Data Science, Editorial, News
Know what employers are expecting for a data scientist role in 2021. Data analysis from over 3000+ data scientist job postings — extracted from several career portals using web scraping
Author(s): Sujan Shirol, Roberto Iriondo
Disclaimer: This article is only for educational purposes. We do not encourage anyone to scrape websites, especially those web properties that may have terms and conditions against such actions.
Are you preparing for a data science job interview in 2021? We have analyzed the hiring trends from more than 3000+ data science job postings across several online career portals. Hopefully, these insights will help you get ready for an interview by analyzing the expectations of employers and the overall market demand.
Data science and machine learning opportunities in the US are getting better every year…
Statistics, Editorial, Programming
This article provides a sample of our book: “Descriptive statistics for data-driven decision making with Python.”
Author(s): Pratik Shukla, Roberto Iriondo
Last updated on July 19, 2021
Data science and machine learning are scientific disciplines that are ruled by programming and mathematics. Nowadays, most corporations globally generate immense amounts of data that can be further analyzed and visualized by experts to understand trends and forecast predictions. For instance, we can only perform accurate data visualization if our data is clear and understandable.
However, organizations’ data is (frequently) too messy to tinker with — therefore, finding structures and important patterns in data is a crucial task for data science. Statistics provides the methods and tools to find…
Data Science, Machine Learning, Tutorial
Diving into genuine state-of-the-art automation of the data labeling workflow on large unstructured datasets
Author(s): Roberto Iriondo
Data labeling is an essential part of the machine learning workflow, particularly data preprocessing, where both input and output data are labeled for classification to present a learning base for planned data processing.
We use data labeling to identify raw data, such as objects in images, videos, text, and so on. It works by affixing one or more significant and informative labels to produce context so that a model can learn from it [2]. …
NEWS, NEWSLETTER
The Artificial Intelligence (AI) Newsletter by Towards AI
If you have trouble reading this email, see it on a web browser.
Hey everyone, I hope you are well. In this issue, we dive into the definitive guide to starting in machine learning, how realistic it is to automate an end-to-end AI pipeline, is Codex really going to replace programmers, what about modelitis, is it coming to an end, and last but not least why AI will never replace managers.
This issue is brought to you thanks to expert.ai:
Learn how to apply natural language to one of the most challenging and real-world problems. Check out the free-to-access whitepaper…
NEWS, NEWSLETTER
AI news, research, and updates, a limited supply and free to access whitepaper tackling real-world challenges in natural language, and our monthly editorial picks!
If you have trouble reading this email, see it on a web browser.
Natural language understanding (NLU) refers to a set of AI techniques that are used to understand and extract information from human language, and NLU is all about making sense of it. Human language has a lot going on in it, and being able to understand it is not an easy task. If you are interested in diving into NLU, check out this limited supply and free to access whitepaper by our friends at Expert AI tackling some of the most crucial challenges that NLU is facing nowadays:
NEWS, NEWSLETTER
AI news, research, updates, an exciting and free-to-attend NLP summit with top speakers, and our monthly editorial picks!
If you have trouble reading this email, see it on a web browser.
Applied natural language processing (Applied NLP) is becoming a very hot topic in the machine learning community, and for good reasons. It has emerged as an essential subdiscipline in the field of artificial intelligence (AI), and the main reason can be summed up in one sentence: We are drowning in unstructured data — texts, documents, articles, blog posts, emails, and more — and most of them are written in natural language. NLP’s objective is to build computer systems that can understand and derive meaning from human language…
Artificial Intelligence, Editorial, News
These are the best AI communities that every AI enthusiast should know about
Once you start exploring the field of artificial intelligence and machine learning, you will realize that there is a lot to learn. There is a variety of online groups and communities where experts share their insights on AI algorithms, problems in AI, ML, computer vision, and so on. These discussions lead to developing new ideas and solutions, which will eventually help the AI community grow more robust. This article will look at some actionable tips that help identify good online groups and communities.
If you are an enthusiast of artificial intelligence, then it is very likely that you might be…
Events
Join an exciting lunch-and-learn event by our friends at Determined AI
Training complex state-of-the-art natural language processing (NLP) models is now a breeze, thanks to HuggingFace — making it an essential open-source go-to for data scientists and machine learning engineers to implement Transformers models and configure them as state-of-the-art NLP models with straightforward library calls. As a result, the library has become crucial for training NLP models, like in Baidu or Alibaba, and has contributed to state-of-the-art results in several NLP tasks.
Our friends at Determined AI are hosting an exciting lunch-and-learn covering training HuggingFace Transformers at scale using Determined! …
News, Newsletter
AI news, research, updates, an exciting and free-to-attend AI summit, and our monthly editorial picks!
If you have trouble reading this email, see it on a web browser.
Game theory is a mathematical method modeled after the mechanics of game balancing and structure. It’s used to create interactions between multiple parties to help them achieve optimal results without extreme compromises or deviations from normality. It helped Google create an AI that learned how to master Starcraft II in just four hours, and its understanding of risk, strategy, and other critical variables makes it an invaluable tool when teaching AI systems to be flexible and adaptable thinkers. …
Data Science, Editorial, Machine Learning
These are some of the most common challenges in machine learning (ML) with straightforward solutions on how to tackle them
Author(s): Saniya Parveez
Machine learning (ML) is the process of building models that learn from data. It has a diverse set of algorithms that are brought to life with data. However, a traditional classical ML approach can also solve problems with a minimal set of variables by using explicit rules. But, the trick is that it gets complicated when the number of variables increases. Regardless of how they are represented visually, machine learning models are mathematical functions and can consequently be implemented from scratch using numerical software frameworks and packages [1].
