Are you looking to continue your learning of natural language processing? This small collection of 3 free top notch courses will allow you to do just that.
Data Vault is a modern data modelling approach for capturing (historical) data in a structurally auditable and tractable way. While very helpful for data engineers, the Data Vault also enables Data Science in practice.
ML models interpretability can be seen as “the ability to explain or to present in understandable terms to a human.” Read this article and learn to go beyond the black box of AI, where algorithms make predictions, toward the underlying explanation remains unknown and untraceable.
Follow along with the author's most recent installment in their quest to solve Sudoku puzzles, this time with the help of a decision engine to solve candidate pairs.
The role of the Data Scientist continues to offer many great opportunities as a career. However, the 'sexiest job of the 21st century' has lost some of its appeal because of unrealized expectations and how organizations might leverage this type of work. Having a better understanding of how data science typically plays out in the business world can help you achieve the success you want.
Great News for KDnuggets subscribers! You now have access to the WorldData.AI Partners Plan at no cost, including access to some of the premium datasets only available to enterprise members. Connect your data to many of 3.5 Billion WorldData datasets and improve your Data Science and Machine Learning models! Subscribe to KDnuggets to get access.
Also: Top 10 Python Libraries Data Scientists should know in 2021; More Data Science Cheatsheets; The Portfolio Guide for Data Science Beginners; The Best Machine Learning Frameworks & Extensions for Scikit-learn
Learning all you need to learn about data science is only part of the adventure. Landing that first job is another. While it might take a while to get your foot into the door, there are several key efforts you can do to shorten this time as much as possible.
Building a machine learning model is great, but to provide real business value, it must be made useful and maintained to remain useful over time. Machine Learning Operations (MLOps), overviewed here, is a rapidly growing space that encompasses everything required to deploy a machine learning model into production, and is a crucial aspect to delivering this sought after value.
Ramapo College’s Master of Science in Data Science program will teach you to collect, synthesize, and analyze big data, become skilled in programming languages like R and Python, and leverage advanced tools to meet the demands of modern business and science.
If you are the "data person" for your organization, then providing meaningful results to stakeholder data requests can sometimes feel like shots in the dark. However, you can make sure your data analysis is actionable by asking one magic question before getting started.
If you are looking to expand or transition your current professional career that is buried in spreadsheet analysis into one powered by data science, then you are in for an exciting but complex journey with much to explore and master. To begin your adventure, following this complete road map to guide you from a gnome in the forest of spreadsheets to an AI wizard known far and wide throughout the kingdom.
PixelLib is a library created for easy integration of image and video segmentation in real life applications. Learn to use PixelLib to extract objects In images and videos with minimal code.
Learn from experts and hear real-world use cases about how you and your organization can optimize data to enable innovation through visualization. Register now.
So many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development. While the list may seem overwhelming, there are certain libraries you should focus your time on, as they are some of the most commonly used today.
New advances in natural language processing (NLP) based on deep learning and transfer learning have made a whole set of software use cases in healthcare viable. The Healthcare NLP Summit is a free online conference on April 6th and 7th, bringing together 30+ technical sessions from across the community that works to apply these advances in the real world.
With recent growth in data science, now is the best time to get into freelancing. The following steps will help you get started with finding clients or help you improve your current strategy.
In this second part review of the many options available for choosing metrics to evaluate machine learning models, learn how to select the most appropriate metric for your analysis of regression models.
These are the top 15 YouTube channels for machine learning as determined by our stated criteria, along with some additional data on the channels to help you decide if they may have some content useful for you.
Whether you are an aspiring or seasoned Data Scientist, establishing a clear and well-designed online portfolio presence will help you stand out in the industry, and provide potential employers a powerful understanding of your work and capabilities. These tips will help you brainstorm and launch your first data science portfolio.
