We provide an in-depth introduction to Random Forest, with an explanation to how it works, its advantages and disadvantages, important hyperparameters and a full example Python implementation.
What linear regression is and how it can be implemented for both two variables and multiple variables using Scikit-Learn, which is one of the most popular machine learning libraries for Python.
Adversarially Constrained Autoencoder Interpolation (ACAI; Berthelot et al., 2018) is a regularization procedure that uses an adversarial strategy to create high-quality interpolations of the learned representations in autoencoders.
If you work with data in your organization and want to expand your role, or you're considering a career move into a data analytics or data science role, the University of Delaware's 100% online Master of Science in Applied Statistics (ASTAT) can help you succeed.
We are observing an increasing number of great tools that help facilitate the intricate process that is deep learning, making it both more accessible and more efficient.
Also - 7 Steps to Mastering Basic Machine Learning with Python - 2019 Edition; 10 Free Must-See Courses for Machine Learning and Data Science; How to Train a Keras Model 20x Faster with a TPU for Free.
The Predictive Analytics Innovation Summit takes place Apr 29 & 30 in San Diego. Secure your place at this must-attend event for data professionals today and deep-dive into a new era of AI and data strategy.
A brief overview of a new method for explainable AI (XAI), called anchors, introduce its open-source implementation and show how to use it to explain models predicting the survival of Titanic passengers.
This article presents an infographic for choosing which chart type is most useful in a given scenario. The infographic and chart types are then explored for greater clarity.
Also: 8 Reasons Why You Should Get a Microsoft Azure Certification; How To Work In Data Science, AI, Big Data; How to Train a Keras Model 20x Faster with a TPU for Free; Who is a typical Data Scientist in 2019?; My Best Tips for Agile Data Science Research
This eBook includes insights on how data scientists from 4 leading companies delivered impressive business results such as accelerating global inventory from 48 hours to 45 minutes and reducing operational cost of analytics infrastructure by 30%. Get the eBook now!
We outline our four-step model to categorize how successfully a company uses analytics by its ability to show the analytics, uncover underlying trends, and take action based on them.
Object Detection in Aerial Images is a challenging and interesting problem. By using Keras to train a RetinaNet model for object detection in aerial images, we can use it to extract valuable information.
This article contains an interview veteran data scientist, Dr Stylianos (Stelios) Kampakis, in which he discusses his career, and how he helps decision makers across a range of businesses understand how data science can benefit them.
IBM’s Data Science Professional Certificate program on Coursera brings you everything you need to plunge into an exciting career in data science—no prior experience required! Start learning today.
Data and AI are all about scale. Databricks is bringing the Spark + AI Summit to San Francisco Apr 23-25. Check out the full list of sessions at Summit to see more exciting talks. Use code KDNuggets200 and get $200 off registration.
Introducing Black Box AI, a system for automated decision making often based on machine learning over big data, which maps a user’s features into a class predicting the behavioural traits of the individuals.
This article demonstrates creating similar plots in R and Python using two of the most prominent data visualization packages on the market, namely ggplot2 and Seaborn.
This tutorial discusses how to use the genetic algorithm (GA) for reducing the feature vector extracted from the Fruits360 dataset in Python mainly using NumPy and Sklearn.
Mode is the data science platform that helps you get data in every corner of your business and create a single source of truth. Free your data science team, automate everything, and create a single source of truth.
A hands-off approach would seem reckless for questions about things like security. And yet that approach is not just the norm for analytical questions in most organizations; it's often the ideal.
At Vettery, we're flipping the job search on its head. Take a breather and consider an important question: is your job working just as hard as you are? If you're wondering what's out there or if you're ready to take a big step in your career, let your next job come to you.
Data science is said to change the manufacturing industry dramatically. Let's take under consideration several data science use cases in manufacturing that have already become common and brought benefits to the manufacturers.
Also: Artificial Neural Networks Optimization using Genetic Algorithm with Python; How To Work In Data Science, AI, Big Data; Why #BERT has 3 Embedding Layers and Their Implementation Details #DeepLearning; How to Train a Keras Model 20x Faster with a TPU for Free
We explain deep compression for improved inference efficiency, mobile applications, and regularization as technology cozies up to the physical limits of Moore's law.
ODSC is delighted to host an incredible lineup of leading experts in data science and AI from around the globe, with conferences in Boston, San Francisco, London, Bengaluru, São Paulo, and more on the way. Registration is now open!
