RE•WORK's world-famous Deep Learning Summit is taking place in Toronto this October with the addition of a new AI for Government track. Don't forget to use code KDNUGGETS at the checkout for 20% off your pass. See you there!
We outline the four key areas of Maths in Machine Learning and begin to answer the question: how can we start with high school maths and use that knowledge to bridge the gap with maths for AI and Machine Learning?
Download this report from Databricks to understand how enterprises are adopting AI technology, the primary challenges holding enterprises back from seeing success with AI, and the benefits of a taking a unified approach to data and AI.
Enterprise organizations face conflicting priorities. Where do they turn to cut through the hype and determine how to prioritize AI strategies and technologies for their business? Find out Dec 3-5 in Boston.
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.
Also: 25 fun questions for a #MachineLearning interview; #Free Public Datasets #KDN; 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study; Machine Learning Cheat Sheets
In this webinar, learn best practices and practical implementation tips for each step of an automated machine learning project, and see live, hands-on demos of Trifacta, DataRobot, and Tableau.
This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more.
Key takeaways and highlights from ODSC India 2018 conference about the latest trends, breakthroughs and revolutions in the field of Data Science and Artificial Intelligence
Without the right precautions, machine learning — the technology that drives risk-assessment in law enforcement, as well as hiring and loan decisions — explicitly penalizes underprivileged groups.
As a data scientist, managing environments and experiments is always hard and results in wasted time and effort with all the troubleshooting and lost work. With datmo, you can track your experiments using this common standard and not worry about reproduction of previous work.
It is amazing that Deep Neural Networks display this Universality in their weight matrices, and this suggests some deeper reason for Why Deep Learning Works.
Instead of building your own dataset, there already exists a rich collection of computer vision datasets contributed by academic researchers, hobbyists and companies.
We breakdown the various aspects of web scraping, from why businesses need to do it, to instructions on how to go about acquiring this data with PromptCloud - a pioneer in Data as Service solutions with specialization in large-scale and custom web data extraction.
Get free ebook, DATAx Guide to Data Visualization in 2019, the definitive foundation to help you prepare for the future of data visualization, AI and machine learning.
Whether you are a beginner in Machine Learning or you have been trying hard to understand the Super Natural Machine Learning Algorithms and you still feel that the dots do not connect somehow, this post is definitely for you!
Automated machine learning is a rapidly developing segment of artificial intelligence - it’s time to define what an AutoML product is so end-users can compare product capabilities intelligently.
A step-by-step guide that includes suggestions on how to preprocess data and deriving features from this. This article also contains links to help you explore additional resources about machine learning methods and other examples.
We take a hard look at diversity within the tech industry, root causes, and potential solutions and highlight resources/initiatives that can connect readers with programs aiding their professional development.
Also: 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study; A Winning Game Plan For Building Your Data Science Team; Machine Learning Cheat Sheets; Essential Math for Data Science: Why and How
The biggest (and anecdotally best) data engineering and analytics conference in the CEE region, is back! Practical Data Engineering and Data Analytics talks will take over Budapest, 29-31 October. Best part: discounted 3-in-1 tickets for Crunch, Amuse and Impact.
Drexel's new online Master's of Data Science places an emphasis on skills like data mining and algorithm creation, so you’ll graduate workplace-ready by having experience with some of the industry’s leading technology.
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.
Learn what exactly deep learning is, how it works, and about its growing and innovative applications in healthcare, finance, retail, and more with this illustrated guide.
The process of how we listen, think, talk and do using this data is not possible without the effective management thereof. This skill enables the business to exploit this asset and ride these Majestic Unicorns.
ODSC West 2018, and Accelerate AI return to San Francisco on Oct 31-Nov 3rd. The prior ODSC events sold out, so why wait? Save 30% with code KDD30 - offer ends Friday.
Successfully pushing ML to production requires a shift in your DevOps practices to become MLOps, machine learning operationalization. Learn how to do it in this Sep 27 webinar.
Detailed analysis into utilizing deep learning on the edge, covering both advantages and disadvantages and comparing this against more traditional cloud computing methods.
Data Augmentation is one way to battle this shortage of data, by artificially augmenting our dataset. In fact, the technique has proven to be so successful that it's become a staple of deep learning systems.
In this tutorial, you will learn how to create a table, insert values into it, use and understand some data types, use SELECT statements, UPDATE records, use some aggregate functions, and more.
NYU Stern MS in Business Analytics program can propel you forward to achieve your long-term career goals - here is a compelling story of one alumna, now a Lead Data Scientist at a top firm. Apply by Nov 1.
We examine the famous McKinsey prediction from 2011 and look into whether there a shortage of people with analytical expertise and estimate how many Data Scientists are there.
In this Looker webinar, Sep 20, 2 PM EST, you'll learn what is meant by operationalizing marketing data, how to track demand generation key performance metrics, and much more.
Data science education is readily available and live online courses are a great way to begin. Metis 20% off all part-time, Live Online courses offer ends Sep 23 - Get started today.
An extensive list of free resources to help you learn Natural Language Processing, including explanations on Text Classification, Sequence Labeling, Machine Translation and more.
In this eBook from Figure Eight and AWS you'll learn what active learning is and how it works, the areas in which active learning can be particularly effective, and how active learning iteratively improves your model.
Join Looker for this webcast, Sep 19, 2 PM EST, where you will learn how you can leverage Looker with the power of BigQuery Machine Learning (BQML) to build machine learning (ML) models directly where your data lives.
