Oct
2018 Nov
All (105) | Courses, Education (10) | Meetings (9) | News (13) | Opinions (32) | Top Stories, Tweets (9) | Tutorials, Overviews (24) | Webcasts & Webinars (8)
- Interpretability is crucial for trusting AI and machine learning
- Nov 30, 2018.
We explain what exactly interpretability is and why it is so important, focusing on its use for data scientists, end users and regulators.
- Introducing the First Machine Learning Course With a Job Guarantee
- Nov 30, 2018.
Springboard is focused on filling the gaps in the current job market and helping people around the world achieve their career goals through accessible, flexible, lifelong learning. You will find a job within six months of completing the course. If you don’t, they will refund your tuition.
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A Complete Guide to Choosing the Best Machine Learning Course
- Nov 30, 2018.
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. - Deep Learning for the Masses (… and The Semantic Layer)
- Nov 30, 2018.
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.
- Variational Autoencoders Explained in Detail
- Nov 30, 2018.
We explain how to implement VAE - including simple to understand tensorflow code using MNIST and a cool trick of how you can generate an image of a digit conditioned on the digit.
- Serve yourself. The Next-Generation of Data Analytics. Dec 6 Webinar
- Nov 29, 2018.
We walk thru the evolution of BI and how new technologies have paved the way for modern data platforms like Looker that can serve the data needs of an entire company.
- Combating Customer Churn with AI
- Nov 29, 2018.
Businesses today can use the power of AI to help determine which customers are more likely to churn, and what actions to take to keep them. In this DataRobot webinar on Dec 10 @ 1 PM EST, learn how to combat customer churn with AI.
- Free ebook: Exploring Data with Python
- Nov 29, 2018.
This free eBook starts building your foundation in data science processes with practical Python tips and techniques for working and aspiring data scientists.
- Linking Data Science Activities to Business Initiatives Using the Hypothesis Development Canvas
- Nov 29, 2018.
The Hypothesis Development Canvas is an effective and concise tool that integrates the different elements of the “Thinking Like A Data Scientist” process into a single document.
- Top KDnuggets tweets, Nov 21-27: Intro to #DataScience for Managers – a mindmap; An Introduction to #AI
- Nov 28, 2018.
Also: An Introduction to #AI; Intuitively Understanding Convolutions for #DeepLearning; 10 Free Must-See Courses for Machine Learning and Data Science.
- DATAx Cyber Monday Extended – 40% off all summit tickets with CYBER40
- Nov 28, 2018.
Take advantage of our EXTENDED cyber Monday offer of 40% off all two-day passes and free access to all 5 tracks of the DATAx New York Festival. Use the code CYBER40.
- 8 Reasons to Take Data Analytics Certification Courses
- Nov 28, 2018.
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.
- How to Build a Machine Learning Team When You Are Not Google or Facebook
- Nov 28, 2018.
If you don’t have a clear application for machine learning, you’re going to regret your investment. We provide tips on how to go about setting up your machine learning team - no matter the size of your business.
- Sales Forecasting Using Facebook’s Prophet
- Nov 28, 2018.
In this tutorial we’ll use Prophet, a package developed by Facebook to show how one can achieve this.
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Deep Learning Cheat Sheets
- Nov 28, 2018.
Check out this collection of high-quality deep learning cheat sheets, filled with valuable, concise information on a variety of neural network-related topics. - SQL, Python, and R in One Platform
- Nov 27, 2018.
Stop jumping between applications. Get a complete analytical toolkit.
- What Python editors or IDEs you used the most in 2018?
- Nov 27, 2018.
Vote in the new KDnuggets Poll - what are your favorite Python editors or IDEs?
- Making Machine Learning Accessible [Webinar Replay]
- Nov 27, 2018.
Learn the business "why" and technical "how" for implementing machine learning in your organization - watch now.
- How to Engineer Your Way Out of Slow Models
- Nov 27, 2018.
We describe how we handle performance issues with our deep learning models, including how to find subgraphs that take a lot of calculation time and how to extract these into a caching mechanism.
