All (102) | Courses, Education (5) | Meetings (18) | News (14) | Opinions (30) | Top Stories, Tweets (9) | Tutorials, Overviews (22) | Webcasts & Webinars (4)
- [Webinar] Managing the Complete Machine Learning Lifecycle - Feb 28, 2019.
Join Databricks Mar 7, 2019, to learn how using MLflow can help you keep track of experiment runs and results across frameworks, execute projects remotely on to a Databricks cluster, and quickly reproduce your runs, and more. Sign up for this webinar now.
- Preparing for the Unexpected - Feb 28, 2019.
In some domains, new values appear all the time, so it's crucial to handle them in a good way. Using deep learning, one can learn a special Out-of-Vocabulary embedding for these new values. But how can you train this embedding to generalize well to any unseen value? We explain one of the methods employed at Taboola.
- 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.
- TensorFlow.js: Machine learning for the web and beyond - Feb 28, 2019.
- Top KDnuggets tweets, Feb 20-26: Learning programming languages for free; Import Your Favorite Libraries into a Jupyter Notebook - Feb 27, 2019.
Also: Python Data Science for Beginners; A comprehensive survey on graph neural networks; Convolutional Neural Networks — Simplified #NeuralNetworks; What are Some “Advanced” AI and Machine Learning Online Courses?; Artificial Neural Network Implementation using NumPy
- Python 2 support ends this year. Are you ready to migrate? - Feb 27, 2019.
Python 2 ends on Jan 1, 2020. Migrating from Python 2 to 3 can be a scary process, so get this solution sheet with different options for moving your existing packages and applications from Python 2 to 3, along with best practice guidelines.
- Join the future of AI and Data at DATAx San Francisco this May with Microsoft, Google and so many more - Feb 27, 2019.
Join us as we bring you the leading innovations and insights to the fast-paced world of AI & Data from Machine Learning, Healthcare, Marketing, Gaming analytics.
- Acquiring Labeled Data to Train Your Models at Low Costs - Feb 27, 2019.
We discuss groundbreaking and unique methods to acquire labeled data at low cost, including 3rd-Party Plug-and-Play AI Model, Zero-Shot Learning, and Restructuring the Existing Data Set.
- 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!
- 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.
- Top Stories, Feb 18-24: How to Setup a Python Environment for Machine Learning; Artificial Neural Network Implementation using NumPy - Feb 26, 2019.
Also: Running R and Python in Jupyter; What are Some “Advanced” AI and Machine Learning Online Courses?; Python Data Science for Beginners; 6 Books About Open Data Every Data Scientist Should Read
- Upskill your Data Science Career. Attend ODSC East in person or Live Online. 45% off Ends Friday - Feb 26, 2019.
ODSC East is the top conference for data science practitioners and AI engineers: 300+ talks, full and half-day expert-led trainings, and shorter hands-on workshops. KDnuggets subscribers save 45% with code KDN45. Register now!
- Reflections on the State of AI: 2018 - Feb 26, 2019.
We provide a detailed overview of the key developments in the AI space, focusing on key players, applications, opportunities, and challenges.
- 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.
- 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.
- Io-Tahoe releases enhanced Smart Data Discovery solution, with PII and Sensitive Data Discovery capability, enabling compliance with the California Consumer Privacy Act (CCPA) - Feb 25, 2019.
Io-Tahoe technology can track changes to the sensitive data landscape over time to understand how the PII and the sensitive data footprint is changing, enabling firms to continually keep track of their data on an ongoing basis.
- 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.
- Where did you apply Analytics, Data Science, Machine Learning in 2018? - Feb 25, 2019.
Where did you apply Analytics, Machine Learning, and Data Science in 2018? Take part in the latest KDnuggets poll to share your input, and see what others have to say.
- Stanford online Data Science, Data Mining courses and certificates - Feb 22, 2019.
With Stanford's online graduate courses and certificates, you can earn a higher education credential while still maintaining your career. Start now!
- Don’t do analysis in a vacuum - Feb 22, 2019.
Traditional tools force analysts to play the import-and-export game, so it's difficult to keep data fresh and accessible. Every Mode report or dashboard lives at a unique URL for future sharing, iterating, and building upon. Mode brings your entire team together in one platform.
- Two Major Difficulties in AI and One Applied Solution - Feb 22, 2019.
Some of AI’s biggest problems can be solved by focusing on modelling our own human abilities instead of admiring NN and ML “intelligence”. We present an example that takes us in that direction in the form of chess.
