How to use scikit-learn, pickle, Flask, Microsoft Azure and ipywidgets to fully deploy a Python machine learning algorithm into a live, production environment.
In this blog, we will discuss what makes Python so popular, its features, and why you should consider Python as a programming language for your startup.
This list of best data science companies aims to go beyond the usual and expected. Some great and perhaps underrated options to get a job as a data scientist.
AI/ML systems have a wide range of applications in a variety of industries and sectors, and this article highlights the top ways AI/ML will impact your small business in 2022.
Continue your learning journey in Reinforcement Learning with this second of two part tutorial that covers the foundations of the technique with examples and Python code.
Review this detailed tutorial with code and revisit the decades-long old problem using a democratized and interpretable AI framework of how precisely can we anticipate the future and understand its causal factors?
Feature selection methodologies go beyond filter, wrapper and embedded methods. In this article, I describe 3 alternative algorithms to select predictive features based on a feature importance score.
Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This post brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.
Start your learning journey in Reinforcement Learning with this first of two part tutorial that covers the foundations of the technique with examples and Python code.
If you have clear, well-defined objectives, it won’t be hard to identify the ETL technology that best meets your needs. Here are some of the best ETL tools you can use in your business.
Customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data are necessary to train and perfect the AI that impacts our lives and daily conveniences.
Many real-world datasets consist of records of events that occur at arbitrary and irregular intervals. These datasets then need to be processed into regular time series for further analysis. We will use the AI & Analytics Engine to illustrate how you can prepare your time-series data in just 1 step.
Also: 5 Key Skills Needed To Become a Great Data Scientist; A Full End-to-End Deployment of a Machine Learning Algorithm into a Live Production Environment; The 5 Characteristics of a Successful Data Scientist; Top Resources for Learning Statistics for Data Science
XGBoost is an open-source implementation of gradient boosting designed for speed and performance. However, even XGBoost training can sometimes be slow. This article will review the advantages and disadvantages of each approach as well as go over how to get started.
What AI and data analytics trends are taking the industry by storm this year? This comprehensive review highlights upcoming directions in AI to carefully watch and consider implementing in your personal work or organization.
Most viewed KDnuggets stories in 2021 focused on Data Scientists vs Data Engineers; How to become a Data Scientist; Increase income with Data Science; Stunning visualizations using python; and more.
Let’s take a closer look on Cloud ML market in 2021 in retrospective (with occasional drills into realities of 2020, too). Read this in-depth analysis.
Learn how one data scientist increased their full-time job salary 14 times in 14 years of a career, with highlights on experiencing an IPO, RSUs, start-ups and working at FAANG companies.
Flexibility versus maintainability—every decision you make in software engineering involves balancing tradeoffs. Software Mistakes and Tradeoffs is available in early access from its publisher Manning. Pre-order now and start reading immediately as part of the Manning Early Access Program (MEAP).
We have solicited insights from experts at industry-leading companies, asking: "What were the main AI, Data Science, Machine Learning Developments in 2021 and what key trends do you expect in 2022?" Read their opinions here.
Thinking about taking your data science expertise to a new level of creating a start-up company? These tips -- learned from experience -- can help you forge an early path toward success.
Also: 5 Practical Data Science Projects That Will Help You Solve Real Business Problems for 2022; How to Get Certified as a Data Scientist; A $9B AI Failure, Examined; AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2021 and Key Trends for 2022
So much of data science and machine learning is founded on having clean and well-understood data sources that it is unsurprising that the data labeling market is growing faster than ever. Here, we highlight many of the top players in this industry and the techniques they use to help you consider which might make a good partner for your needs.
Our panel of leading experts reviews 2021 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.
Machine learning techniques continue to evolve with increased efficiency for recognition problems. But, they still lack the critical element of intelligence, so we remain a long way from attaining AGI.
Take the first step towards your machine learning engineering career and explore the UC San Diego Extension Machine Learning Engineering Bootcamp today. Those with prior software engineering or data science experience are encouraged to apply.
2021 has almost come and gone. We saw some standout advancements in AI, Analytics, Machine Learning, Data Science, Deep Learning Research this past year, and the future, starting with 2022, looks bright. As per KDnuggets tradition, our collection of experts have contributed their insights on the matter. Read on to find out more.
How do you put together a solid data science team when it comes to developing data-driven products? A variety of roles are available to consider, so which ones do you need and which are most crucial?
Hey, data scientists! Here’s how to bend your CFO’s ear, equip your company with high-quality analysis, and boost your value and career in the process.
PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. See how to use it for binary classification.
What happened at Zillow? An important real-world lesson in... just because you have a cool AI tool, doesn't mean that alone becomes your business model.
A lot of missing values in the dataset can affect the quality of prediction in the long run. Several methods can be used to fill the missing values and Datawig is one of the most efficient ones.
Also: How to Get Certified as a Data Scientist; 5 Practical Data Science Projects That Will Help You Solve Real Business Problems for 2022; Most Common SQL Mistakes on Data Science Interviews; 19 Data Science Project Ideas for Beginners
This article explores Meta-Learning for Key phrase Extraction, which delves into the how and why of KeyPhrase Extraction (KPE) - extracting phrases/groups of words from a document to best capture and represent its content. The article outline what needs to be done to build a keyphrase extractor that performs well not only on in-domain data, but also in a zero-shot scenario where keyphrases need to be extracted from data that have a different distribution (either a different domain or a different type of documents).
If you are early in your journey to becoming a Data Scientist, an interesting option is to earn certification by DataCamp, and this guide offers tips that will help beginners complete the challenges.
PyCaret’s new time series module is now available in beta. Staying true to the simplicity of PyCaret, it is consistent with the existing API and comes with a lot of functionalities.
UC San Diego Extension’s certificate in Data Mining is a five course, 15-unit program, that can be completed in as little as one year. Upon completion, you will be equipped with the necessary skills to make data-driven decisions in any industry. Find out more today.
ELT helps to streamline the process of modern data warehousing and managing a business’ data. In this post, we’ll discuss some of the best ELT tools to help you clean and transfer important data to your data warehouse.
This curated list of data science projects offers real-life problems that will help you master skills to demonstration that you are technically sound and know how to conduct data science projects that add business value.
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What Makes Python An Ideal Programming Language For Startups, by Nikita Bajaj - Dec 31, 2021.
In this blog, we will discuss what makes Python so popular, its features, and why you should consider Python as a programming language for your startup.
Write Clean Python Code Using Pipes, by Khuyen Tran - Dec 15, 2021.
A short and clean approach to processing iterables.
What Makes Python An Ideal Programming Language For Startups
3 Tools to Track and Visualize the Execution of Your Python Code
Write Clean Python Code Using Pipes