All (102) | Courses, Education (7) | Meetings (9) | News, Features (9) | Opinions, Interviews (24) | Top Stories, Tweets (10) | Tutorials, Overviews (36) | Webcasts & Webinars (7)
- Mega-PAW: Largest Predictive Analytics World, Las Vegas, June 2018 – Super Early Bird Discount until Dec 22 - Nov 30, 2017.
In June 2018, Las Vegas will host the largest Predictive Analytics World ever, with PAW Business, Financial, Healthcare, and Manufacturing, and Deep Learning World. Get SEB discount till Dec 22.
- InfoGAN - Generative Adversarial Networks Part III - Nov 30, 2017.
In this third part of this series of posts the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome.
- Machine Learning with Optimus on Apache Spark - Nov 30, 2017.
The way most Machine Learning models work on Spark are not straightforward, and they need lots of feature engineering to work. That’s why we created the feature engineering section inside the Optimus Data Frame Transformer.
- Top KDnuggets tweets, Nov 22-28: Reinforcement Learning: An Introduction by Sutton and Barto – Complete Second Draft - Nov 29, 2017.
Also #DeepLearning Specialization by Andrew Ng - 21 Lessons Learned; How (and Why) to Create a Good Validation Set; Predicting Cryptocurrency Prices With #DeepLearning
- Evolutionary Algorithms for Feature Selection - Nov 29, 2017.
Feature selection is a very important technique in machine learning. In this post we discuss one of the most common optimization algorithms for multi-modal fitness landscapes - evolutionary algorithms.
- Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras - Nov 29, 2017.
We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.
- Why You Should Forget ‘for-loop’ for Data Science Code and Embrace Vectorization - Nov 29, 2017.
Data science needs fast computation and transformation of data. NumPy objects in Python provides that advantage over regular programming constructs like for-loop. How to demonstrate it in few easy lines of code?
- Multichannel Marketing Attribution with Automated Machine Learning, Dec 12 Webinar - Nov 28, 2017.
In this webinar, Dec 12, DataRobot outlines Multichannel Marketing Attribution with Automated Machine Learning, demonstrating how automated machine learning offers the shortest path to success. Space is limited, so sign up now!
- Natural Language Processing Library for Apache Spark – free to use - Nov 28, 2017.
Introducing the Natural Language Processing Library for Apache Spark - and yes, you can actually use it for free! This post will give you a great overview of John Snow Labs NLP Library for Apache Spark.
- Fusing Human and Machine for Seamless, Automated Insurance Claims (Webinar, Dec 14) - Nov 28, 2017.
Insurance claims is standing on the brink of transformation with new technology uncovering opportunities to process claims more efficiently and provide a superior customer experience. Learn about the oportunities in this Dec 14 Webinar.
- How To Unit Test Machine Learning Code - Nov 28, 2017.
One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time.
- Top KDnuggets tweets, Nov 15-21: DeepLearning is “shallow”: here are underlying concepts you need - Nov 27, 2017.
Also: New Poll: Data Science / Machine Learning methods you used; The amazing predictive power of conditional probability in Bayes Nets; The 10 Statistical Techniques Data Scientists Need to Master.
- Survival Analysis for Business Analytics - Nov 27, 2017.
We compare survival analysis to other predictive techniques, and provide examples of how it can produce business value, with a focus on Kaplan-Meier and Cox Regression methods which have been underutilized in business analytics.
- Analyzing the Migration of Scientific Researchers - Nov 27, 2017.
This is a visualization of the inter- and intra-continental migration of scientific researchers based on ORCID (Open Researcher and Contributor ID) data. It is best seen as a directional sample of all researchers, and tracks their earliest/latest countries with research activities as well as their PhD countries.
- Implementing Enterprise AI course using TensorFlow and Keras - Nov 27, 2017.
The course is for developers and architects who want to transition their career to Enterprise AI, but also has strategic (non-coding) version. The course starts in Jan 2018 and will take 3 months for the content and up to 3 months for the team project.
