KDnuggets

DECEMBER 19, 2022

RPA and IA have stunned the business world by availing impressive, intelligent automation capabilities for scales of businesses across industries, which we'll know in this blog.

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Netflix Tech

NOVEMBER 9, 2022

By Vadim Filanovsky and Harshad Sane In one of our previous blogposts, A Microscope on Microservices we outlined three broad domains of observability (or “levels of magnification,” as we referred to them)?—?Fleet-wide, Microservice and Instance. We described the tools and techniques we use to gain insight within each domain. There is, however, a class of problems that requires an even stronger level of magnification going deeper down the stack to introspect CPU microarchitecture.

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Confluent

DECEMBER 2, 2022

ksqlDB use case: see how apps can use ksqlDB to ingest, filter, enrich, aggregate, and query data directly with Kafka—no complex architectures or data stores needed.

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Cloudera

APRIL 22, 2022

This blog post was written by Dean Bubley , industry analyst, as a guest author for Cloudera. . Communications service providers (CSPs) are rethinking their approach to enterprise services in the era of advanced wireless connectivity and 5G networks, as well as with the continuing maturity of fibre and Software-Defined Wide Area Network (SD-WAN) portfolios. .

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Speaker: Dr. Greg Loughnane and Chris Alexiuk

Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. They're often developing using prompting, Retrieval Augmented Generation (RAG), and fine-tuning (up to and including Reinforcement Learning with Human Feedback (RLHF)), typically in that order. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are le

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KDnuggets

DECEMBER 30, 2022

Data science is ever-evolving, so mastering its foundational technical and soft skills will help you be successful in a career as a Data Scientist, as well as pursue advance concepts, such as deep learning and artificial intelligence.

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KDnuggets

DECEMBER 16, 2022

Many aspiring Data Scientists, especially when self-learning, fail to learn the necessary math foundations. These recommendations for learning approaches along with references to valuable resources can help you overcome a personal sense of not being "the math type" or belief that you "always failed in math.".

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KDnuggets

DECEMBER 9, 2022

As more people are entering the field of Data Science and more companies are hiring for data-centric roles, what type of jobs are currently in highest demand? There is so much data in the world, and it just keeps flooding in, it now looks like companies are targeting those who can engineer that data more than those who can only model the data.

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KDnuggets

DECEMBER 1, 2022

In this blog, I shared my story on getting 4 data science job offers including Airbnb, Lyft and Twitter after being laid off. Any data scientist who was laid off due to the pandemic or who is actively looking for a data science position can find something here to which they can relate.

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KDnuggets

NOVEMBER 23, 2022

There exist so many great computational tools available for Data Scientists to perform their work. However, mathematical skills are still essential in data science and machine learning because these tools will only be black-boxes for which you will not be able to ask core analytical questions without a theoretical foundation.

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Technology

KDnuggets

NOVEMBER 18, 2022

The Pandas library is core to any Data Science work in Python. This introduction will walk you through the basics of data manipulating, and features many of Pandas important features.

Data Science 145

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KDnuggets

NOVEMBER 16, 2022

While different ways to learn Data Science for the first time exist, the approach that works for you should be based on how you learn best. One powerful method is to evolve your learning from simple practice into complex foundations, as outlined in this learning path recommended by a physicist who turned into a Data Scientist.

Data Science 145

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KDnuggets

AUGUST 24, 2022

This blog post introduces seven techniques that are commonly applied in domains like intrusion detection or real-time bidding, because the datasets are often extremely imbalanced.

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KDnuggets

OCTOBER 24, 2022

Preprocessing data for machine learning models is a core general skill for any Data Scientist or Machine Learning Engineer. Follow this guide using Pandas and Scikit-learn to improve your techniques and make sure your data leads to the best possible outcome.

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KDnuggets

SEPTEMBER 1, 2022

Subset selection is one of the most frequently performed tasks while manipulating data. Pandas provides different ways to efficiently select subsets of data from your DataFrame.

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KDnuggets

OCTOBER 19, 2022

This post is a summary of 2 distinct frameworks for approaching machine learning tasks, followed by a distilled third. Do they differ considerably (or at all) from each other, or from other such processes available?