- Bringing Machine Learning Research to Product Commercialization
- Nov 27, 2018.
In this blog post I want to share some of the insights into the differences between academia and industry when applying deep learning to real-world problems as we experienced them at Merantix over the last two years.
- Data Pro Cyber Monday – Choose Your Savings
- Nov 26, 2018.
Cyber Monday Is Back & Bigger Than Ever! This Cyber Monday deal is so good, only the first 30 people to purchase TDWI's Cyber Monday sale can unlock all TDWI Online Learning courses for six months. Choose your savings now!
- 3 Challenges for Companies Tackling Data Science
- Nov 26, 2018.
From new technology to workflows, we outline three of the more common problems and how businesses can overcome them.
- My secret sauce to be in top 2% of a Kaggle competition
- Nov 26, 2018.
A collection of top tips on ways to explore features and build better machine learning models, including feature engineering, identifying noisy features, leakage detection, model monitoring, and more.
- Top Stories, Nov 19-25: What is the Best Python IDE for Data Science?; Intro to Data Science for Managers
- Nov 26, 2018.
Also: An Introduction to AI; The Big Data Game Board; 6 Goals Every Wannabe Data Scientist Should Make for 2019; 10 Free Must-See Courses for Machine Learning and Data Science; 9 Must-have skills you need to become a Data Scientist, updated
- Data Science Strategy Safari: Aligning Data Science Strategy to Org Strategy
- Nov 26, 2018.
The title of this post is derived by drawing inspiration from Mintzberg’s seminal work. In this post, I am attempting to take you on a safari through the data science strategy formulation process.
- Top 5 domains Big Data analytics helps to transform
- Nov 23, 2018.
Big data analytics gives a competitive advantage to companies across many industries, especially, financial services, e-commerce, aviation, transportation, logistics, and energy. It enables to reduce downtime, mitigate risks, cut costs, and improve performance.
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Intro to Data Science for Managers
- Nov 23, 2018.
This mindmap contains a condensed introduction to the key data science concepts and techniques that have revolutionized the business landscape and became essential for making beneficial data-driven decisions - 6 Goals Every Wannabe Data Scientist Should Make for 2019
- Nov 22, 2018.
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.
- Cartoon: Thanksgiving, Big Data, and Turkey Data Science.
- Nov 22, 2018.
A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists.
- Top KDnuggets tweets, Nov 14-20: 10 Free Must-See Courses for Machine Learning and Data Science; Great list of #MachineLearning Resources
- Nov 21, 2018.
Also: What is the Best #Python IDE for #DataScience?; The 5 Basic Statistics Concepts Data Scientists Need to Know; New Book: Automated #DataScience and Artificial General Intelligence #AI; Mastering The New Generation of Gradient Boosting #Catboost
- Join the World’s Biggest Deep Learning Summit – KDnuggets Early Cyber Monday
- Nov 21, 2018.
RE•WORK are offering an exclusive early discount to KDnuggets subscribers for any of their upcoming AI and Deep Learning Summits when you register before November 30th with the code CYBER25.
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An Introduction to AI
- Nov 21, 2018.
We provide an introduction to AI key terminologies and methodologies, covering both Machine Learning and Deep Learning, with an extensive list including Narrow AI, Super Intelligence, Classic Artificial Intelligence, and more. - Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices
- Nov 21, 2018.
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.
- Autonomy – Do we have the choice?
- Nov 21, 2018.
Choice of taking decision or not taking a decision requires a free will. Machines do not have free will. They do what they do, some machines do intelligent things but not with choice. Interesting question to think is - what is choice? or what is autonomy?
- Address Your Data Science Strategy at DSNY
- Nov 20, 2018.
Don't panic about your data science strategies, join us at Data Science New York (DSNY) on January 23-24, 2019! SPECIAL OFFER - Data Science leaders can join us for only $699 when you register by Friday, November 23 with promo code KD699!
- Mega-PAW Las Vegas Registration is Live & Super Early Bird Pricing is Now Available!
- Nov 20, 2018.