- 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?
- The AI Conference in New York–Exclusive offer for KDnuggets readers - Feb 21, 2019.
Find out how many organizations are now planning AI implementations, although only a very small percentage are successfully deploying AI in production, at the AI Conference in New York Apr 15–18.
- DATAx Singapore, 5-6 March 2019, Suntec Singapore Convention and Exhibition Centre - Feb 21, 2019.
Unlock the power of data for your business in this 4-event-in-1, where over 500 data leaders will gather to analyze, develop and conquer real-world challenges with data.
- Deep Learning for Natural Language Processing (NLP) – using RNNs & CNNs - Feb 21, 2019.
We investigate several Natural Language Processing tasks and explain how Deep Learning can help, looking at Language Modeling, Sentiment Analysis, Language Translation, and more.
- 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
- Top KDnuggets tweets, Feb 13-19: Intro to Scikit Learn: The Gold Standard of Python ML; The Essential Data Science Venn Diagram - Feb 20, 2019.
Also: Cartoon: #MachineLearning Problems in 2118 #ValentinesDay; A must-read tutorial when you are starting your journey with #DeepLearning.
- Data Science Survey - Feb 20, 2019.
Gurobi would like to tap into your expertise in the field of Data Science and Analytics, and invites you to participate in their Data Science Survey. Everyone who completes this 10-minute survey can choose to be entered into a drawing to receive one of five $100 Amazon gift cards.
- U. of Cincinnati Analytics Summit 2019, April 1-3 - Feb 20, 2019.
Analytics Summit 2019 focuses on analytics and data science content to support the growth and development of analytics efforts in business, government and non-profit organizations.
- 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.
- 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.
- 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.
- 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.
- DATAx Singapore Highlights, March 5-6 - Feb 19, 2019.
Join conversations with Oracle, WPP, Axiata, Dyson, IBM, Netflix, Visa, AIA, Google, Bloomberg & more as they share how they utilize technology and data science.
- How to Cope with the Rise of the Citizen Data Scientist - Feb 19, 2019.
Gartner predicts that citizen data scientists will surpass data scientists in the amount of advanced analytics produced. Does that mean that Enterprise AI and augmented analytics render the job of a data scientist obsolete? Download this white paper to found out more.
- 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.
- 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.
- 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
- 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?
- Top Stories, Feb 11-17: Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant; Scikit Learn: The Gold Standard of Python Machine Learning - Feb 18, 2019.
Also: Learn How to Listen: One of the hardest parts of being a data scientist; Top 10 Data Science Use Cases in Telecom; The Best and Worst Data Visualizations of 2018; The Analytics Engineer – new role in the data team; A Quick Guide to Feature Engineering
- Can’t-Miss Keynote Speakers at PAW Financial, plus 3 other PAWs in Vegas – Save ’til March 8 - Feb 18, 2019.
Predictive Analytics World for Financial is heading to Las Vegas, NV on Jun 16-20, and we're excited to announce the speaker line-up. The year’s only PAW Financial will be held alongside PAW Business, PAW Healthcare, PAW Industry 4.0, and Deep Learning World. Register now!
- Artificial Intelligence and Data Science Advances in 2018 and Trends for 2019 - Feb 18, 2019.
We recap some of the major highlights in data science and AI throughout 2018, before looking at the some of the potential newest trends and technological advances for the year ahead.
- 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.
- The Persuasion Paradox – How Computers Optimize their Influence on You - Feb 16, 2019.
How do computers optimize mass persuasion – for marketing, presidential campaigns, and even healthcare? And why is there actually no data that directly records influence, considering it's so important? In this season finale episode, Eric Siegel introduces machine learning methods designed to persuade.
- ModelOps – Get it done. 3 Day Webinar Mini-Series - Feb 15, 2019.
Join us for an educational series ModelOps - Get it done. Learn how a combination of technology and processes can help solve modelOps.
- How to solve 4 big problems in data science – eBook - Feb 15, 2019.
This eBook includes insights and learnings on how data scientists from four leading companies delivered impressive business results like accelerating global inventory from 48 hours to 45 minutes and reducing operational cost of analytics infrastructure by 30%. Get the eBook now!
- 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.
- 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.
- Learn How to Listen: One of the hardest parts of being a data scientist - Feb 15, 2019.