- Top Stories, Nov 20-26: Deep Learning Specialization by Andrew Ng – 21 Lessons Learned; A Framework for Approaching Textual Data Science Tasks - Nov 27, 2017.
Also: Estimating an Optimal Learning Rate For a Deep Neural Network; Automated Feature Engineering for Time Series Data; How (and Why) to Create a Good Validation Set; Building a Wikipedia Text Corpus for Natural Language Processing; The 10 Statistical Techniques Data Scientists Need to Master
- Deep Learning Specialization by Andrew Ng – 21 Lessons Learned - Nov 24, 2017.
I found all 3 courses extremely useful and learned an incredible amount of practical knowledge from the instructor, Andrew Ng. Ng does an excellent job of filtering out the buzzwords and explaining the concepts in a clear and concise manner.
- How (and Why) to Create a Good Validation Set - Nov 24, 2017.
The definitions of training, validation, and test sets can be fairly nuanced, and the terms are sometimes inconsistently used. In the deep learning community, “test-time inference” is often used to refer to evaluating on data in production, which is not the technical definition of a test set.
- Are Scientists Doing Too Much Research? - Nov 24, 2017.
At the heart of this reproducibility problem is the statistical inference methods used to validate research findings—specifically the concept of “statistical significance.”
- Understanding Objective Functions in Neural Networks - Nov 23, 2017.
This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.
- Cartoon: Thanksgiving, Big Data, & Turkey Data Science. - Nov 23, 2017.
A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists.
- Building a Wikipedia Text Corpus for Natural Language Processing - Nov 23, 2017.
Wikipedia is a rich source of well-organized textual data, and a vast collection of knowledge. What we will do here is build a corpus from the set of English Wikipedia articles, which is freely and conveniently available online.
- Taming the Python Visualization Jungle, Nov 29 Webinar - Nov 22, 2017.
Python has a ton of plotting libraries—but which ones should you use? And how should you go about choosing them? This webinar shows you key starting points and demonstrates how to solve a range of common problems.
- Did Spark Really Kill Hadoop? - Nov 22, 2017.
A comprehensive survey conducted by iDatalabs shows us the trends of the future of these two Data Science technologies.
- A Framework for Approaching Textual Data Science Tasks - Nov 22, 2017.
Although NLP and text mining are not the same thing, they are closely related, deal with the same raw data type, and have some crossover in their uses. Let's discuss the steps in approaching these types of tasks.
- Chief Data & Analytics Officer Sydney, Mar 20-22, KDnuggets Offer - Nov 21, 2017.
Chief Data & Analytics Officer Sydney event has assembled an outstanding speaker line up to address all things data and analytics. Special KDnuggets discount.
- Best Masters in Data Science and Analytics in US/Canada - Nov 21, 2017.
Second comprehensive list of master's degrees in the US and Canada with tuition information and duration.
- Using TensorFlow for Predictive Analytics with Linear Regression - Nov 21, 2017.
This post presents a powerful and simple example of how to use TensorFlow to perform a Linear Regression. check out the code for your own experiments!
- Key Takeaways from Open Data Science Conference (ODSC) West 2017 - Nov 21, 2017.
This year, the ODSC West was held at the Hyatt Regency San Francisco Airport, from November 2 to 4. I am, attempting here, to give you a snapshot tour of what I experienced.
- Estimating an Optimal Learning Rate For a Deep Neural Network - Nov 21, 2017.
This post describes a simple and powerful way to find a reasonable learning rate for your neural network.
- [eBook] A Gentle Introduction to Apache Spark(tm) - Nov 21, 2017.
If you are a developer or data scientist interested in big data, Spark is the tool for you. Download this ebook to learn why Spark is a popular choice for data analytics, what tools and features are available, and much more.
- Call for Bids to Host KDD-202x - Nov 20, 2017.
ACM SIGKDD Executive Committee hereby invites proposals to host the annual KDD Conference in 2020 and later. Proposals due Jan 31, 2018.
- NVIDIA DGX Systems – Deep Learning Software Whitepaper - Nov 20, 2017.