Also: How To Overcome The Fear of Math and Learn Math For Data Science; Know your data much faster with the new Sweetviz Python library; Introducing dbt, the ETL and ELT Disrupter; Must Know for Data Scientists and Data Analysts: Causal Design Patterns
Can AI algorithms help us find love? Can they go a step further and replace a human being as a partner in a relationship? Here, we analyze how far technology has come in helping us meet "our" people, find love, and feel less lonely.
At Wrangle Summit 2021, Apr 7-9, you’ll get access to all the best people, ideas, and technology in data engineering, all in one place. Learn how to refine raw data and engineer unique data products, and gain insights from your data that can catalyze real, measurable business success.
It's time again to look at some data science cheatsheets. Here you can find a short selection of such resources which can cater to different existing levels of knowledge and breadth of topics of interest.
Understanding your data first is a key step before going too far into any data science project. But, you can't fully understand your data until you know the right questions to ask of it.
As AI continues to boom, improved technologies and processes for data labeling and annotation are on the rise. iMerit, a leader in providing high-quality data for Machine Learning and AI, shares the latest trends in annotation workflow and tooling.
Moving and processing data is happening 24/7/365 world-wide at massive scales that only get larger by the hour. Tools exist to introduce efficiencies in how data can be extracted from sources, transformed through calculations, and loaded into target data repositories. However, on their own, these tools can introduce some restrictions in the processing, especially for the needs of data analytics and data science.
You have many options when choosing metrics for evaluating your machine learning models. Select the right one for your situation with this guide that considers metrics for classification models.
In this article, we will understand the difference between data verification and data validation, two terms which are often used interchangeably when we talk about data quality. However, these two terms are distinct.
See the progress the author has made since last time, after setting themselves the challenge of solving Sudoku puzzles using an optimized inference engine, along with a few other advanced features of FICO® Blaze Advisor®.
The latest KDnuggets survey is looking to determine the job satisfaction levels of the data community. Take a few moments to contribute your answer and help paint a picture of the current situation.
So much progress in AI and machine learning happened in 2020, especially in the areas of AI-generating creativity and low-to-no-code frameworks. Check out these trending and popular machine learning projects released last year, and let them inspire your work throughout 2021.
Also: Know your data much faster with the new Sweetviz Python library; Must Know for Data Scientists and Data Analysts: Causal Design Patterns; Are You Still Using Pandas to Process Big Data in 2021? Here are two better options; 3 Mathematical Laws Data Scientists Need To Know
AI Accelerator Festival: Hardware Acceleration for AI at the Edge . The world's only end-user led event dedicated to accelerating industries by harnessing the power of AI. March 16-19, 2021.
The Kedro team and Astronomer have released Kedro-Airflow 0.4.0 to help you develop modular, maintainable & reproducible code with orchestration superpowers!
Industry is a prime setting for observational causal inference, but many companies are blind to causal measurement beyond A/B tests. This formula-free primer illustrates analysis design patterns for measuring causal effects from observational data.
One of the latest exploratory data analysis libraries is a new open-source Python library called Sweetviz, for just the purposes of finding out data types, missing information, distribution of values, correlations, etc. Find out more about the library and how to use it here.
HSE’s Master of Data Science is the first fully English-taught online data science Master’s from a Russian university. The degree is designed for students with or without prior coding experience. The final application deadline is June 17th. Learn more about HSE’s Master of Data Science now.
CLIP is a bridge between computer vision and natural language processing. I'm here to break CLIP down for you in an accessible and fun read! In this post, I'll cover what CLIP is, how CLIP works, and why CLIP is cool.
The foundations of Data Science and machine learning algorithms are in mathematics and statistics. To be the best Data Scientists you can be, your skills in statistical understanding should be well-established. The more you appreciate statistics, the better you will understand how machine learning performs its apparent magic.
Once you deploy a machine learning model in production, you need to make sure it performs. In the article, we suggest how to monitor your models and open-source tools to use.