Listen, Be Humble, Be Present and Transform ideas. A Data Scientist will spend a large amount of their time in meetings where you can understand the business, the goals of the area, their KPIs, and their requirements.
- A Word from KDD Cup 2019 Organizers - Feb 15, 2019.
This year’s KDD Cup will be celebrating 22 years. It’s been an exciting journey and we have come a long way! We invite industrial and academic institutions to submit proposals for organizing the 2019 KDD Cup Competition. Learn more now!
- Accelerating Time Series Analysis with Automated Machine Learning - Feb 14, 2019.
This IDC Solution Spotlight examines how automated machine learning tools can augment the analysis, modeling, and prediction of time series data to deliver easily understood and actionable insights for businesses in a simple and agile fashion. Get the report now.
- Decision Trees — An Intuitive Introduction - Feb 14, 2019.
An extensive introduction including a look at decision tree classification, data distribution, decision tree regression, decision tree learning, information gain, and more.
- 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.
- Top KDnuggets tweets, Feb 06-12: The Essential Data Science Venn Diagram; Python Pandas: Tricks & Features You May Not Know - Feb 13, 2019.
Also: GitHub: Numpy and Scipy are the most popular packages for machine learning projects; The Best and Worst Data Visualizations of 2018; 200 cognitive biases rule our everyday thinking; Neural Networks – an Intuition
- Top January Stories: Your AI skills are worth less than you think - Feb 13, 2019.
Also: The cold start problem: how to build your machine learning portfolio; 7 Steps to Mastering Basic Machine Learning with Python - 2019 Edition.
- The Top 15 Data Priorities for 2019 - Feb 13, 2019.
This is the perfect opportunity for you to join 90+ data leaders to discuss your challenges, share success stories and offer your solutions Chief Data Officer Exchange in London next month. KDNuggets readers can benefit from a £500 saving to join as a delegate when you request an invitation.
- Find Your Algorithm for Success with Drexel’s Online MS in Data Science - Feb 13, 2019.
Drexel’s new online MS in Data Science is the degree that launched a thousand opportunities. You’ll graduate workplace-ready by having experience with some of the industry’s leading technology. Success is waiting. Apply today!
- Deep Learning World Agenda Now Released! - Feb 13, 2019.
The agenda for Deep Learning World Europe has just been released. Industry leaders will gather in Munich to foster progress in the value-driven operationalization of established deep learning methods. Register now and take advantage of Early Bird rates!
- 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.
- 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.
- Data Isn’t Enough: Impact the Future of Business with Tepper’s Online MS in Business Analytics - Feb 12, 2019.
If you want to translate the power of data analytics into business value, you need the skills you'll learn from the online Master of Science in Business Analytics program from the Tepper School of Business at Carnegie Mellon University.
- Top Stories, Feb 4-10: Data Scientists: Why are they so expensive to hire?; The Essential Data Science Venn Diagram - Feb 12, 2019.
Also: Intuitive Visualization of Outlier Detection Methods; Understanding Gradient Boosting Machines; The Best and Worst Data Visualizations of 2018; Your AI skills are worth less than you think
- Free eBook: Practical Data Science Cookbook – Second Edition - Feb 12, 2019.
Starting with the basics, this free eBook covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format.
- Reinforce AI Conference, March 20-22, Budapest - Feb 12, 2019.
Reinforce is the perfect place to meet other professionals, network with the leaders of the AI industry and have a beer in Budapest, in the heart of Europe. Use code KDNuggets for 20% off.
- Free Access: Intelligent Automation Report - Feb 12, 2019.
Learn the top challenges in the world of Intelligent Automation, the main trends in RPA, AI, Machine Learning, and key areas of spend for technologies in 2019. Get the report!
- 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.
- 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.
- DATAx Singapore – meet Data Science Leaders – special year of the Pig offer - Feb 11, 2019.
At DATAx Singapore join data science and business leaders across industries presenting how machine learning algorithms and analytics improve business results. Book by 15 Feb and get 20% off using code KDNY20.
- Agenda Live for Predictive Analytics World for Industry 4.0 – Munich 6-7 May - Feb 11, 2019.
The agenda for Predictive Analytics World Industry 4.0 has just been released. Join experts in predictive analytics on 6-7 May in Munich to discover and discuss the latest trends and technologies in machine & deep learning. Register now and take advantage of Early Bird rates!
- Secure Your Data at Marketing Analytics and Data Science - Feb 11, 2019.