Mega-PAW will be in Las Vegas, Jun 16-20, 2019, and registration is now live. Super Early Bird pricing is available until Dec 21! Find out who the first confirmed keynote speakers will be.
- Introducing Octoparse New Version 7.1 – web scraping for dummies is official
- Nov 20, 2018.
Introducing Octoparse 7.1, which includes a brand-new feature, Task Templates with ready-to-use tasks for extracting different types of websites and also includes three major updates to the dashboard, URL input features, and anti-blocking settings.
- Word Morphing – an original idea
- Nov 20, 2018.
In this post, we describe how to utilise word2vec's embeddings and A* search algorithm to morph between words.
- Machine Learning in Action: Going Beyond Decision Support Data Science
- Nov 20, 2018.
In order to disrupt business, machine learning models must adopt a product-focused approach, which is a much more significant undertaking.
- Insights on the role data can play in your organization
- Nov 19, 2018.
If you are looking for more insights on the role data can play in your organization, check out all the disruptive technology sessions happening at the 23rd Annual Shared Services & Outsourcing Week program.
- How Important is that Machine Learning Model be Understandable? We analyze poll results
- Nov 19, 2018.
About 85% of respondents said it was always or frequently important that Machine Learning model be understandable. This was is especially important for academic researchers, and surprisingly more in US/Canada than in Europe or Asia.
- What I Learned About Machine Learning at ODSC West 2018
- Nov 19, 2018.
Reflecting back on the ODSC West 2018 conference, with a review of some of the best talks on topics including active learning, interactive coefficient plots, time-series forecasting, and more.
- Predictive Analytics in 2018: Salaries & Industry Shifts
- Nov 19, 2018.
Highlights from Burtch Works Study: Salaries of Predictive Analytics Professionals include: salaries remain steady, which industries pay the most, and which industries are attracting more analytics professionals.
- Top Stories, Nov 12-18: What is the Best Python IDE for Data Science?; To get hired as a data scientist, don’t follow the herd
- Nov 19, 2018.
Also: The 5 Basic Statistics Concepts Data Scientists Need to Know; Mastering The New Generation of Gradient Boosting; The Most in Demand Skills for Data Scientists; Mastering The New Generation of Gradient Boosting; Top 10 Python Data Science Libraries
- The Big Data Game Board™
- Nov 19, 2018.
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™”!
- Anticipating the next move in data science – my interview with Thomson Reuters
- Nov 17, 2018.
Like chess, Big Data is a combination of science, art and play; Gregory Piatetsky-Shapiro of KDnuggets helps data devotees discover winning moves - my Thomson Reuters interview.
- How to Choose an AI Vendor
- Nov 16, 2018.
This report explores why it is so challenging to choose an AI vendor and what you should consider as you seek a partner in AI. Download now.
- Top 10 Python Data Science Libraries
- Nov 16, 2018.
The third part of our series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
- Using Uncertainty to Interpret your Model
- Nov 16, 2018.
We outline why you should care about uncertainty and discuss the different types, including model, data and measurement uncertainty and what different purposes these all serve.
- Sorry I didn’t get that! How to understand what your users want
- Nov 16, 2018.
Creating a chatbot is difficult, it involves knowledge in many AI-Hard tasks, such as Natural Language Understanding, Machine Comprehension, Inference, or Automatic Language Generation (in fact, solving these tasks is close to solving AI) and large human effort is required.
- Introducing Drexel new online MS in Data Science
- Nov 15, 2018.
Drexel’s new online MS in Data Science is the degree that launched a thousand opportunities. Complete your courses on your own schedule, while still building meaningful relationships with your professors and classmates.
- (Webinar) Farmers and Chubb on Humanizing Claims with AI
- Nov 15, 2018.
This Webinar from Insurance Nexus discusses how AI is transforming claims from a necessary back-office function into a source of competitive advantage.
- Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU
- Nov 15, 2018.
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.
- Mastering The New Generation of Gradient Boosting
- Nov 15, 2018.
Catboost, the new kid on the block, has been around for a little more than a year now, and it is already threatening XGBoost, LightGBM and H2O.