Download this whitepaper from NVIDIA DGX Systems, and gain insight into the engineering expertise and innovation found in pre-optimized deep learning frameworks available only on NVIDIA DGX Systems and learn how to dramatically reduce your engineering costs using today’s most popular frameworks.
- A Course in Semantic Technologies for Designing a Proof-of-Concept, starting Nov 30 - Nov 20, 2017.
Ontotext live, online training designed to improve understanding of how Semantic Technology operates to help you make best use of it. Preparation starts Nov 30 and live class is Dec 7.
- New Poll: Which Data Science / Machine Learning methods and tools you used? - Nov 20, 2017.
Please vote in new KDnuggets poll which examines the methods and tools used for a real-world application or project.
- Automated Feature Engineering for Time Series Data - Nov 20, 2017.
We introduce a general framework for developing time series models, generating features and preprocessing the data, and exploring the potential to automate this process in order to apply advanced machine learning algorithms to almost any time series problem.
- DataScience.com Adds Former U.S. Chief Data Scientist DJ Patil to Advisory Board - Nov 20, 2017.
Former U.S. Chief Data Scientist DJ Patil will be lending his expertise to DataScience.com’s product, engineering, and R&D teams as they expand the features of the company’s enterprise data science platform.
- Top Stories, Nov 13-19: The 10 Statistical Techniques Data Scientists Need to Master; Best Online Masters in Data Science and Analytics – a comprehensive, unbiased survey - Nov 20, 2017.
Also: A Day in the Life of a Data Scientist; Top 10 Videos on Deep Learning in Python; 8 Ways to Improve Your Data Science Skills in 2 Years; Machine Learning Algorithms: Which One to Choose for Your Problem; Top 10 Machine Learning Algorithms for Beginners
- How (& Why) Data Scientists and Data Engineers Should Share a Platform - Nov 17, 2017.
Sharing one platform has some obvious benefits for Data Science and Data Engineering teams, but technical, language and process challenges often make this a challenge. Learn how one company implemented single cloud platform for R, Python and other workloads – and some of the unexpected benefits they discovered along the way.
- Best Data Science, Machine Learning Courses from Udemy, only $10 until Nov 28- Black Friday/Cybermonday sale - Nov 17, 2017.
Black Friday/Cybermonday sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until Nov 28, 2017.
- We Speak Data at TDWI Las Vegas, Feb 11-16. Save w. code KD30 thru Dec 15 - Nov 17, 2017.
TDWI provides the in-depth, vendor-neutral training in business analytics, data science, and data management, including a certificate track. Save 30% thru Dec 15, 2017 with code KD30.
- Generative Adversarial Networks — Part II - Nov 17, 2017.
Second part of this incredible overview of Generative Adversarial Networks, explaining the contributions of Deep Convolutional-GAN (DCGAN) paper.
- Top 10 Videos on Deep Learning in Python - Nov 17, 2017.
Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!
- Stop Doing Fragile Research - Nov 17, 2017.
If you develop methods for data analysis, you might only be conducting gentle tests of your method on idealized data. This leads to “fragile research,” which breaks when released into the wild. Here, I share 3 ways to make your methods robust.
- 8 Ways to Improve Your Data Science Skills in 2 Years - Nov 17, 2017.
Two years. Two years is the maximum amount of time you should spend focused on your learning, education and training. That’s exactly why this guide is focused on honing the most beneficial skills in two years.
- Webinar: Data Preparation Essentials for Automated Machine Learning, Nov 29 - Nov 16, 2017.
Jen Underwood will review how to organize data in a machine learning-friendly format that accurately reflects the business process and outcomes.
- Deep Learning in Robotics and Healthcare Summits: Join & save with KDnuggets offer - Nov 16, 2017.
RE•WORK are pleased to announce the launch of 'Expo Only Passes' for the upcoming San Francisco events, on January 25 from 14:00 - 18:00. Plus, save 20% on passes to all RE•WORK summits with the code KDNUGGETS.
- The Python Graph Gallery - Nov 16, 2017.
Welcome to the Python Graph Gallery, a website that displays hundreds of python charts with their reproducible code snippets.