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Netflix Tech

DECEMBER 3, 2022

by Jasmine Omeke , Obi-Ike Nwoke , Olek Gorajek Intro This post is for all data practitioners, who are interested in learning about bootstrapping, standardization and automation of batch data pipelines at Netflix. You may remember Dataflow from the post we wrote last year titled Data pipeline asset management with Dataflow. That article was a deep dive into one of the more technical aspects of Dataflow and didn’t properly introduce this tool in the first place.

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KDnuggets

MAY 26, 2022

Read the best books on Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, MLOps, Robotics, IoT, AI Products Management, and Data Science for Executives.

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KDnuggets

OCTOBER 14, 2022

Leveraging sparse matrix representations for your data when appropriate can spare you memory storage. Have a look at the reasons why, see how to create sparse matrices in with Python, and compare the memory requirements for standard and sparse representations of the same data.

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KDnuggets

OCTOBER 11, 2022

A Data Science Enablement Team consists of people from various departments like marketing, sales, product development, etc. They are responsible for providing the necessary tools and resources to help the data scientists do their job more efficiently.

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KDnuggets

OCTOBER 7, 2022

There is no shortage of tools today that can help you through the steps of natural language processing, but if you want to get a handle on the basics this is a good place to start. Read about the ABCs of NLP, all the way from A to Z.

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KDnuggets

OCTOBER 12, 2022

Do you want to learn PyTorch for machine learning and deep learning? Check out this 24 hour long video course with accompanying notes and courseware for free. Did I mention it's free?

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KDnuggets

SEPTEMBER 20, 2022

When building and optimizing your classification model, measuring how accurately it predicts your expected outcome is crucial. However, this metric alone is never the entire story, as it can still offer misleading results. That's where these additional performance evaluations come into play to help tease out more meaning from your model.

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KDnuggets

OCTOBER 10, 2022

The only cheat you need for a job interview and data professional life. It includes SQL, web scraping, statistics, data wrangling and visualization, business intelligence, machine learning, deep learning, NLP, and super cheat sheets.

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KDnuggets

SEPTEMBER 5, 2022

People assume that NoSQL is a counterpart to SQL. Instead, it’s a different type of database designed for use-cases where SQL is not ideal. The differences between the two are many, although some are so crucial that they define both databases at their cores.

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KDnuggets

AUGUST 15, 2022

ETL during the process of producing effective machine learning algorithms is found at the base - the foundation. Let’s go through the steps on how ETL is important to machine learning.

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KDnuggets

FEBRUARY 14, 2022

Calculus is the key to fully understanding how neural networks function. Go beyond a surface understanding of this mathematics discipline with these free course materials from MIT.

Deep Learning 145

Deep Learning Machine Learning 145

KDnuggets

OCTOBER 13, 2022

In NLP we must find a way to represent our data (a series of texts) to our systems (e.g. a text classifier). As Yoav Goldberg asks, "How can we encode such categorical data in a way which is amenable for us by a statistical classifier?" Enter the word vector.

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KDnuggets

AUGUST 12, 2022

Increasing accuracy in your models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature scaling methods of standardization and normalization, and demonstrates when and how to apply each approach.

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KDnuggets

AUGUST 12, 2022

Do you feel overwhelmed by the sheer number of ideas that you could try while building a machine learning pipeline? You can not take the liberty of trying all possible ways to arrive at a solution - hence we discuss the importance of experiment design in data science projects.

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KDnuggets

JULY 22, 2022

The AIoT has the potential to transform industries and society, and it is already starting to have an impact. This article will explore the principles of AIoT, its benefits, and its current use.

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IT 145

KDnuggets

JUNE 28, 2022

Preparing for the SQL job interview can be overwhelming enough. You don’t need someone telling you that you need to know everything on top of that! Be smart and focus on preparing the SQL questions that appear most often at the job interview.

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SQL 145

KDnuggets

OCTOBER 4, 2022

Among the four big NoSQL database types, key-value stores are probably the most popular ones due to their simplicity and fast performance. Let’s further explore how key-value stores work and what are their practical uses.

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