- Top KDnuggets tweets, Nov 07-13: 10 Free Must-See Courses for Machine Learning and Data Science
- Nov 14, 2018.
Also: Best Practices for Using Notebooks for #DataScience; Automated #MachineLearning - results of Gene Feruzza AutoML research.
- Bright Lights, Bright Future. TDWI Is Back in Vegas
- Nov 14, 2018.
Whether you're building an architecture to support self-service or advanced analytics, delivering ROI from analytics, or using machine learning and AI, TDWI Las Vegas will give tools for success. Super Early Bird by Dec 14 and save with code KD20.
- Strategy: Customer Analytics: Are you Profiting from your Data?
- Nov 14, 2018.
Introducing Wharton's Customer Analytics program, that helps participants make the connection between the numbers and the narrative, making it easier for them to help others understand the data they are collecting.
- Metadata Enrichment is Essential to Realize the Value of Open Datasets
- Nov 14, 2018.
The last few years have seen great advancement in AI technologies for data science and analytics. With analytics engines capable of ingesting and analyzing almost any amount and type of data, the bottleneck has shifted from the technology to the data itself.
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What is the Best Python IDE for Data Science?
- Nov 14, 2018.
Before you start learning Python, choose the IDE that suits you the best. We examine many available tools, their pros and cons, and suggest how to choose the best Python IDE for you. - LinkedIn Top Voices 2018: Data Science & Analytics
- Nov 13, 2018.
Meet 10 must-know writers and creators discussing everything from the prominence of Python to the ethical implications of artificial intelligence. Hint - you already know no. 1.
- Help us understand your Data Science goals!
- Nov 13, 2018.
If you are currently enrolled in or completed either short, online courses in data science or intensive, paid online bootcamps in data science, please complete this short survey for chance to win one of twenty $50 Amazon gift cards, and help improve Data Science education.
- [Download] Real-Life ML Examples + Notebooks
- Nov 13, 2018.
In this eBook, we will walk you through four Machine Learning use cases on Databricks: Loan Risk Use Case; Advertising Analytics & Prediction Use Case; Market Basket Analysis Problem at Scale; Suspicious Behavior Identification in Video Use Case. Get your copy now!
- The Evolution of Build Engineering in Managing Open Source [Webinar Replay]
- Nov 13, 2018.
Explore how the role of build engineering is evolving to reconcile two key trends: massive wide-scale adoption of open source; the most devastating cyber-attacks in recent history tied to unpatched dependencies and other vulnerabilities.
- The ultimate guide to starting AI
- Nov 13, 2018.
A step-by-step overview of how to begin your project, including advice on how to craft a wise performance metric, setting up testing criteria to overcome human bias, and more.
- The 5 Basic Statistics Concepts Data Scientists Need to Know
- Nov 13, 2018.
Today, we’re going to look at 5 basic statistics concepts that data scientists need to know and how they can be applied most effectively!
- Top Stories, Nov 5-11: The Most in Demand Skills for Data Scientists; 10 Free Must-See Courses for Machine Learning and Data Science
- Nov 12, 2018.
Also: What does a data scientist REALLY look like? Introduction to PyTorch for Deep Learning
- Machine Learning Toronto Summit
Nov 20-21 – Special KDnuggets discount - Nov 12, 2018.The Toronto Machine Learning Summit takes place Nov 20-21. Register to celebrate Canada's top AI Research, and enjoy a 30% off -Special Discount with code KDNUGGETS. Register now! - Self-Service Analytics and Operationalization – Why You Need Both
- Nov 12, 2018.
Get the guidebook / whitepaper for a look at how today's top data-driven companies scale their advanced analytics & machine learning efforts.
- Healthcare Analytics Made Simple
- Nov 12, 2018.
Finally, a book on Python healthcare machine learning techniques is here! Healthcare Analytics Made Simple does just what the title says: it makes healthcare data science simple and approachable for everyone.
- To get hired as a data scientist, don’t follow the herd
- Nov 12, 2018.
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.
