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Improving model performance through human participation- Apr 23, 2021.
Certain industries, such as medicine and finance, are sensitive to false positives. Using human input in the model inference loop can increase the final precision and recall. Here, we describe how to incorporate human feedback at inference time, so that Machines + Humans = Higher Precision & Recall.
Data Science Books You Should Start Reading in 2021- Apr 23, 2021.
Check out this curated list of the best data science books for any level.
What is Adversarial Neural Cryptography?- Apr 22, 2021.
The novel approach combines GANs and cryptography in a single, powerful security method.
How to ace A/B Testing Data Science Interviews- Apr 22, 2021.
Understanding the process of A/B testing and knowing how to discuss this approach during data science job interviews can give you a leg up over other candidates. This mock interview provides a step-by-step guide through how to demonstrate your mastery of the key concepts and logical considerations.
Top 10 Must-Know Machine Learning Algorithms for Data Scientists – Part 1- Apr 22, 2021.
New to data science? Interested in the must-know machine learning algorithms in the field? Check out the first part of our list and introductory descriptions of the top 10 algorithms for data scientists to know.
Production-Ready Machine Learning NLP API with FastAPI and spaCy- Apr 21, 2021.
Learn how to implement an API based on FastAPI and spaCy for Named Entity Recognition (NER), and see why the author used FastAPI to quickly build a fast and robust machine learning API.
10 Must-Know Statistical Concepts for Data Scientists- Apr 21, 2021.
Statistics is a building block of data science. If you are working or plan to work in this field, then you will encounter the fundamental concepts reviewed for you here. Certainly, there is much more to learn in statistics, but once you understand these basics, then you can steadily build your way up to advanced topics.
Time Series Forecasting with PyCaret Regression Module- Apr 21, 2021.
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 PyCaret's Regression Module for Time Series Forecasting.
Data Analysis Using Tableau- Apr 20, 2021.
Read this overview of using Tableau for sale data analysis, and see how visualization can help tell the business story.
Data Science 101: Normalization, Standardization, and Regularization- Apr 20, 2021.
Normalization, standardization, and regularization all sound similar. However, each plays a unique role in your data preparation and model building process, so you must know when and how to use these important procedures.
Want To Get Good At Time Series Forecasting? Predict The Weather- Apr 20, 2021.
This article is designed to help the reader understand the components of a time series.
How to organize your data science project in 2021- Apr 19, 2021.
Maintaining proper organization of all your data science projects will increase your productivity, minimize errors, and increase your development efficiency. This tutorial will guide you through a framework on how to keep everything in order on your local machine and in the cloud.
Free From Stanford: Machine Learning with Graphs- Apr 19, 2021.
Check out the freely-available Stanford course Machine Learning with Graphs, taught by Jure Leskovec, and see how a world renowned researcher teaches their topic of expertise. Accessible materials include slides, videos, and more.
What makes a song popular? Analyzing Top Songs on Spotify- Apr 16, 2021.
With so many great (and not-so-great) songs out there, it can be hard to find those that match your musical preferences. Follow along this ML model building project to explore the extensive song data available on Spotify and design a recommendation engine that could help you discover your next favorite artist!
Essential Math for Data Science: Linear Transformation with Matrices- Apr 16, 2021.
You’ll start seeing matrices, not only as operations on numbers, but also as a way to transform vector spaces. This conception will give you the foundations needed to understand more complex linear algebra concepts like matrix decomposition.
Top 3 Statistical Paradoxes in Data Science- Apr 15, 2021.
Observation bias and sub-group differences generate statistical paradoxes.
ETL in the Cloud: Transforming Big Data Analytics with Data Warehouse Automation- Apr 15, 2021.
Today, organizations are increasingly implementing cloud ETL tools to handle large data sets. With data sets becoming larger by the day, unified ETL tools have become crucial for data integration needs of enterprises.
Is Your Model Overtained?- Apr 14, 2021.
WeightWatcher is based on theoretical research (done injoint with UC Berkeley) into Why Deep Learning Works, based on our Theory of Heavy Tailed Self-Regularization (HT-SR). It uses ideas from Random Matrix Theory (RMT), Statistical Mechanics, and Strongly Correlated Systems.
Continuous Training for Machine Learning – a Framework for a Successful Strategy- Apr 14, 2021.
A basic appreciation by anyone who builds machine learning models is that the model is not useful without useful data. This doesn't change after a model is deployed to production. Effectively monitoring and retraining models with updated data is key to maintaining valuable ML solutions, and can be accomplished with effective approaches to production-level continuous training that is guided by the data.
Automated Anomaly Detection Using PyCaret- Apr 13, 2021.
Learn to automate anomaly detection using the open source machine learning library PyCaret.
10 Real-Life Applications of Reinforcement Learning- Apr 12, 2021.
In this article, we’ll look at some of the real-world applications of reinforcement learning.
Zero-Shot Learning: Can you classify an object without seeing it before?- Apr 12, 2021.
Developing machine learning models that can perform predictive functions on data it has never seen before has become an important research area called zero-shot learning. We tend to be pretty great at recognizing things in the world we never saw before, and zero-shot learning offers a possible path toward mimicking this powerful human capability.
How to Apply Transformers to Any Length of Text- Apr 12, 2021.
Read on to find how to restore the power of NLP for long sequences.
Interpretable Machine Learning: The Free eBook- Apr 9, 2021.
Interested in learning more about interpretability in machine learning? Check out this free eBook to learn about the basics, simple interpretable models, and strategies for interpreting more complex black box models.
Deep Learning Recommendation Models (DLRM): A Deep Dive- Apr 9, 2021.
The currency in the 21st century is no longer just data. It's the attention of people. This deep dive article presents the architecture and deployment issues experienced with the deep learning recommendation model, DLRM, which was open-sourced by Facebook in March 2019.
NoSQL Explained: Understanding Key-Value Databases- Apr 8, 2021.
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.
A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 2- Apr 8, 2021.
In this second article in this series, we’ll continue to take an interview-driven approach by linking some of the most commonly asked interview questions to different components of A/B testing, including selecting ideas for testing, designing A/B tests, evaluating test results, and making ship or no ship decisions.
E-commerce Data Analysis for Sales Strategy Using Python- Apr 7, 2021.
Check out this informative and concise case study applying data analysis using Python to a well-defined e-commerce scenario.
Microsoft Research Trains Neural Networks to Understand What They Read- Apr 7, 2021.
The new models make inroads in a new areas of deep learning known as machine reading comprehension.
Working With Time Series Using SQL- Apr 6, 2021.
This article is an overview of using SQL to manipulate time series data.
How to Dockerize Any Machine Learning Application- Apr 6, 2021.
How can you -- an awesome Data Scientist -- also be known as an awesome software engineer? Docker. And these 3 simple steps to use it for your solutions over and over again.
Automated Text Classification with EvalML- Apr 6, 2021.
Learn how EvalML leverages Woodwork, Featuretools and the nlp-primitives library to process text data and create a machine learning model that can detect spam text messages.
The Best Machine Learning Frameworks & Extensions for TensorFlow- Apr 5, 2021.
Check out this curated list of useful frameworks and extensions for TensorFlow.
How to deploy Machine Learning/Deep Learning models to the web- Apr 5, 2021.
The full value of your deep learning models comes from enabling others to use them. Learn how to deploy your model to the web and access it as a REST API, and begin to share the power of your machine learning development with the world.
Awesome Tricks And Best Practices From Kaggle- Apr 5, 2021.
Easily learn what is only learned by hours of search and exploration.
Shapash: Making Machine Learning Models Understandable- Apr 2, 2021.
Establishing an expectation for trust around AI technologies may soon become one of the most important skills provided by Data Scientists. Significant research investments are underway in this area, and new tools are being developed, such as Shapash, an open-source Python library that helps Data Scientists make machine learning models more transparent and understandable.
What’s ETL?- Apr 2, 2021.
Discover what ETL is, and see in what ways it’s critical for data science.
Easy AutoML in Python- Apr 1, 2021.
We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.
A/B Testing: 7 Common Questions and Answers in Data Science Interviews, Part 1- Apr 1, 2021.
In this article, we’ll take an interview-driven approach by linking some of the most commonly asked interview questions to different components of A/B testing, including selecting ideas for testing, designing A/B tests, evaluating test results, and making ship or no ship decisions.