Deep Learning World Munich is coming May 6-7 in Munich, Germany. You still have the chance to get your early bird pass until Apr 5. Secure your ticket for a lower price and access case studies and deep dives covering the commercial deployment of deep learning!
This post shows how to train an LSTM Model using Keras and Google CoLaboratory with TPUs to exponentially reduce training time compared to a GPU on your local machine.
Also: Another 10 Free Must-Read Books for Machine Learning and Data Science; Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision; My favorite mind-blowing Machine Learning/AI breakthroughs; The 7 Myths of Data Anonymisation
PAW Workshops are coming to four cities in 2019. Along with top-tier keynotes and information-packed sessions, Predictive Analytics World brings you a wide selection of in-depth workshops this year in Munich, Las Vegas, London, and Berlin.
With huge and growing popularity of Microsoft Azure, getting that certification will advance your career. Consider these 8 reasons for taking an Azure certification course
We outline the importance of overcoming distrust in data and analytics, with tips on how to align all stakeholders, being a data optimist, streamlining the process, and more.
There are many facets to working in Data Science. Your role will depend greatly on the industry you pick and the area of Data Science you want to pursue. A Data Science career is very dynamic and requires a team effort to succeed.
In this blog, I’ll walk you through a personal project in which I cheaply built a classifier to detect anti-semitic tweets, with no public dataset available, by combining weak supervision and transfer learning.
RE-WORK returns to Boston in May to showcase global experts in Deep Learning. Get up to 50% off passes with code KDNUGGETS if you register before March 22.
Gain the necessary technical skills to further your career in an ever-growing sector with U. of Bath Computer Science online MSc. Apply until 1 April 2019.
We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.
The rise of ML Engineering; Build your own Robust #DeepLearning Environment in Minutes; Another 10 Free Must-Read Books for Machine Learning and Data Science; Top 5 #MachineLearning Courses for 2019 - from @Coursera and @EdX.
Learn effective data governance practices and how to successfully implement advanced analytics by attending our industry leading training at TDWI Chicago, April 28 - May 3, and take your projects to the next level.
The Executive Guide covers the benefits to your business, the build-or-buy process, and gives a practical overview for implementing ML in your organization.
The basic idea looks simple: find the gist, cut off all opinions and detail, and write a couple of perfect sentences, the task inevitably ended up in toil and turmoil. Here is a short overview of traditional approaches that have beaten a path to advanced deep learning techniques.
During their 10-month tenure, Mozilla fellows design products, run campaigns, and influence policy around the theme of “better machine decision making.” Fellows receive competitive funding + benefits, and a travel stipend. Apply by April 8.
In this article you will learn about Luminoth, an open source computer vision library which sits atop Sonnet and TensorFlow and provides object detection for images and video.
This guidebook walks through the myths & realities of pseudonymization and working with personal data, and suggests data team processes for compliance.
With four highly-specialised data analytics modules, and the practical business knowledge provided by the core MBA modules, NTU online course can prepare you for a career in big data.
An analysis of the current state of the competition between US, Europe, and China in AI, examining research, patent publications, global datasphere, devices and IoT, people, and more.
Anonymisation has always been rather seen as a necessary evil instead of a helpful tool. That’s why plenty of myths have arisen around that technology over the years.
AI is expected to contribute $150bn value to business by 2026. How can AI succeed in your industry? Do you know how to set an ambitious AI vision within your organization? Download your free ebook to find these solutions.
All Manning live video courses, includes courses on AI, Big Data, Deep Learning, Machine Learning, Reinforcement Learning, and more - are on sale until March 31 - only twenty five dollars.
We investigate what a typical data scientist looks like and see how this differs from this time last year, looking at skill set, programming languages, industry of employment, country of employment, and more.
In this webinar from DataRobot, learn common automated machine learning use cases how automated machine learning enables more employees to take part in AI initiatives while making existing data science teams more productive, and more!
Join to connect with your peers, colleagues, and friends! Network, share and learn from the 60+ expert speakers across 2 immersive days. Register for your free pass!
We investigate how to use a custom loss function to identify fair odds, including a detailed example using machine learning to bet on the results of a darts match and how this can assist you in beating the bookmaker.
For the 2019 international women's day, we profile a new set of 19 inspiring women who lead the field in AI, Big Data, Data Science, and Machine Learning fields.