- The Long Tail of Medical Data
- Nov 12, 2018.
This article discusses some issues related to medical data, relating specifically to power law distributions and computer aided diagnosis. Read on to see machine learning's place in the puzzle.
- Dr. Data Show Video: What the Hell Does “Data Science” Really Mean?
- Nov 10, 2018.
The latest episode of the Dr. Data Show answers the question, "What the hell is data science?"
- Stanford online Data Science / Data Mining courses & certificates
- Nov 9, 2018.
With Stanford online graduate courses and certificates, you can earn a higher education credential while still maintaining your career. Apply now!
- What does a data scientist REALLY look like?
- Nov 9, 2018.
Using the responses from Stack Overflow's 2018 Annual Developer Survey, we attempt to build a portrait of data scientists today, including a look at gender, skills, job satisfaction, and more.
- Top October Stories: 9 Must-have skills you need to become a Data Scientist, updated; 10 Best Mobile Apps for Data Scientist / Data Analysts
- Nov 9, 2018.
Also: How To Learn Data Science If You're Broke; Graphs Are The Next Frontier In Data Science; BIG, small or Right Data: Which is the proper focus?
- Multi-Class Text Classification with Doc2Vec & Logistic Regression
- Nov 9, 2018.
Doc2vec is an NLP tool for representing documents as a vector and is a generalizing of the word2vec method. In order to understand doc2vec, it is advisable to understand word2vec approach.
- Hilary Mason and Gilad Lotan to Keynote at MADS 2019
- Nov 8, 2018.
The 2019 Marketing Analytics and Data Science conference, Apr 8-10 in San Francisco, will empower you with practical applications to achieve better bottom line results in a data-driven future. Save 20% with VIP Code MADS19KDN. Register now and save!
- Deep Learning Performance Cheat Sheet
- Nov 8, 2018.
We outline a variety of simple and complex tricks that can help you boost your deep learning models accuracy, from basic optimization, to open source labeling software.
- Best Practices for Using Notebooks for Data Science
- Nov 8, 2018.
Are you interested in implementing notebooks for data science? Check out these 5 things to consider as you begin the process.
- 10 Free Must-See Courses for Machine Learning and Data Science
- Nov 8, 2018.
Check out a collection of free machine learning and data science courses to kick off your winter learning season.
- Top KDnuggets tweets, Oct 31 – Nov 6: 10 More Free Must-Read Books for Machine Learning and Data Science
- Nov 7, 2018.
Also: #DataScientist Personas: What Skills Do They Have and How Much Do They Make?; Cartoon: Halloween Costume for Big Data; Stop Installing Tensorflow Using pip for Performance Sake!; How Machines Understand Our Language: An Introduction to NLP
- 7 Best Practices for Machine Learning on a Data Lake
- Nov 7, 2018.
Download this report to learn about the data requirements for advanced analytics on a data lake, and best practices such analytics with a focus on machine learning.
- Latest Trends in Computer Vision Technology and Applications
- Nov 7, 2018.
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.
- Introduction to PyTorch for Deep Learning
- Nov 7, 2018.
In this tutorial, you’ll get an introduction to deep learning using the PyTorch framework, and by its conclusion, you’ll be comfortable applying it to your deep learning models.
- Turn data into revenue. Wharton can show you how.
- Nov 6, 2018.
Customer Analytics from Wharton Executive Education gives you an actionable plan to analyze your customer data by delving into specific collection methodologies and patterns for predictive behavior. Find out more about the Feb 25-Mar 1 session in Philadelphia.
- Turbocharge Tech Transformation: Integrate AI Across Insurance
- Nov 6, 2018.
This Webinar from Insurance Nexus will give you insights into integrating analytics in real-time, turning your vision into reality, satisfying budgetary constraints with incremental technological improvement, and more.
- Text Preprocessing in Python: Steps, Tools, and Examples
- Nov 6, 2018.
We outline the basic steps of text preprocessing, which are needed for transferring text from human language to machine-readable format for further processing. We will also discuss text preprocessing tools.
