An extensive introduction into Dimension Reduction, including a look at some of the different techniques, linear discriminant analysis, principal component analysis, kernel principal component analysis, and more.
Machine learning, predictive analytics, IoT, smart cities and fintech are some of the hot topics where you need to know the latest. Get a sneak peak of upcoming DATAx with the DATAx New York festival post event report.
Also: Logistic Regression: A Concise Technical Overview; 7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition; Data Science Project Flow for Startups; Papers with Code: A Fantastic GitHub Resource for Machine Learning
Access a complimentary copy of the Gartner 2019 Magic Quadrant for Data Science and Machine-Learning Platforms to discover the latest trends and see why Dataiku was named a "Challenger" in the industry.
A detailed explanation of random forests, with real life use cases, a discussion into when a random forest is a poor choice relative to other algorithms, and looking at some of the advantages of using random forest.
In this webinar, Feb 12, find out how distributed deep learning works, get an an overview of the different frameworks, and learn how Databricks is making it easy for data scientists to migrate their single-machine workloads to distributed workloads, at all stages of a deep learning project.
After interviewing with over 50 companies for Data Scientist/Machine Learning Engineer, I am going to frame my experiences in the Q&A format and try to debunk any myths that beginners may have in their quest for becoming a Data Scientist.
With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python!
Also: 2018's Top 7 R Packages for Data Science and AI; Data Science Project Flow for Startups; What were the most significant machine learning/AI advances in 2018?; How to go from Zero to Employment in Data Science; Logistic Regression: A Concise Technical Overview
Read this white paper to discover what the future has in store for robotic automation, as well as the current limitations of RPA, how to move past these limitations, why an intelligent future is the natural progression for RPA, and more.
ODSC East in Boston will be the top global event in 2019 that gets you ahead. We offer an unparalleled 350 hours of talks, workshops and training sessions across 14 tracks. Register now with code KDN55 to save 55%!
The new Global Master of Management Analytics* from Smith School of Business at Queen’s University (Canada) will put you at the forefront of this rapidly growing field. You can join our next class from anywhere in the world. On January 31st, attend an online information session.
Because big data touches almost every industry across the board, those who aren’t already working in data and analytics will soon be utilizing the technology for its undeniable business benefits. Whichever way you slice it, the future of work is through data.
The aim of this post, then, is to present the characteristic project flow that I have identified in the working process of both my colleagues and myself in recent years. Hopefully, this can help both data scientists and the people working with them to structure data science projects in a way that reflects their uniqueness.
Also: Why Ice Cream Is Linked to Shark Attacks; Data Scientist’s Dilemma: The Cold Start Problem; 10 Exciting Ideas of 2018 in #NLP; What were the most significant machine learning/AI advances in 2018?
This is an overview of designing a computer program capable of developing predictive models without any manual intervention that are trained & evaluated in a lifelong machine learning setting in NeurIPS 2018 AutoML3 Challenge.
Logistic Regression is a Regression technique that is used when we have a categorical outcome (2 or more categories). Logistic Regression is one of the most easily interpretable classification techniques in a Data Scientist’s portfolio.
Also: How to build an API for a machine learning model in 5 minutes using Flask; How to solve 90% of NLP problems: a step-by-step guide; Data Scientists Dilemma: The Cold Start Problem - Ten Machine Learning Examples; Ontology and Data Science; 9 Must-have skills you need to become a Data Scientist, updated
Will 2019 be the year your organization embraces AI? If not, it might be too late. In this live webinar, we will unveil five major predictions companies should pay close attention to in 2019. Register now!
2018 was an exciting year for Machine Learning and AI. We saw “smarter” AI, real-world applications, improvements in underlying algorithms and a greater discussion on the impact of AI on human civilization. In this post, we discuss some of the highlights.
Together, artificial intelligence (AI) and data science are causing positive developments for the utilities providers that choose to investigate these things. Here are some examples of technology at work.
KDD-2019 invites submission of papers describing innovative research on all aspects of data science, and of applied papers describing designs and implementations for practical tasks in data science. Submissions due Feb 3.
An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.
2 graduate programmes now available at Data ScienceTech Institute in France: Applied MSc in Data Engineering Applied MSc in Data Science & Artificial Intelligence, with enterprise level certifications included in each. There is a 100% conversion to an internship and 90% to a job contract.
This book covers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning.
We present an array of examples showcasing the cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution.
