine Learning

  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.

  https://www.kdnuggets.com/2019/02/dr-data-five-ways-safety-depends-machine-learning.html

• The Algorithms Aren’t Biased, We Are

  We explain the concept of bias and how it can appear in your projects, share some illustrative examples, and translate the latest academic research on “algorithmic bias.”

  https://www.kdnuggets.com/2019/01/algorithms-arent-biased-we-are.html

• Cracking the Data Scientist Interview

  After interviewing with over 50 companies for Data Scientist/Machine Learning Engineer, I am going to frame my experiences in the Q&A format and try to debunk any myths that beginners may have in their quest for becoming a Data Scientist.

  https://www.kdnuggets.com/2019/01/cracking-data-scientist-interview.html

• Your AI skills are worth less than you think">Your AI skills are worth less than you think

  We are in the middle of an AI boom. That doesn’t mean that making your AI startup succeed is easy. I think there are some important pitfalls ahead of anyone trying to build their business around AI.

  https://www.kdnuggets.com/2019/01/your-ai-skills-worth-less-than-you-think.html

• The Data Science Gold Rush: Top Jobs in Data Science and How to Secure Them

  Because big data touches almost every industry across the board, those who aren’t already working in data and analytics will soon be utilizing the technology for its undeniable business benefits. Whichever way you slice it, the future of work is through data.

  https://www.kdnuggets.com/2019/01/top-jobs-data-science.html

• Data Science Project Flow for Startups

  The aim of this post, then, is to present the characteristic project flow that I have identified in the working process of both my colleagues and myself in recent years. Hopefully, this can help both data scientists and the people working with them to structure data science projects in a way that reflects their uniqueness.

  https://www.kdnuggets.com/2019/01/data-science-project-flow-startups.html

• How AI and Data Science is Changing the Utilities Industry

  Together, artificial intelligence (AI) and data science are causing positive developments for the utilities providers that choose to investigate these things. Here are some examples of technology at work.

  https://www.kdnuggets.com/2019/01/how-ai-data-science-changing-utilities-industry.html

• Why Ice Cream Is Linked to Shark Attacks – Correlation/Causation Smackdown

  Why are soda and ice cream each linked to violence? This article delivers the final word on what people mean by "correlation does not imply causation."

  https://www.kdnuggets.com/2019/01/dr-data-ice-cream-linked-shark-attacks.html

• Comparing Machine Learning Models: Statistical vs. Practical Significance

  Is model A or B more accurate? Hmm… In this blog post, I’d love to share my recent findings on model comparison.

  https://www.kdnuggets.com/2019/01/comparing-machine-learning-models-statistical-vs-practical-significance.html

• End To End Guide For Machine Learning Projects">End To End Guide For Machine Learning Projects

  Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.

  https://www.kdnuggets.com/2019/01/end-to-end-guide-machine-learning-project.html

• Why Vegetarians Miss Fewer Flights – Five Bizarre Insights from Data

  A frenzy of number-crunching is churning out a heap of insights that are colorful, sometimes surprising, and often valuable. We explain how this works, and investigate five bizarre discoveries found in data.

  https://www.kdnuggets.com/2019/01/dr-data-five-bizarre-insights-from-data.html

• Explainable Artificial Intelligence

  We outline the necessity of explainable AI, discuss some of the methods in academia, take a look at explainability vs accuracy, investigate use cases, and more.

  https://www.kdnuggets.com/2019/01/explainable-ai.html

• The Role of the Data Engineer is Changing

  The role of the data engineer in a startup data team is changing rapidly. Are you thinking about it the right way?

  https://www.kdnuggets.com/2019/01/role-data-engineer-changing.html

• Supervised Learning: Model Popularity from Past to Present

  An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?

  https://www.kdnuggets.com/2018/12/supervised-learning-model-popularity-from-past-present.html

• A Case For Explainable AI & Machine Learning

  In support of the explainable AI cause, we present a variety of use cases covering operational needs, regulatory compliance and public trust and social acceptance.