- Capsule Networks Are Shaking up AI – Here’s How to Use Them - Nov 16, 2017.
If you follow AI you might have heard about the advent of the potentially revolutionary Capsule Networks. I will show you how you can start using them today.
- PySpark SQL Cheat Sheet: Big Data in Python - Nov 16, 2017.
PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. With Spark, you can get started with big data processing, as it has built-in modules for streaming, SQL, machine learning and graph processing.
- Top KDnuggets tweets, Nov 08-14: Approaching (Almost) Any NLP Problem on #Kaggle; Choosing an Open Source #MachineLearning Library - Nov 15, 2017.
Also: What is the difference between Bagging and Boosting?; Which #Python package manager should you use?; The Practical Importance of Feature Selection.
- Basic Concepts of Feature Selection - Nov 15, 2017.
Feature selection is a key part of data science but is it still relevant in the age of support vector machines (SVMs) and Deep Learning? Yes, absolutely. We explain why.
- You have created your first Linear Regression Model. Have you validated the assumptions? - Nov 15, 2017.
Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.
- The 10 Statistical Techniques Data Scientists Need to Master - Nov 15, 2017.
The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.
- MS in Business Analytics from NYU Stern – Advance your career - Nov 14, 2017.
Organizations are seeking top-notch, global talent that understand how to effectively leverage data to make more informed decisions. Just ask Deepesh Chandra, a recent graduate of of NYU Stern MS in Business Analytics.
- Best Online Masters in Data Science and Analytics – a comprehensive, unbiased survey - Nov 14, 2017.
The first comprehensive and objective survey of online Masters in Analytics / Data Science, including rankings, tuition, and duration of the education program.
- Extracting Tweets With R - Nov 14, 2017.
This article will give you a great, brief overview for extracting Tweets using R.
- Some Things to Remember About Memory - Nov 14, 2017.
A lot of the recent buzz about memory is old news.
- Machine Learning Algorithms: Which One to Choose for Your Problem - Nov 14, 2017.
This article will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you’ll find the structured overview of the main features of described algorithms.
- Strata Data Conference, San Jose, Mar 5-8, 2018 – KDnuggets Offer - Nov 13, 2017.
Strata Data Conference is where thousands of innovators, leaders, and practitioners gather to develop new skills, share best practices, and discover how tools and technologies are evolving. Best rate ends Dec 8 - use code PCKDNG to save.
- Your guide to predictive analytics in media and entertainment - Nov 13, 2017.
Download your free guide to predictive analytics in media and entertainment for a look at the landscape and use cases, from Dataiku.
- The amazing predictive power of conditional probability in Bayes Nets - Nov 13, 2017.
This article explains how Bayes Nets gain remarkable predictive power by their use of conditional probability. This adds to several other salient strengths, making them a preeminent method for prediction and understanding variables’ effects.
- Top Stories, Nov 6-12: When Will Demand for Data Scientists/Machine Learning Experts Peak?; Interpreting Machine Learning Models: An Overview - Nov 13, 2017.
Also: TensorFlow: What Parameters to Optimize?; 7 Super Simple Steps From Idea To Successful Data Science Project; Tips for Getting Started with Text Mining in R and Python; Top 10 Machine Learning Algorithms for Beginners
- A Day in the Life of a Data Scientist - Nov 13, 2017.
Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these five individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
- Stanford online Data Science and Data Mining courses and certificates - Nov 10, 2017.
With our Online Data Mining Certificates, you’ll learn to guide important business decisions, become indispensable to your organization, and give your career a boost. Benefit from flexibility, world-class teaching and research, and a Stanford credential.
- Overview of GANs (Generative Adversarial Networks) – Part I - Nov 10, 2017.
A great introductory and high-level summary of Generative Adversarial Networks.
- 2018 Will Be the Perfect Time to Build an AI Startup - Nov 10, 2017.
At the core, AI is actually built into many technologies currently in use, and it’s probably not as risky an investment as you might think.
- Webinar: Take Your Data to The Next Level with Embedded Analytics, Nov 16 - Nov 9, 2017.