- Building Surveillance System Using USB Camera and Wireless-Connected Raspberry Pi
- Nov 6, 2018.
Read this post to learn how to build a surveillance system using a USB camera plugged into Raspberry Pi (RPi) which is connected a PC using its wireless interface.
- Mastering the Learning Rate to Speed Up Deep Learning
- Nov 6, 2018.
Figuring out the optimal set of hyperparameters can be one of the most time consuming portions of creating a machine learning model, and that’s particularly true in deep learning.
- Top Stories, Oct 29 – Nov 4: The Most in Demand Skills for Data Scientists; How Machines Understand Our Language
- Nov 5, 2018.
Also: Introduction to Deep Learning with Keras; How Data Science Is Improving Higher Education; 9 Must-have skills you need to become a Data Scientist, updated; Data Representation for Natural Language Processing Tasks; Top 13 Python Deep Learning Libraries
- Get ahead of your peers with a Certificate in Business Analytics
- Nov 5, 2018.
The program equips you with the skills to interpret data, formulate insights, and communicate your findings effectively. Courses are focused on the strategic use of data, providing a foundation that can be applied to diverse fields.
- Quantum Machine Learning: A look at myths, realities, and future projections
- Nov 5, 2018.
An overview of quantum computing and quantum algorithm design, including current state of the hardware and algorithm design within the existing systems.
- Machine Learning Classification: A Dataset-based Pictorial
- Nov 5, 2018.
In order to relate machine learning classification to the practical, let's see how this concept plays out, step by step (and with images), specifically in direct relation to a dataset.
- Data Mining Book – Chapter Download
- Nov 2, 2018.
Download this immediately useful book chapter, and learn how to create derived variables, which allow the statistical and Data Science modeling to incorporate human insights.
- Learn how machine learning is transforming business, Nov 12 Webinar
- Nov 2, 2018.
In this webinar on Nov 12, titled How to Transform Your Business with Automated Machine Learning, learn the difference between AI, machine learning, and deep learning, the challenges of implementing traditional data science solutions, and how automated machine learning enables more employees to take part in AI initiatives.
- Top 13 Python Deep Learning Libraries
- Nov 2, 2018.
Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
- The Most in Demand Skills for Data Scientists
- Nov 2, 2018.
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?
- Data Representation for Natural Language Processing Tasks
- Nov 2, 2018.
In NLP we must find a way to represent our data (a series of texts) to our systems (e.g. a text classifier). As Yoav Goldberg asks, "How can we encode such categorical data in a way which is amenable for us by a statistical classifier?" Enter the word vector.
- Data Science “Paint by the Numbers” with the Hypothesis Development Canvas
- Nov 2, 2018.
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.
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: November and Beyond
- Nov 1, 2018.
Coming soon: PASS Summit Seattle, TDWI Orlando, IEEE Conf on Data Mining Singapore, AI & Big Data Innovation Summit 2018 Beijing, Deep Learning World Berlin, PAW Business Berlin, Big Data LDN London, NIPS, and many more.
- Join AI experts from Google Brain, Open AI & Uber AI Labs in San Francisco
- Nov 1, 2018.
Join us at the Deep Learning Summit, San Francisco, 24 - 25 Jan 2019. Learn from industry experts in speech & pattern recognition, neural networks, image analysis and NLP, and explore how deep learning will impact all industries.
- Tomorrow, Nov 8 Webinar: Transform Your Stagnant Data Swamp into a Pristine Data Lake
- Nov 1, 2018.
We explore how to implement an Enterprise Data Management strategy that will unleash your data to power decisions, examine a real-world digital-transformation use case from a Tier-1 bank, and see a demo of Trifacta Wrangler.
- Why AI will not replace radiologists
- Nov 1, 2018.
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.
- How Data Science Is Improving Higher Education
- Nov 1, 2018.
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.
- Multi-Class Text Classification Model Comparison and Selection
- Nov 1, 2018.
This is what we are going to do today: use everything that we have presented about text classification in the previous articles (and more) and comparing between the text classification models we trained in order to choose the most accurate one for our problem.