  https://www.kdnuggets.com/2018/12/explainable-ai-machine-learning.html

• Deep learning in Satellite imagery

  This article outlines possible sources of satellite imagery, what its properties are and how this data can be utilised using R.

  https://www.kdnuggets.com/2018/12/deep-learning-satellite-imagery.html

• KDnuggets Site Map

  About KDnuggets Awards and Honors for KDnuggets Companies, offering Bioinformatics products and solutions Data Science and Analytics products Consulting and Training Data Warehousing and OLAP Read more »

  https://www.kdnuggets.com/about/site-map.html

• Six Steps to Master Machine Learning with Data Preparation

  To prepare data for both analytics and machine learning initiatives teams can accelerate machine learning and data science projects to deliver an immersive business consumer experience that accelerates and automates the data-to-insight pipeline by following six critical steps.

  https://www.kdnuggets.com/2018/12/six-steps-master-machine-learning-data-preparation.html

• How will automation tools change data science?

  This article provides an overview of recent trends in machine learning and data science automation tools and addresses how those tools will change data science.

  https://www.kdnuggets.com/2018/12/automation-data-science.html

• Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019">Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019

  This is a collection of data science, machine learning, analytics, and AI predictions for next year from a number of top industry organizations. See what the insiders feel is on the horizon for 2019!

  https://www.kdnuggets.com/2018/12/predictions-industry-2019.html

• Solve any Image Classification Problem Quickly and Easily

  This article teaches you how to use transfer learning to solve image classification problems. A practical example using Keras and its pre-trained models is given for demonstration purposes.

  https://www.kdnuggets.com/2018/12/solve-image-classification-problem-quickly-easily.html

• Introduction to Named Entity Recognition

  Named Entity Recognition is a tool which invariably comes handy when we do Natural Language Processing tasks. Read on to find out how.

  https://www.kdnuggets.com/2018/12/introduction-named-entity-recognition.html

• Should you become a data scientist?">Should you become a data scientist?

  An overview of the current situation for data scientists, from its origins and history, to the recent growth in job postings, and looking at what changes the future might bring.

  https://www.kdnuggets.com/2018/12/should-i-become-a-data-scientist.html

• The Machine Learning Project Checklist">The Machine Learning Project Checklist

  In an effort to further refine our internal models, this post will present an overview of Aurélien Géron's Machine Learning Project Checklist, as seen in his bestselling book, "Hands-On Machine Learning with Scikit-Learn & TensorFlow."

  https://www.kdnuggets.com/2018/12/machine-learning-project-checklist.html

• Common mistakes when carrying out machine learning and data science">Common mistakes when carrying out machine learning and data science

  We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!

  https://www.kdnuggets.com/2018/12/common-mistakes-data-science.html

• Four Techniques for Outlier Detection

  There are many techniques to detect and optionally remove outliers from a dataset. In this blog post, we show an implementation in KNIME Analytics Platform of four of the most frequently used - traditional and novel - techniques for outlier detection.

  https://www.kdnuggets.com/2018/12/four-techniques-outlier-detection.html

• 6 Step Plan to Starting Your Data Science Career

  When people want to launch data science careers but haven't made the first move, they're in a scenario that's understandably daunting and full of uncertainty. Here are six steps to get started.

  https://www.kdnuggets.com/2018/12/6-step-plan-starting-data-science-career.html

• Handling Imbalanced Datasets in Deep Learning

  It’s important to understand why we should do it so that we can be sure it’s a valuable investment. Class balancing techniques are only really necessary when we actually care about the minority classes.

  https://www.kdnuggets.com/2018/12/handling-imbalanced-datasets-deep-learning.html

• AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019">AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019

  Review of 2018 and Predictions for 2019 from our panel of experts, including Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Bill Schmarzo, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood.

  https://www.kdnuggets.com/2018/12/predictions-data-science-analytics-2019.html

• A Complete Guide to Choosing the Best Machine Learning Course">A Complete Guide to Choosing the Best Machine Learning Course

  A collection of the best courses covering machine learning concepts and techniques, including supervised and unsupervised learning, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer.

  https://www.kdnuggets.com/2018/11/simplilearn-complete-guide-machine-learning-course.html