The Modin library has the ability to scale your pandas workflows by changing one line of code and integration with the Python ecosystem and Ray clusters. This tutorial goes over how to get started with Modin and how it can speed up your pandas workflows.
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."
The demand for analytics skills and talent has never been higher. As the workforce continues to evolve, so do the technology and skillsets needed. Learn how the Millennium Bank partnered with SAS to customize a development and training program that improved skills, knowledge, and retention.
Out of all the NoSQL database types, document-stores are considered the most sophisticated ones. They store data in a JSON format which as opposed to a classic rows and columns structure.
Diving into building your first machine learning model will be an adventure -- one in which you will learn many important lessons the hard way. However, by following these four tips, your first and subsequent models will be put on a path toward excellence.
Some exceptional female researchers and engineers are working on projects to make the world a better place with the help of AI, data science, and machine learning.
Also: Are You Still Using Pandas to Process Big Data in 2021? Here are two better options; 3 Mathematical Laws Data Scientists Need To Know; Google’s Model Search is a New Open Source Framework that Uses Neural Networks to Build Neural Networks; Machine Learning Systems Design: A Free Stanford Course
If your scikit-learn models are taking a bit of time to train, then there are several techniques you can use to make the processing more efficient. From optimizing your model configuration to leveraging libraries to speed up training through parallelization, you can build the best scikit-learn model possible in the least amount of time.
PyCaret, a low code Python ML library, offers several ways to tune the hyper-parameters of a created model. In this post, I'd like to show how Ray Tune is integrated with PyCaret, and how easy it is to leverage its algorithms and distributed computing to achieve results superior to default random search method.
Penn MS of Computer and Information Technology is an online masters degree tailored for non-CS majors, empowering then to succeed in computing and technology fields. Apply by May 1.
The increasing computation time and costs of training natural language models (NLP) highlight the importance of inventing computationally efficient models that retain top modeling power with reduced or accelerated computation. A single experiment training a top-performing language model on the 'Billion Word' benchmark would take 384 GPU days and as much as $36,000 using AWS on-demand instances.
Do you love pandas, but don't love it when you reach the limits of your memory or compute resources? Dask provides you with the option to use the pandas API with distributed data and computing. Learn how it works, how to use it, and why it’s worth the switch when you need it most.
A data engineer is a fast-growing profession with amazing challenges and rewards. Which skills do you need to become a data engineer? In this post, we’ll take a look at both hard and soft skills.
Writing Python code that works for your data science project and performs the task you expect is one thing. Ensuring your code is readable by others (including your future self), reproducible, and efficient are entirely different challenges that can be addressed by minimizing common bad practices in your development.
Facebook recently introduced and open-sourced their new framework for self-supervised learning of representations from raw audio data called Wav2Vec 2.0. Learn more about it and how to use it here.
Machine learning and data science are founded on important mathematics in statistics and probability. A few interesting mathematical laws you should understand will especially help you perform better as a Data Scientist, including Benford's Law, the Law of Large Numbers, and Zipf's Law.
Also: Powerful Exploratory Data Analysis in just two lines of code; Machine Learning Systems Design: A Free Stanford Course; Telling a Great Data Story: A Visualization Decision Tree
When its time to handle a lot of data -- so much that you are in the realm of Big Data -- what tools can you use to wrangle the data, especially in a notebook environment? Pandas doesn’t handle really Big Data very well, but two other libraries do. So, which one is better and faster?
Have a look at the top 15 YouTube channels for data science by number of subscribers, along with some additional data on the channels to help you decide if they may have some content useful for you.
Feb
The Best Machine Learning Frameworks & Extensions for Scikit-learn, by Derrick Mwiti - Mar 22, 2021.
Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators.
Top 10 Python Libraries Data Scientists should know in 2021
The Best Machine Learning Frameworks & Extensions for Scikit-learn
More Data Science Cheatsheets
Know your data much faster with the new Sweetviz Python library