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All Blog Posts Tagged 'machine' (189)

Top Data Science Use Cases in HR

Data Science methods and techniques allow new approaching the solution of complex tasks in terms of mathematics, and statistics for the various aspects and areas of our life, work, and business. Therefore, this makes it possible to produce the most unobvious…

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Added by Igor Bobriakov on January 16, 2020 at 10:30am — No Comments

Docker in 10 minutes

Software is always not only a set of instructions but also a context that manages, interacts and executes these instructions. At the start of developing, the engineer configures a dev environment. He can continue to change it in all stages of development. The problem appears when…

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Added by Igor Bobriakov on January 14, 2020 at 6:00pm — No Comments

Just How Much Does the Future Depend on AI?

Summary:  Looking at the 12 hottest world-changing segments in the VC-funded world shows that AI will play a key role.  Here’s a little more detail.

 

From the inside of the data science profession looking out it’s easy to imagine that almost everything that is or will be important somehow depends on AI.  Maybe…

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Added by William Vorhies on January 13, 2020 at 11:20am — 1 Comment

Scylla vs Cassandra: Performance Comparison

One of the main requirements for modern information systems is the high data processing rate.  Among the solutions to solve this problem the popular one is to use high-performance databases. This article will review and compare two popular databases in performance terms:…

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Added by Igor Bobriakov on January 10, 2020 at 12:12am — No Comments

5 Best Python Machine Learning Courses Online for 2020

Best Python Machine Learning course tutorial class certification training online

25 Experts have compiled this list of Best Python for Machine Learning Course, Tutorial, Training, Class, and Certification available online for 2020. It includes both paid and free resources to help you learn Python for Machine Learning and these courses are suitable for beginners, intermediate learners as well as experts.

1.Machine Learning with Python (Coursera) 

If you are interested in getting started with the field…

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Added by Digital Defynd on January 3, 2020 at 9:30pm — No Comments

Not tools, focusing on problems: a project cycle in data science

As a data scientist in an organization you frequently find yourself in a couple of situations:

  • you have a business…
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Added by Mab Alam on December 27, 2019 at 8:00pm — No Comments

Top NLP Algorithms & Concepts

Today, one of the most popular tasks in Data Science is processing information presented in the text form. Exactly this is text representation in the form of mathematical equations, formulas, paradigms, patterns in order to understand the text semantics (content) for its further…

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Added by Igor Bobriakov on December 21, 2019 at 6:56am — 1 Comment

A Question of Usability

Thinking of data science as merely a technical profession, like programming, may take you away from your goals. Focusing on the usability of mathematics for data science before jumping into full-fledged math courses will save you a lot of time.

I wrote this blog post because I made a few mistakes while starting…

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Added by Arnuld on December 19, 2019 at 4:00am — No Comments

Top 8 Data Science Use Cases in Production

Production sphere embraces a wide range of processes related to all branches and stages of creating material goods. In addition, these material goods may be of different values and even have rather contrasting goals. 

Under conditions…

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Added by Igor Bobriakov on December 18, 2019 at 8:30am — No Comments

Top 9 Data Science Use Cases in Analytics

Data science and analytics are growing in their popularity and range of applications in the modern world. Data science deals with answering questions and uncovering hidden insights, while analytics is rather concentrated on the processing itself and conducting of statistical…

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Added by Igor Bobriakov on December 18, 2019 at 8:00am — No Comments

H2O Framework for Machine Learning

H2O is a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks. The dataset we are using is the Bank Marketing Dataset. Here we need to train a model which will be able to predict if the client of the bank opens…

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Added by Igor Bobriakov on December 16, 2019 at 8:00am — No Comments

Sports Telemetry in Real-Time

The history of F1 motor racing and the use of telemetry as a way to monitor car setup and performance dates back to the 80s. The first electronic systems were installed onboard the car, collected information for only one lap and the data were then downloaded when the car was back in the garage. The explosion of computing capabilities, in the 90s, contributed to the growth of intelligent data usage in the F1 and the…

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Added by Valeria on December 13, 2019 at 1:00am — No Comments

Make Crucial Predictions as Data Comes

Walking by the hottest IT streets in these days means you've likely heard about achieving Streaming Machine Learning, i.e. moving AI towards streaming scenario and exploiting the real-time capabilities along with new Artificial Intelligence techniques. Moreover, you will also notice the lack of research related to this topic, despite the growing interest in it.

If we try to investigate it a little bit deeper then, we realize that…

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Added by Valeria on December 10, 2019 at 7:30am — No Comments

Why Event Stream Processing Is Leading the New Big Data Era

Big Data is probably one of the most misused words of the last decade. It was widely promoted, discussed, and spread around by business managers, technical experts, and experienced academics. Slogans like “Data is the new oil” were widely accepted as unquestionable truth.

These beliefs pushed  technologies forward. Its stack, formerly developed by Yahoo! and now owned by the Apache Software Foundation, was recognized as “The” Big Data…

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Added by Valeria on December 10, 2019 at 7:21am — No Comments

Fighting Overfitting in Deep Learning

Problem

While training the model, we want to get the best possible result according to the chosen metric. And at the same time we want to keep a similar result on the new data. The cruel truth is that we can’t get 100% accuracy. And even if we did, the result is still not…

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Added by Igor Bobriakov on December 6, 2019 at 9:00am — No Comments

Visually Explained: How Can Executives Grasp What Programming Is All About?

Quite often, non-technical executives have difficulties understanding what programming, on a very fundamental level, is all about. Because of that knowledge-gap, they tend to hire and overburden experienced data professionals with tasks which they are hopelessly overqualified for. Such as, for example, doing ad-hoc SQL queries on CRM data: "You're the go-to-guy for all things data, and we need the results for the board meeting tomorrow." That's a quite humbling and frustrating…

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Added by Rafael Knuth on December 5, 2019 at 6:30am — No Comments

No Matter What You Call It, It’s all the Same Thing

Summary:  A little history lesson about all the different names by which the field of data science has been called, and why, whatever you call it, it’s all the same thing.

 

A little reminiscence, or for those of you who are only recently data scientists, a little history lesson. 

Our profession of…

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Added by William Vorhies on December 4, 2019 at 3:12pm — No Comments

Top 7 Data Science Use Cases in Administration

A successful business requires new approaches to data management in this age. Modern advances in data science area provide an efficient solutions for numerous use cases.

Data science embraces a broad spectrum of tasks in the sphere of…

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Added by Igor Bobriakov on November 27, 2019 at 6:27am — No Comments

The Logjam in AI/ML Platforms is About to Complicate Your Life

Summary:  Too many solutions.  We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production.  The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems.

 …

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Added by William Vorhies on November 25, 2019 at 9:44am — No Comments

Visually Explained: How Can Executives Make Sense of Machine Learning & Deep Learning?

Many executives struggle to make sense of machine learning (ML) and deep learning (DL). Having a pragmatic relationship with technology, executives need to know on a very fundamental level: "What problems do ML & DL try to solve?" A simple, high-level answer to that question is: "It's all about building systems that do certain things better than humans, with as little intervention by humans as possible." That being said, the simplest way to distinguish between ML and its branch DL…

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Added by Rafael Knuth on November 19, 2019 at 12:30pm — No Comments

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