• Deep Learning for the Masses (… and The Semantic Layer)

  Deep learning is everywhere right now, in your watch, in your television, your phone, and in someway the platform you are using to read this article. Here I’ll talk about how can you start changing your business using Deep Learning in a very simple way. But first, you need to know about the Semantic Layer.

  https://www.kdnuggets.com/2018/11/deep-learning-masses-semantic-layer.html

• 8 Reasons to Take Data Analytics Certification Courses

  We outline some of the benefits of taking data analytics classes, including the huge job opportunities, the current gap in the market, the salary aspect, the flexibility of working in any sector, and more.

  https://www.kdnuggets.com/2018/11/8-reasons-take-data-analytics-certification-courses.html

• 6 Goals Every Wannabe Data Scientist Should Make for 2019

  Looking to embark on a new path as a data scientist? That goal may be worthy, but it's essential for people to also set goals for 2019 that will help them get closer to that broader aim.

  https://www.kdnuggets.com/2018/11/6-goals-every-wannabe-data-scientist-2019.html

• Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices

  LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price.

  https://www.kdnuggets.com/2018/11/keras-long-short-term-memory-lstm-model-predict-stock-prices.html

• The Big Data Game Board™">The Big Data Game Board™

  Move aside “Monopoly,” “Risk,” and “Snail Race!” Time to teach the youth of the world of an important, career-advancing game: how to leverage data and analytics to change your life! Introducing the “Big Data Game Board™”!

  https://www.kdnuggets.com/2018/11/big-data-game-board.html

• Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU

  A detailed comparison of the best places to train your deep learning model for the lowest cost and hassle, including AWS, Google, Paperspace, vast.ai, and more.

  https://www.kdnuggets.com/2018/11/deep-learning-cloud-providers-cpu-gpu-tpu.html

• To get hired as a data scientist, don’t follow the herd">To get hired as a data scientist, don’t follow the herd

  Key tips, including advice on how to step out of your comfort zone and sometimes overlooked important skills that will impress employers. Check also the audio version with additional advice.

  https://www.kdnuggets.com/2018/11/get-hired-as-data-scientist.html

• Latest Trends in Computer Vision Technology and Applications

  We investigate the advancements in deep learning, the rise of edge computing, object recognition with point cloud, VR and AR enhanced merged reality, semantic instance segmentation and more.

  https://www.kdnuggets.com/2018/11/trends-computer-vision-technology-applications.html

• Top 13 Python Deep Learning Libraries">Top 13 Python Deep Learning Libraries

  Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.

  https://www.kdnuggets.com/2018/11/top-python-deep-learning-libraries.html

• The Most in Demand Skills for Data Scientists">The Most in Demand Skills for Data Scientists

  Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. How should data scientists who want to be in demand by employers spend their learning budget?

  https://www.kdnuggets.com/2018/11/most-demand-skills-data-scientists.html

• Data Science “Paint by the Numbers” with the Hypothesis Development Canvas

  Now you are ready to take the next step from a Big Data MBA perspective by building off of the Business Model Canvas to flesh out the business use cases – or hypothesis – which is where we can become more effective at leveraging data and analytics to optimize our the business.

  https://www.kdnuggets.com/2018/11/data-science-paint-by-numbers-hypothesis-development-canvas.html

• Why AI will not replace radiologists

  We investigate some of the reasons why radiologists will be safe from AI, including the fact that humans will always maintain ultimate responsibility, how productivity gains will drive demand, and more.

  https://www.kdnuggets.com/2018/11/why-ai-will-not-replace-radiologists.html

• How Data Science Is Improving Higher Education

  Increasingly, colleges and universities, as well as governments, are using data science to improve the ways educational institutions do everything from recruiting to engaging with students to budgeting.

  https://www.kdnuggets.com/2018/11/data-science-improving-higher-education.html

• Labeling Unstructured Text for Meaning to Achieve Predictive Lift

  In this post, we examine several advance NLP techniques, including: labeling nouns and noun phrases for meaning, labeling (most often) adverbs and adjectives for sentiment, and labeling verbs for intent.

