4 Steps to ensure your AI/Machine Learning system survives COVID-19; State of the Machine Learning and AI Industry; A Key Missing Part of the Machine Learning Stack; 5 Papers on CNNs Every Data Scientist Should Read

If you find yourself quarantined and looking for free learning materials in the way of books and courses to sharpen your data science and machine learning skills, this collection of articles I have previously written curating such things is for you.

This hands-on book bridges the gap between theory and practice, showing you the math of deep learning algorithms side by side with an implementation in PyTorch. You can save 40% off Math and Architectures of Deep Learning until May 13! Just enter the code nlkdarch40 at checkout when you buy from manning.com.

This post will cover how testing is done for the coronavirus, why it's important in battling the pandemic, and how deep learning tools for medical imaging can help us improve the quality of COVID-19 testing.

Biostatisticians use statistical techniques that your current everyday data scientists have probably never heard of. This is a great example where lack of domain knowledge exposes you as someone that does not know what they are doing and are merely hopping on a trend.

If you are already applying your Data Science skills or getting ready to contribute to analyzing COVID-19 data, then be sure to take sufficient time to appreciate the context of the numbers to focus on what's most important as we collaborate on this global battle.

Also: Math for Programmers!; If #Programming languages had honest slogans #humor; 5 Papers on CNNs Every Data Scientist Should Read; Why Understanding CVEs Is Critical for Data Scientists

Data related positions are considered the hottest in the job market during the last couple of years. While everyone wants to join the party and enter this fascinating field, it is essential to first get an understanding. In this quick guide, I’ll do my best to dispel the confusion by crystalizing the essence of the different positions.

How far we are from achieving Artificial General Intelligence? We answer this through the study of three limitations of current machine learning.

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.

An introduction to the DBSCAN algorithm and its Implementation in Python.

Check out this repository of more than 100 freely-accessible NLP notebooks, curated from around the internet, and ready to launch in Colab with a single click.

How can you keep your focus and drive during a global crisis? Take on a 90-day learning challenge for data science and check out this list of books and courses to follow.

The new framework uses contrastive learning to improve image analysis in unlabeled datasets.

Also: Should Data Scientists Model COVID19 and other Biological Events; 5 Papers on CNNs Every Data Scientist Should Read; 24 Best (and Free) Books To Understand Machine Learning; Mathematics for Machine Learning: The Free eBook; Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World

Check out this freely available book, All of Statistics: A Concise Course in Statistical Inference, and learn the probability and statistics needed for success in data science.

Learn how to develop a LSTM neural network with PyTorch on trading data to predict future prices by mimicking actual values of the time series data.

Check out these 10 books that can help data scientists and aspiring data scientists learn machine learning today.

The COVID-19 pandemic has affected everything, and building predictions during this time is difficult. Data science teams need to update their models to prepare for the recovery, and know how to properly train 2020 data models to learn from the coronavirus anomaly.

The book Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing by Ron Kohavi (Microsoft, Airbnb), Diane Tang (Google) and Ya Xu (LinkedIn) is available for purchase, with the authors proceeds from the book being donated to charity.

Also: Should Data Scientists Model COVID19 and other Biological Events; Learning during a crisis (Data Science 90-day learning challenge); Data Transformation: Standardization vs Normalization; DBSCAN Clustering Algorithm in Machine Learning; Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World

So in this article, we will interpret, analyze the COVID-19 DNA sequence data and try to get as many insights regarding the proteins that made it up. Later will compare COVID-19 DNA with MERS and SARS and we’ll understand the relationship among them.

Interactive visualizations are an effective method for understanding the COVID-19 pandemic. This article presents a repository filled with just such insightful interactions.

A growing consensus of researchers contend that new algorithms are needed to transform narrow AI to AGI. Brain Simulator II is free software for new algorithm development targeted at AGI that you can experiment with and participate in its development.

Also: A Concise Course in Statistical Inference: The Free eBook; ML Ops: Machine Learning as an Engineering Discipline; Learning during a crisis (#DataScience 90-day learning challenge) ; Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition

Check out these 5 cool Python libraries that the author has come across during an NLP project, and which have made their life easier.

GIS has mostly been behind more popular buzzwords like machine learning and deep learning. GIS has always been around us in the background being used in government, business, medicine, real estate, transport, manufacturing etc.

You walk down one aisle of the grocery store to get your favorite cereal. On the dairy aisle, someone sick from COVID-19 coughs. Did your decision to grab your cereal before your milk possibly keep you healthy? How can these unpredictable, near-random choices be included in complex models?

Here is an overview of another great natural language processing resource, this time from Microsoft, which demonstrates best practices and implementation guidelines for a variety of tasks and scenarios.

This post explains using a pre-trained GAN to generate human faces, and discusses the most common generative pitfalls associated with doing so.

This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.

AI tools and services are expanding at a rapid clip, and keeping a handle on this evolving ecosystem is crucial for the success of your AI projects. This framework will help you build your technical stack to deploy AI projects faster and at scale.

Also: Coronavirus COVID-19 Genome Analysis using Biopython; LSTM for time series prediction; A Concise Course in Statistical Inference: The Free eBook; Exploring the Impact of Geographic Information Systems

"Deep Learning" is the quintessential book for understanding deep learning theory, and you can still read it freely online.

Optimus, FQ-GAN and Prevalent bring new ideas to apply generative models at large scale.

As the global economy dwindles, tech companies are hiring en masse. Now is the time to get yourself noticed as a Data Scientist and try to land your dream job.

At present, the data scientist is one of the most sought after professions. That’s one of the main reasons why we decided to cover the latest data visualization tools that every data scientist can use to make their work more effective.

This post will unravel a practical example to illustrate and motivate the intuition behind each step of the spectral clustering algorithm.

So, you are interested in machine learning? Here is your complete learning path to start your career in the field.

This online course is available – for free – to anyone interested in building practical skills in using data to solve problems better.

5 cool Python libraries for Data Science; NLP Recipes: Best Practices and Examples; Deep Learning: The Free eBook; Demystifying the AI Infrastructure Stack; and more.

Check out this curated collection of coronavirus-related projections, dashboards, visualizations, and data that we have encountered on the internet.

Train a "blackbox" GBM model on a real dataset and make it explainable with SHAP.

Understanding the types of statistical bias that pop up in popular media and reporting is especially important during this pandemic where the data -- and our global response to the data -- directly impact peoples' lives.

What are Some 'Advanced ' #AI and #MachineLearning Online Courses?; 24 Best (and Free) Books To Understand Machine Learning; Top 5 must-have #DataScience skills for 2020

Check out this comprehensive guide to model optimization techniques.

Why is Gradient Descent so important in Machine Learning? Learn more about this iterative optimization algorithm and how it is used to minimize a loss function.

With time-series decomposition, we were able to infer that the consumers were waiting for the highest sale of the year rather than buying up-front.

I realized that from a corporate perspective, “fortune teller” was not entirely off from the role of a “data scientist”.

As can be common in many technical fields, the landscape of specialized roles is evolving quickly. With more people learning at least a little machine learning, this could eventually become a common skill set for every software engineer.