Learn how data-driven businesses use Looker embedded platform to provide real time, data-driven insights to customers and suppliers.
- Learn to turn data into revenue at Wharton - Nov 9, 2017.
Are you using your customer data to its full advantage? Chances are the answer is no. Customer Analytics, Feb 26-Mar 1, from Wharton Executive Education gives you a deeper, actionable understanding of your data.
- How Bayesian Networks Are Superior in Understanding Effects of Variables - Nov 9, 2017.
Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.
- The Qualitative Side of Quantitative Research - Nov 9, 2017.
Kevin and Koen may buy the same brand for the same reasons. On the other hand, they may buy the same brand for different reasons, or buy different brands for the same reasons, or even different brands for different reasons. The brands they purchase and the reasons why may vary by occasion, too.
- TensorFlow: What Parameters to Optimize? - Nov 9, 2017.
Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model.
- Top KDnuggets tweets, Nov 01-07: Airbnb develops an #AI which converts design into source code - Nov 8, 2017.
Also: One LEGO at a time: Explaining the #Math of How #NeuralNetworks Learn; 6 Books Every #DataScientist Should Keep Nearby; Direct from Sebastian Raschka #Python #MachineLearning book, new edition.
- Top October Stories: Top 10 Machine Learning Algorithms for Beginners - Nov 8, 2017.
Also: Understanding Machine Learning Algorithms; Want to Become a Data Scientist? Read This Interview First; 6 Books Every Data Scientist Should Keep Nearby.
- Data Scientist: The Hottest Job on Wall Street - Nov 8, 2017.
The demand for professionals that can build financial analytics programs is booming. We foresee two main objectives- to predict market movement for profit, and to protect customer assets of banks.
- Choosing an Open Source Machine Learning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe - Nov 8, 2017.
Open Source is the heart of innovation and rapid evolution of technologies, these days. Here we discuss how to choose open source machine learning tools for different use cases.
- 7 Super Simple Steps From Idea To Successful Data Science Project - Nov 8, 2017.
Ever had this great idea for a data science project or business? In the end you did not do it because you did not know how to make it a success? Today I am going to show you how to do it.
- Tips for Getting Started with Text Mining in R and Python - Nov 8, 2017.
This article opens up the world of text mining in a simple and intuitive way and provides great tips to get started with text mining.
- When Will Demand for Data Scientists/Machine Learning Experts Peak? - Nov 7, 2017.
We analyze the results of Data Science / Machine Learning peak demand poll, examine the split between optimists and pessimists, and try to explain why predictions look so similar regardless of experience, affiliation, and region?
- AI in Healthcare Summit, January 18-19, Boston - Nov 7, 2017.
Join us for this industry-leading event uniquely seated at the growing intersection of healthcare and cutting-edge technologies.
- DJ Patil Presents at Marketing Analytics and Data Science 2018 - Nov 7, 2017.
The 2018 Data Science & Marketing Analytics Conference, April 11-13, San Francisco, will focus on how Data can be used to drive specific business purposes. Exclusive Offer for KDnuggets Readers: Save 20% with VIP Code MADS18KDN.
- How to Job Interview a Data Scientist - Nov 7, 2017.
Data Scientist is a very broad term and hiring a good fit data scientist for your project is challenging task. Here we discuss this important topic in details.
- Interpreting Machine Learning Models: An Overview - Nov 7, 2017.
This post summarizes the contents of a recent O'Reilly article outlining a number of methods for interpreting machine learning models, beyond the usual go-to measures.
- Real World Deep Learning: Neural Networks for Smart Crops - Nov 7, 2017.
The advances in image classification, object detection, and semantic segmentation using deep Convolutional Neural Networks, which spawned the availability of open source tools such as Caffe and TensorFlow (to name a couple) to easily manipulate neural network graphs... made a very strong case in favor of CNNs for our classifier.
- Webinar: Transform the business with automated embedded Artificial Intelligence, Nov 16 - Nov 6, 2017.
Learn how much value companies can get by adding AI to business applications and processes through AI and automation, how to architect a smart business with ubiquitous AI, and more.