  https://www.kdnuggets.com/2018/10/labeling-unstructured-text-meaning.html

• Implementing Automated Machine Learning Systems with Open Source Tools

  What if you want to implement an automated machine learning pipeline of your very own, or automate particular aspects of a machine learning pipeline? Rest assured that there is no need to reinvent any wheels.

  https://www.kdnuggets.com/2018/10/implementing-automated-machine-learning-open-source-path.html

• The Intuitions Behind Bayesian Optimization with Gaussian Processes

  Bayesian Optimization adds a Bayesian methodology to the iterative optimizer paradigm by incorporating a prior model on the space of possible target functions. This article introduces the basic concepts and intuitions behind Bayesian Optimization with Gaussian Processes.

  https://www.kdnuggets.com/2018/10/intuitions-behind-bayesian-optimization-gaussian-processes.html

• Apache Spark Introduction for Beginners">Apache Spark Introduction for Beginners

  An extensive introduction to Apache Spark, including a look at the evolution of the product, use cases, architecture, ecosystem components, core concepts and more.

  https://www.kdnuggets.com/2018/10/apache-spark-introduction-beginners.html

• Graphs Are The Next Frontier In Data Science">Graphs Are The Next Frontier In Data Science

  GraphConnect 2018, Neo4j’s bi-annual conference, was held in New York City in mid-September. Read about what happened, and why graphs are the next big thing in data science.

  https://www.kdnuggets.com/2018/10/graphs-next-frontier-data-science.html

• Adversarial Examples, Explained

  Deep neural networks—the kind of machine learning models that have recently led to dramatic performance improvements in a wide range of applications—are vulnerable to tiny perturbations of their inputs. We investigate how to deal with these vulnerabilities.

  https://www.kdnuggets.com/2018/10/adversarial-examples-explained.html

• Machine Reading Comprehension: Learning to Ask & Answer

  Investigating the dual ask-answer network, covering the embedding, encoding, attention and output layer, as well as the loss function, with code examples to help you get started.

  https://www.kdnuggets.com/2018/10/machine-reading-comprehension-learning-ask-answer.html

• Using Confusion Matrices to Quantify the Cost of Being Wrong

  The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.

  https://www.kdnuggets.com/2018/10/confusion-matrices-quantify-cost-being-wrong.html

• Evaluating the Business Value of Predictive Models in Python and R

  In these blogs for R and python we explain four valuable evaluation plots to assess the business value of a predictive model. We show how you can easily create these plots and help you to explain your predictive model to non-techies.

  https://www.kdnuggets.com/2018/10/evaluating-business-value-predictive-models-modelplotpy.html

• Top 8 Python Machine Learning Libraries">Top 8 Python Machine Learning Libraries

  Part 1 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.

  https://www.kdnuggets.com/2018/10/top-python-machine-learning-libraries.html

• Top 3 Trends in Deep Learning

  We investigate the intermediate stage of deep learning, and the trends that are emerging in response to the challenges at this stage, including Interoperability and the multi-deployment options.

  https://www.kdnuggets.com/2018/10/mathworks-top-3-trends-deep-learning.html

• Visualising Geospatial data with Python using Folium

  Folium is a powerful data visualization library in Python that was built primarily to help people visualize geospatial data. With Folium, one can create a map of any location in the world if its latitude and longitude values are known. This guide will help you get started.

  https://www.kdnuggets.com/2018/09/visualising-geospatial-data-python-folium.html

• Data Capture – the Deep Learning Way

  An overview of how an information extraction pipeline built from scratch on top of deep learning inspired by computer vision can shakeup the established field of OCR and data capture.

  https://www.kdnuggets.com/2018/09/data-capture-deep-learning-way.html

• Ethics + Data Science: opinion by DJ Patil, former US Chief Data Scientist

  How much has data changed our lives over the past decade? Former US Chief Data Scientist DJ Patil investigates.

  https://www.kdnuggets.com/2018/09/ethics-data-science.html

• The Growing Participation of Women in the Data Science Community

  We still have a long way to go before the gender representation becomes more equalized, but the field at large indicates hopeful trends about women working in the role or desiring to do so in the future.