Ethical algorithm design is becoming a hot topic as machine learning becomes more widespread. But how do you make an algorithm ethical? Here are 5 suggestions to consider.
Also: Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms; The Essential Data Science Venn Diagram.
Good things come in threes, including Online Learning courses. Introducing Data Science & Build Your Own Bundles: bundles include three full courses, allowing data and analytics pros to both broaden and deepen skills across today's hottest topics and save 15% for maximum ROI.
Check out these historical Predictive Analytics World milestones, from spawning the Target-predicting-pregnancy publicity debacle, to getting dinged by the Hollywood action movie star Chuck Norris, to growing into the leading international event series it is today.
Strata Data Conference is coming to London Apr 29-May 2. Discover what's coming in data and AI—and how to implement it for your business. Save 20% on Gold, Silver, and Bronze passes with code KDNU until Friday, Mar 15.
CRM/Consumer analytics, health care, banking, finance, and science were the top sectors in 2018. The greatest increases were in mobile apps, investment, security, entertainment, and social policy, while fraud detection, retail, advertising, direct marketing, and social media saw the greatest declines.
In this tutorial, you will learn to implement Linear Regression for prediction using Numpy in detail and also visualize how the algorithm learns epoch by epoch. In addition to this, you will explore two layer Neural Networks.
Today we’re looking at a more general fake news problem: detecting fake news that is being spread on a social network. This is a summary of a recent paper which demonstrates why we should also look at the social context: the publishers and the users spreading the information!
This book provides a strong connection between the concepts in data science and process engineering needed to ensure better quality and takes you through a systematic approach to measure holistic quality with several case studies.
Strata Data Conference is coming to San Francisco Mar 25-28. Register by Friday, Mar 8, with the code FREEROOM, and you'll get a night at the Hilton Union Square on us (and 20% off Gold, Silver, and Bronze passes).
Come to New York City on May 23–24 for Rev 2, and learn from data science teams and leaders. This year’s focus is “What can teams learn from each other?”
Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.
In this post, the author shows how BERT can mimic a Bag-of-Words model. The visualization tool from Part 1 is extended to probe deeper into the mind of BERT, to expose the neurons that give BERT its shape-shifting superpowers.
Learn how to cut through the complexity of scientific language; hear how SciBite puts AI techniques in action, and find how to supercharge search at your organization.
With Drexel University's online MS in Business Analytics program, you'll be able to effectively analyze this data to give your company and yourself a competitive edge. Learn more today!
We explain why state-of-the-art Deep Neural Networks can still recognize scrambled images perfectly well and how this helps to uncover a puzzlingly simple strategy that DNNs seem to use to classify natural images.
In this tutorial, you will get a brief understanding of what Neural Networks are and how they have been developed. In the end, you will gain a brief intuition as to how the network learns.
Self-Attention Generative Adversarial Networks (SAGAN; Zhang et al., 2018) are convolutional neural networks that use the self-attention paradigm to capture long-range spatial relationships in existing images to better synthesize new images.
Also: Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters; How to do Everything in Computer Vision; What no one will tell you about data science job applications; Python Data Science for Beginners
Start planning for PAW Healthcare 2019 in Las Vegas Jun 16-20 and get ready to hear excellent sessions and case studies across healthcare business operations and clinical applications. Save til Friday!
ODSC East 2019 has multiple tracks for both Data Scientists and Data Engineers, including workshops, talks, and training sessions. Save 45% with code KDN45.
OpenAI recently released a very large language model called GPT-2. Controversially, they decided not to release the data or the parameters of their biggest model, citing concerns about potential abuse. Read this researcher's take on the issue.
StreamAnalytix is an Apache Spark based big data analytics and machine learning platform. It offers an intuitive visual development environment to rapidly build and operationalize batch + streaming applications, across industries, data formats, and use cases.
For every person who has a question relating to a data science job application, and asks it, there are ten people who have the same question, but don’t ask it. If you’re one of those ten, then this post is for you.
MobileNets are a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.
As both research and applied teams are doubling down on their engineering and infrastructure needs, the nascent field of ML Engineering will build upon 2018’s foundation and truly blossom in 2019.
Explaining Random Forest® (with Python Implementation)
R vs Python for Data Visualization
Artificial Neural Networks Optimization using Genetic Algorithm with Python
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