Marketing Analytics and Data Science is coming to San Francisco, Apr 8-10, and get the tools you need to secure your data and continue business success with GDPR. Save 20% with VIP Code: MADS19KDN.
- 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.
- How to Adopt Machine Learning: Interviews with Technical & Business Leaders - Feb 11, 2019.
This 8 chapter series includes interviews with technical and business leaders from a number of large companies with the aim to help you adopt machine learning in your organization.
- 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.
- 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.
- QCon.ai San Francisco: Applied AI Software Conference for Developers – KDnuggets Offer - Feb 8, 2019.
QCon.ai is a three-day conference focused on the major machine learning and AI software trends affecting software engineers today. Register by Feb 23 with code "KDN" and save.
- Advance Your Data and Analytics Initiatives With Training - Feb 8, 2019.
Join us at TDWI Chicago, Apr 28 - May 3 - the agenda is live! Choose from 50+ full- and half-day sessions and achieve your #DataGoals in 2019. Save up to $915 with code KD20!
- 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.
- 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?
- Strata Data SF – join top data scientists, analysts, engineers, and execs – KDnuggets Offer - Feb 7, 2019.
Thousands of top data scientists, analysts, engineers, and executives converge at Strata Data Conference every year. Early Price for Strata in San Francisco expires on Friday, February 15. Save up to $689 off a standard price Gold pass with code KDNU.
- 10 Trending Data Science Topics at ODSC East 2019 - Feb 7, 2019.
ODSC East 2019, Boston, Apr 30 - May 3, will host over 300+ of the leading experts in data science and AI. Here are a few standout topics and presentations in this rapidly evolving field. Register for ODSC East at 50% off till Feb 8.
- 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.
- 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.
- 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.
- Top KDnuggets tweets, Jan 30 – Feb 05: state-of-the-art in #AI, #MachineLearning - Feb 6, 2019.
Also Brilliant tour-de-force! Reinforcement Learning to solve Rubiks Cube; Dask, Pandas, and GPUs: first steps; Neural network AI is simple. So Stop pretending you are a genius.
- Statistical Thinking for Industrial Problem Solving – a free online course - Feb 6, 2019.
This online course is available – for free – to anyone interested in building practical skills in using data to solve problems better.
- Data analytics degree with certs included - Feb 6, 2019.
At WGU, you could earn your data analytics degree and multiple industry-recognized certifications at the same time, for one price. Take the next step in your career! Earn the credentials you need and turn your future success up a degree.
- 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.
- 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.
- Top Stories, Jan 28 – Feb 3: 7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition; Your AI skills are worth less than you think - Feb 5, 2019.
Also: Data Scientists: Why are they so expensive to hire?; Trending Deep Learning Github Repositories; The Algorithms Aren’t Biased, We Are; Five Ways Your Safety Depends on Machine Learning; Cracking the Data Scientist Interview
- 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.
- 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.
- 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.
- 2019 INFORMS Business Analytics Conference: Industry 4.0, Apr 14-16, Austin - Feb 4, 2019.
The 2019 INFORMS Conference on Business Analytics and Operations Research offers a rich experience for Analytics professionals and other business leaders. Get early rates until March 4.
- Video: Five reasons to go to Mega-PAW – Las Vegas, June 2019 - Feb 4, 2019.
Mega-Paw, Jun 16-20, delivers brand-name, cross-industry, vendor-neutral case studies purely on machine learning's commercial deployment, and the hottest topics and techniques. Check out 5 reasons to join us in Las Vegas!
- Embedded Analytics – What are your choices? - Feb 4, 2019.
This webinar from Looker is designed to introduce you to the benefits of embedded analytics, criteria for evaluation tools and vendors and the Lookers platform vs the alternatives.
- 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.
- 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.
- 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.
- From Analytics to AI: Is Your Team Ready? - Feb 1, 2019.
If you have data-savvy analytics talent, then you have a solid foundation to begin your AI journey. The next step: automated machine learning. Will this be the year your team starts implementing AI? Join DataRobot @ 1 PM ET, Feb 7, for more info.
- Aspiring Researchers, Engineers, and Entrepreneurs interested in data: This Book is for You - Feb 1, 2019.
Making Databases Work is a collection of chapters written by leading database researcher and enterpreneur Michael Stonebraker and 38 of his collaborators: world-leading database researchers, world-class systems engineers, and business partners.
- 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.
- 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.