  https://www.kdnuggets.com/2018/09/growing-participation-women-data-science-community.html

• Iterative Initial Centroid Search via Sampling for k-Means Clustering

  Thinking about ways to find a better set of initial centroid positions is a valid approach to optimizing the k-means clustering process. This post outlines just such an approach.

  https://www.kdnuggets.com/2018/09/iterative-initial-centroid-search-sampling-k-means-clustering.html

• Deep Learning for NLP: An Overview of Recent Trends">Deep Learning for NLP: An Overview of Recent Trends

  A new paper discusses some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks and some of the current best practices for applying deep learning in NLP.

  https://www.kdnuggets.com/2018/09/deep-learning-nlp-overview-recent-trends.html

• Financial Data Analysis – Data Processing 1: Loan Eligibility Prediction

  In this first part I show how to clean and remove unnecessary features. Data processing is very time-consuming, but better data would produce a better model.

  https://www.kdnuggets.com/2018/09/financial-data-analysis-loan-eligibility-prediction.html

• An End-to-End Project on Time Series Analysis and Forecasting with Python

  Time series are widely used for non-stationary data, like economic, weather, stock price, and retail sales in this post. We will demonstrate different approaches for forecasting retail sales time series.

  https://www.kdnuggets.com/2018/09/end-to-end-project-time-series-analysis-forecasting-python.html

• AI Knowledge Map: How To Classify AI Technologies">AI Knowledge Map: How To Classify AI Technologies

  What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI.

  https://www.kdnuggets.com/2018/08/ai-knowledge-map-classify-ai-technologies.html

• How to Make Your Machine Learning Models Robust to Outliers

  In this blog, we’ll try to understand the different interpretations of this “distant” notion. We will also look into the outlier detection and treatment techniques while seeing their impact on different types of machine learning models.

  https://www.kdnuggets.com/2018/08/make-machine-learning-models-robust-outliers.html

• Multi-Class Text Classification with Scikit-Learn

  The vast majority of text classification articles and tutorials on the internet are binary text classification such as email spam filtering and sentiment analysis. Real world problem are much more complicated than that.

  https://www.kdnuggets.com/2018/08/multi-class-text-classification-scikit-learn.html

• Why Automated Feature Engineering Will Change the Way You Do Machine Learning

  Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage.

  https://www.kdnuggets.com/2018/08/automated-feature-engineering-will-change-machine-learning.html

• Introduction to Fraud Detection Systems

  Using the Python gradient boosting library LightGBM, this article introduces fraud detection systems, with code samples included to help you get started.

  https://www.kdnuggets.com/2018/08/introduction-fraud-detection-systems.html

• Reinforcement Learning: The Business Use Case, Part 2

  In this post, I will explore the implementation of reinforcement learning in trading. The Financial industry has been exploring the applications of Artificial Intelligence and Machine Learning for their use-cases, but the monetary risk has prompted reluctance.

  https://www.kdnuggets.com/2018/08/reinforcement-learning-business-use-case-part-2.html

• An Introduction to t-SNE with Python Example

  In this post we’ll give an introduction to the exploratory and visualization t-SNE algorithm. t-SNE is a powerful dimension reduction and visualization technique used on high dimensional data.

  https://www.kdnuggets.com/2018/08/introduction-t-sne-python.html

• Data Scientist guide for getting started with Docker">Data Scientist guide for getting started with Docker

  Docker is an increasingly popular way to create and deploy applications through virtualization, but can it be useful for data scientists? This guide should help you quickly get started.

  https://www.kdnuggets.com/2018/08/data-scientist-guide-getting-started-docker.html

• Affordable online news archives for academic research

  Many researchers need access to multi-year historical repositories of online news articles. We identified three companies that make such access affordable, and spoke with their CEOs.

  https://www.kdnuggets.com/2018/08/affordable-online-news-archives.html

• Reinforcement Learning: The Business Use Case, Part 1

  At base, RL is a complex algorithm for mapping observed entities and measures into some set of actions, while optimizing for a long-term or short-term reward.