Also: Hadoop for Beginners; Object Detection and Image Classification with YOLO; Journey to Machine Learning 100 Days of ML Code; Data Visualization Cheat Sheet; Neural Networks and Deep Learning: A Textbook
A collection of Big Data trends to familiarize yourself with, covering IoT Networks, Artificial Intelligence, Predictive Analytics, Dark Data and more.
Cognitive biases are tendencies to think in certain ways that can lead to systematic deviations from a standard of rationality or good judgment. They have all sorts of practical impacts on our lives, whether we want to admit it or not.
A range of interesting posts from the /r/MachineLearning Reddit group for the month of August, including: Everybody Dance Now; Stanford class Machine Learning cheat sheets; Academic Torrents; Getting Alexa to respond to sign language using TensorFlow; PyCharm IDE.
A new whitepaper from Anaconda examines the economic impact of AI, how some companies and industries are leveraging AI to generate business value today, and how Anaconda Enterprise serves as an AI enablement platform for organizations seeking to harness the power of AI at scale.
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.
Hear how three companies benefitted from the performance, simplicity and convenience of NVIDIA DGX Station to supercharge their deep learning development, infusing their products and services with the power of AI.
At Bay Path University, we'll provide you with a framework for working together regardless of your background and experience. That is why we created two tracks to complete the MS in Applied Data Science degree, which is right for you?
In this article, focus on current AI, which is mostly based on the algorithms that can do predictions, and discuss how the economics of AI works and how it may affect business.
Learn why cluster computing makes Spark the ideal processing engine for complex aggregations, the different types of aggregations that you can do with Spark, and more.
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.
In this Looker webinar, Sep 20, 2 PM EST, you'll learn what is meant by operationalizing marketing data, how to track demand generation key performance metrics, and much more.
Highlights and key takeaways from KDD 2018, 24th ACM SIGKDD conference on Data Science and Data Mining: including what is a deconfounder, how Pinterest approaches Machine Learning, Knowledge Graph for Products, and Differential Privacy.
Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.
Hey Boss, I was hoping to attend TDWI Orlando... Where in-depth vendor-neutral analytics + data management training = immediate impact, and I can save a lot with code KD20.
Introducing Octoparse - a sleek, powerful and easy-to-use software that makes web scraping from any websites achievable for most people, including non-coders.
Join Looker for this webcast, Sep 19, 2 PM EST, where you will learn how you can leverage Looker with the power of BigQuery Machine Learning (BQML) to build machine learning (ML) models directly where your data lives.
In this post, you will learn how to train Keras-MXNet jobs on Amazon SageMaker. I’ll show you how to build custom Docker containers for CPU and GPU training, configure multi-GPU training, pass parameters to a Keras script, and save the trained models in Keras and MXNet formats.
Also: Neural Networks and Deep Learning: A Textbook; Essential Math for Data Science: Why and How; Deep Learning for NLP: An Overview of Recent Trends; 5 Resources to Inspire Your Next Data Science Project; Data Visualization Cheat Sheet
Watch Springboard webinar and learn everything from the hard skills to the soft skills aspiring data scientists need. Springboard Data Science Career Track now offers deferred tuition - learn more.
Learn why data quality and data integration are key to delivering meaningful, actionable results, and how to develop data and analytics strategies that offer visibility into healthcare cost and quality.
This book covers both classical and modern models in deep learning. The book is intended to be a textbook for universities, and it covers the theoretical and algorithmic aspects of deep learning.
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.
A personal account from Machine Learning enthusiast Avik Jain on his experiences of #100DaysOfMLCode, a challenge that encourages beginners to code and study machine learning for at least an hour, every day for 100 days.
This article presents 5 things to know about A/B testing, from appropriate sample sizes, to statistical confidence, to A/B testing usefulness, and more.
Including video and written tutorials, beginner code examples, useful tricks, helpful communities, books, jobs and more - this is the ultimate guide to getting started with TensorFlow.
Check out this new data science cheat sheet, a relatively broad undertaking at a novice depth of understanding, which concisely packs a wide array of diverse data science goodness into a 9 page treatment.
Last chance to save 30% off door prices ends Friday at midnight! ODSC Europe comes to London, Sep 19-22, co-located with Accelerate AI. Check out the recent schedule updates for ODSC West, Oct 31-Nov 3 in San Francisco.
Read this report to understand the top nine Predictive Analytics and Machine Learning solution providers in the market, and Forrester's 24-criteria evaluation of their strengths and weaknesses.
If you are wondering how to implement dropout, here is your answer - including an explanation on when to use dropout, an implementation example with Keras, batch normalization, and more.
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.
An overview and discussion around data science, covering the history behind the term, data mining, statistical inference, machine learning, data engineering and more.
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.
Coming soon: AI San Francisco, Big Data Innovation Boston, Strata NYC, INFORMS Seattle, SAS Analytics Experience San Diego, PAW Gov DC, Cypher Bangalore, ECML/PKDD Dublin, ODSC London, and many more.
Based on the recent book - Principles of Database Management - The Practical Guide to Storing, Managing and Analyzing Big and Small Data - this post examines how OLAP queries can be implemented in SQL.
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.
Also: AI Knowledge Map: How To Classify AI Technologies; How to Make Your Machine Learning Models Robust to Outliers; Linear Regression In Real Life; 5 Data Science Projects That Will Get You Hired in 2018
Math for Machine Learning
Learning mathematics of Machine Learning: bridging the gap
Machine Learning Cheat Sheets
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