Exploratory Data Analysis (EDA) in Python is the first step in your data analysis process developed by “John Tukey” in the 1970s. In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. Datatable overcomes the limitations of pandas and speeds up the process of EDA(exploratory data analysis). Datatable has been built by H20.ai, one of the leading AI ML companies in the world. Datatable is pretty similar to pandas and R data.table libraries. Datatable has proper documentation. Works with Python version 3.6+. Advantages of Datatable. Of and in ' a to was is ) ( for as on by he with 's that at from his it an were are which this also be has or: had first one their its new after but who not they have –; her she ' two been other when there all% during into school time may years more most only over city some world would where later up such used many can state about national out known university united then made. PandasCheatSheet.pdf: 판다스 기본문법 및 기능에 대한 cheat sheet; 쥬피터에서 마크다운 이용하기: jupyter markdown 문법이 정리; jupytercoding & 2: jupyter를 설치 후 처음 해보는 python 코딩; pandasbasicgrammer: pandas 기본 문법 설명 jupyter; pandasEDA1, 2: pandas를 이용한 EDA 예제1. Naked Cutie - Sindy Black 51469. We have provided this list to facilitate information about local groups and meetings, please note that only those who are legally adults are permitted to attend sa meetings, orgphone 1 615-370-6062toll-free usa canada 866-424-8777fax 1 615-370-0882 1997-2021 sexaholics anonymous inc.

March:

Top 10 Python Libraries Data Scientists should know in 2021, by Terence Shin
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.
The Best Machine Learning Frameworks & Extensions for Scikit-learn, by Derrick Mwiti
Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators.
The Portfolio Guide for Data Science Beginners, by Navid Mashinchi
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.
More Data Science Cheatsheets, by Matthew Mayo
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.
10 Amazing Machine Learning Projects of 2020, by Anupam Chugh
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.
Must Know for Data Scientists and Data Analysts: Causal Design Patterns, by Emily Riederer
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.
Know your data much faster with the new Sweetviz Python library, by Francois Bertrand
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.
A Machine Learning Model Monitoring Checklist: 7 Things to Track, by Emeli Dral & Elena Samuylova
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.
How To Overcome The Fear of Math and Learn Math For Data Science, by Arnuld On Data
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.'
3 Mathematical Laws Data Scientists Need To Know, by Cornellius Yudha Wijaya
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.
Google’s Model Search is a New Open Source Framework that Uses Neural Networks to Build Neural Networks, by Jesus Rodriguez
The new framework brings state-of-the-art neural architecture search methods to TensorFlow.
Top YouTube Channels for Data Science, by Matthew Mayo
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.