  https://www.kdnuggets.com/2018/08/reinforcement-learning-business-use-case-part-1.html

• Programming Best Practices For Data Science">Programming Best Practices For Data Science

  In this post, I'll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset.

  https://www.kdnuggets.com/2018/08/programming-best-practices-data-science.html

• Autoregressive Models in TensorFlow

  This article investigates autoregressive models in TensorFlow, including autoregressive time series and predictions with the actual observations.

  https://www.kdnuggets.com/2018/08/autoregressive-models-tensorflow.html

• Only Numpy: Implementing GANs and Adam Optimizer using Numpy">Only Numpy: Implementing GANs and Adam Optimizer using Numpy

  This post is an implementation of GANs and the Adam optimizer using only Python and Numpy, with minimal focus on the underlying maths involved.

  https://www.kdnuggets.com/2018/08/only-numpy-implementing-gans-adam-optimizer.html

• Basic Statistics in Python: Descriptive Statistics">Basic Statistics in Python: Descriptive Statistics

  This article covers defining statistics, descriptive statistics, measures of central tendency, and measures of spread. This article assumes no prior knowledge of statistics, but does require at least a general knowledge of Python.

  https://www.kdnuggets.com/2018/08/basic-statistics-python-descriptive-statistics.html

• Big Data a $4.7 Billion opportunity in the healthcare and pharmaceutical industry

  This post contains some of the key findings from the SNS Telecom & IT's latest report, which indicates that Big Data investments in the healthcare and pharmaceutical industry are expected to reach nearly $4.7 Billion by the end of 2018.

  https://www.kdnuggets.com/2018/07/snstelecom-big-data-healthcare-pharm.html

• Data Science For Business: 3 Reasons You Need To Learn The Expected Value Framework

  This article highlights the importance of learning the expected value framework in data science, covering classification, maximization and testing.

  https://www.kdnuggets.com/2018/07/data-science-business-expected-value-framework.html

• 9 Reasons why your machine learning project will fail

  This article explains in detail some of the issues that you may face during your machine learning project.

  https://www.kdnuggets.com/2018/07/why-machine-learning-project-fail.html

• Building A Data Science Product in 10 Days

  At startups, you often have the chance to create products from scratch. In this article, the author will share how to quickly build valuable data science products, using his first project at Instacart as an example.

  https://www.kdnuggets.com/2018/07/building-data-science-product-10-days.html

• The Future of Map-Making is Open and Powered by Sensors and AI

  This article investigates the future of map-making and the role of Sensors, Artificial Intelligence and Machine Learning within that.

  https://www.kdnuggets.com/2018/07/future-map-making-open-sensors-ai.html

• What is Minimum Viable (Data) Product?

  This post gives a personal insight into what Minimum Viable Product means for Machine Learning and the importance of starting small and iterating.

  https://www.kdnuggets.com/2018/07/minimum-viable-data-product.html

• 5 of Our Favorite Free Visualization Tools">5 of Our Favorite Free Visualization Tools

  5 key free data visualization tools that can provide flexible and effective data presentation.

  https://www.kdnuggets.com/2018/07/5-favorite-open-source-visualization-tools.html

• Overview and benchmark of traditional and deep learning models in text classification

  In this post, traditional and deep learning models in text classification will be thoroughly investigated, including a discussion into both Recurrent and Convolutional neural networks.

  https://www.kdnuggets.com/2018/07/overview-benchmark-deep-learning-models-text-classification.html

• Automated Machine Learning vs Automated Data Science">Automated Machine Learning vs Automated Data Science

  Just by adding the term "automated" in front of these 2 separate, distinct concepts does not somehow make them equivalent. Machine learning and data science are not the same thing.

  https://www.kdnuggets.com/2018/07/automated-machine-learning-vs-automated-data-science.html

• Modern Graph Query Language – GSQL

  This post introduces the prospect of fulfilling the need for a modern graph query language with GSQL

  https://www.kdnuggets.com/2018/06/modern-graph-query-language-gsql.html

• What’s the Difference Between Data Integration and Data Engineering?