- The Guts and Glory of Data Science - Nov 6, 2017.
Are you a data science leader, or aspiring to be one? Learn how industry leaders manage their data science initiatives as core capabilities that drive their company’s strategic objectives.
- What is the difference between Bagging and Boosting? - Nov 6, 2017.
Bagging and Boosting are both ensemble methods in Machine Learning, but what’s the key behind them? Here we explain in detail.
- Top Stories, Oct 30-Nov 5: 6 Books Every Data Scientist Should Keep Nearby; Want to know how Deep Learning works? Here’s a quick guide for everyone. - Nov 6, 2017.
Also: Advice For New and Junior Data Scientists; 7 Steps to Mastering Deep Learning with Keras; Getting Started with Machine Learning in One Hour!; Top 10 Machine Learning Algorithms for Beginners
- How Do You Build a Great Analytic Culture? - Nov 3, 2017.
We need to create a sense of urgency around exploring and analyzing data. We also need to train and empower individuals to know how. This video covers the need for students to enter the workforce with analytics skills and why we need to give employees permission to fail.
- Are You Ready for the Future of Data? - Nov 3, 2017.
Join us at TDWI Orlando, Dec 3-8, where we bring the future of data and analytics to life. KDnuggets Readers Save 20% when you register by November 17 with priority code KDSUN.
- Blockchain Key Terms, Explained - Nov 3, 2017.
Need a quick glance over some important definitions associated with the Blockchain? Then consider this article your Blockchain Definitions 101!
- More than the Hype: Beyond Gartner’s Hype Cycle - Nov 3, 2017.
Gartner publishes hype cycles across different technologies and sectors. Here we conduct detailed analysis of Gartner’s Hype Cycles.
- Want to know how Deep Learning works? Here’s a quick guide for everyone - Nov 3, 2017.
Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.
- Cybersecurity: Managing Risk in the Information age, Harvard online short course - Nov 2, 2017.
Learn how to identify and manage operational risk, litigation risk and reputational risk. This course is brought to you by HarvardX in collaboration with GetSmarter, experts in online education for working professionals.
- Process Mining with R: Introduction - Nov 2, 2017.
In the past years, several niche tools have appeared to mine organizational business processes. In this article, we’ll show you that it is possible to get started with “process mining” using well-known data science programming languages as well.
- Machine Ethics and Artificial Moral Agents - Nov 2, 2017.
This article is simply a stream of consciousness on questions and problems I have been thinking and asking myself, and hopefully, it will stimulate some discussion.
- Advice For New and Junior Data Scientists - Nov 2, 2017.
This article is for people who are already in the field but are just starting out. My goal is to not only use this post as a reminder to myself about the important things that I have learned, but also to inspire others as they embark onto their DS careers!
- Top KDnuggets tweets, Oct 25-31: 30 Essential Data Science, Machine Learning, Deep Learning Cheat Sheets; Google Brain chief: DL takes at least 100,000 examples - Nov 1, 2017.
Also Applied #AI Summit will give you the tools for your AI journey, 5-7 Feb, London;10 Free Must-Read Books for Machine Learning, Data Science; Ranking Popular #DeepLearning Libraries for #DataScience.
- Upcoming Meetings in Analytics, Big Data, Data Science, Machine Learning: November 2017 and Beyond - Nov 1, 2017.
Coming soon: ODSC West, MLconf San Francisco, PAW Berlin, IEEE ICDM New Orleans, Data Marketing Toronto, Big Data & Analytics Innovation Summit Beijing, Chief Data Scientist San Francisco, and many more.
- 3 different types of machine learning - Nov 1, 2017.
In this extract from “Python Machine Learning” a top data scientist Sebastian Raschka explains 3 main types of machine learning: Supervised, Unsupervised and Reinforcement Learning. Use code PML250KDN to save 50% off the book cost.
- Conjoint Analysis: A Primer - Nov 1, 2017.
Conjoint is another of those things everyone talks about but many are confused about…
- Getting Started with Machine Learning in One Hour! - Nov 1, 2017.
Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.