Whilst there are many barriers to implementing AI effectively to drive results across underwriting, customer service and claims, it is essential for winning and retaining customers in the future. Join this webinar and learn how.
In simple words, one can say that ontology is the study of what there is. But there is another part to that definition that will help us in the following sections, and that is ontology is usually also taken to encompass problems about the most general features and relations of the entities which do exist.
Build statistical and analytical expertise as well as the management and leadership skills necessary to implement high-level, data-driven decisions in Northwestern’s online Master of Science in Data Science program. Apply now!
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
Also: Top 10 Books on NLP and Text Analysis; Practical Apache Spark in 10 Minutes; The cold start problem: how to build your machine learning portfolio; Core Principles of Sustainable Data Science, Machine Learning and AI Product Development; 4 Myths of Big Data and 4 Ways to Improve with Deep Data
The classic IT question: Which is better for your career, certs or a degree? The answer: Both. For your best shot at a successful data career, you need a degree and certs. Get the credentials you need and turn your career up a degree. Find out what WGU can offer you!
Get your ticket now for PAW Industry 4.0 and DLW Munich, 6-7 May 2019, and enter a world full of Predictive Maintenance, Anomaly Detection, Risk Management, Internet of Things, Deep Learning, Machine Learning & many more related topics!
Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
A frenzy of number-crunching is churning out a heap of insights that are colorful, sometimes surprising, and often valuable. We explain how this works, and investigate five bizarre discoveries found in data.
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?
At RE•WORK, the team are dedicating 2019 to keep up the high-quality events and to bring you the latest innovations & breakthroughs in AI. RE•WORK are offering a huge saving on all summit passes when you register with the discount code NEWYEAR.
Also: Common mistakes when carrying out machine learning and data science; Learning Machine Learning vs Learning Data Science; Here are the most popular Python IDEs / Editors; 10 More Must-See Free Courses for Machine Learning and Data Science.
There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.
When it comes to choosing the right book, you become immediately overwhelmed with the abundance of possibilities. In this review, we have collected our Top 10 NLP and Text Analysis Books of all time, ranging from beginners to experts.
Regardless of the size of your organisation, if you are developing machine learning or AI products, the core asset you have is a research professional, data scientist or AI scientist, regardless of their academic background.
Do you want to achieve new, exciting educational and career goals? Consider NYU Stern MS in Business Analytics, a one-year, part-time graduate degree for experienced professionals. Apply by Feb 15 to start in May.
With Penn State's Master of Professional Studies in Data Analytics offered online through Penn State World Campus, you can evolve as a primary influencer who drives key business decisions. We are currently reviewing applications for summer and fall 2019!
Also: A Guide to Decision Trees for Machine Learning and Data Science; The cold start problem: how to build your machine learning portfolio; Comparison of the Top Speech Processing APIs; Synthetic Data Generation: A must-have skill for new data scientists; Approaches to Text Summarization: An Overview
Rev features interactive sessions, Q&A with industry luminaries, poster sessions for interesting modeling techniques and accomplishments, and stimulating conversations about how to make data science an enterprise-grade capability.
There are plenty of library options out there to make great visualizations. We outline five of the best, complete with code examples and explanations, that will enable you to create and build interactive visualizations.
Strata Data Conference is in San Francisco Mar 25-28. Best price for Strata San Francisco expires on Friday, Jan 11. KDnuggets readers can save an additional 20% on Gold, Silver, and Bronze passes with code KDNU (up to $849 on a Gold pass).
However, sometimes only a limited amount of data from the target distribution can be collected. It may not be sufficient to build the needed train/dev/test sets. What to do in such a case? Let us discuss some ideas!
If you are a developer looking to hone your skills, a tech lead and manager to learn latest AI tech that apply to your engineering teams to innovate products and services, or someone who just wants to learn more about the AI industry that's re-shaping the tech world, the AI NEXTCon is right for you.
A crucial aspect of machine learning is its ability to recognize error margins and to interpret data more precisely as rising numbers of datasets are fed through its neural network. Commonly referred to as backpropagation, it is a process that isn’t as complex as you might think.
Also: Machine Learning Cheat Sheets; Papers with Code: A Fantastic GitHub Resource for Machine Learning; Neural network AI is simple. So… Stop pretending you are a genius; Top Python Libraries in 2018 in Data Science, Deep Learning and Machine Learning
This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.