  Why is this distinction important? Because it’s critical to understanding how leading-organizations are investing in new data engineering skills that exploit advanced analytics to create new sources of business and operational value.

  https://www.kdnuggets.com/2018/06/difference-between-data-integration-data-engineering.html

• 5 Data Science Projects That Will Get You Hired in 2018">5 Data Science Projects That Will Get You Hired in 2018

  A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.

  https://www.kdnuggets.com/2018/06/5-data-science-projects-hired.html

• Statistics, Causality, and What Claims are Difficult to Swallow: Judea Pearl debates Kevin Gray

  While KDnuggets takes no side, we present the informative and respectful back and forth as we believe it has value for our readers. We hope that you agree.

  https://www.kdnuggets.com/2018/06/pearl-gray-statistics-causality-claims-difficult-swallow.html

• Advice For Applying To Data Science Jobs

  A comprehensive guide to applying for a job in data science, covering the application, interview and offer stage.

  https://www.kdnuggets.com/2018/06/advice-applying-data-science-jobs.html

• Command Line Tricks For Data Scientists

  Aspiring to master the command line should be on every developer’s list, especially data scientists. Learning the ins and outs of your terminal will undeniably make you more productive.

  https://www.kdnuggets.com/2018/06/command-line-tricks-data-scientists.html

• Audience Segmentation

  The process of audience segmentation is not about just statistics, it’s about finding your ideal clients and choosing the right way of interaction with them.

  https://www.kdnuggets.com/2018/06/audience-segmentation.html

• The Future of Artificial Intelligence: Is Your Job Under Threat?

  This article examines the rapid growth of artificial intelligence: how we got to this point, the future AI job market and what can be done.

  https://www.kdnuggets.com/2018/06/future-ai-job-under-threat.html

• Using Linear Regression for Predictive Modeling in R

  In this post, we’ll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure.

  https://www.kdnuggets.com/2018/06/linear-regression-predictive-modeling-r.html

• How to tackle common data cleaning issues in R

  R is a great choice for manipulating, cleaning, summarizing, producing probability statistics, and so on. In addition, it's not going away anytime soon, it is platform independent, so what you create will run almost anywhere, and it has awesome help resources.

  https://www.kdnuggets.com/2018/05/packt-tackle-common-data-cleaning-issues-r.html

• Data Science: 4 Reasons Why Most Are Failing to Deliver

  Data Science: Some see billions in returns, but most are failing to deliver. This article explores some of the reasons why this is the case.

  https://www.kdnuggets.com/2018/05/data-science-4-reasons-failing-deliver.html

• Scientific debt – what does it mean for Data Science?

  This article analyses scientific debt - what it is and what it means for data science.

  https://www.kdnuggets.com/2018/05/scientific-debt.html

• Beyond Data Lakes and Data Warehousing

  We give a comprehensive review of data lakes and data warehouses, and look at what the future holds for total data integration.

  https://www.kdnuggets.com/2018/05/data-lakes-data-warehousing-integration-revolution.html

• The Executive Guide to Data Science and Machine Learning

  This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.

  https://www.kdnuggets.com/2018/05/executive-guide-data-science-machine-learning.html

• Deep learning scaling is predictable, empirically

  This study starts with a simple question: “how can we improve the state of the art in deep learning?”

  https://www.kdnuggets.com/2018/05/deep-learning-scaling-predictable-empirically.html

• Top 7 Data Science Use Cases in Finance

  We have prepared a list of data science use cases that have the highest impact on the finance sector. They cover very diverse business aspects from data management to trading strategies, but the common thing for them is the huge prospects to enhance financial solutions.

  https://www.kdnuggets.com/2018/05/top-7-data-science-use-cases-finance.html

• 7 Useful Suggestions from Andrew Ng “Machine Learning Yearning”

  Machine Learning Yearning is a book by AI and Deep Learning guru Andrew Ng, focusing on how to make machine learning algorithms work and how to structure machine learning projects. Here we present 7 very useful suggestions from the book.

  https://www.kdnuggets.com/2018/05/7-useful-suggestions-machine-learning-yearning.html