Natural language processing has garnered interest in helping people interact with computer systems to make sense and meaning of the world. In the area of visual analytics, natural language has been shown to help improve the overall cognition of visualization tasks. In this latest Data Science Central webinar, Vidya will discuss how natural language can be leveraged in various aspects of the analytical workflow ranging from smarter data transformations, visual encodings, autocompletion to supporting analytical intent. More recently, chatbot systems have garnered interest as conversational interfaces for a variety of tasks. Machine learning approaches have proven to be promising for approximating the heuristics and conversational cues for continuous learning in a chatbot interface.

Welcome to the exhaustive course series "MBA in Programming and Trending Technologies". Ready to know what it is all about? "Digishock 1.0" is the ultimate technology course in 2021 to make you a technology expert today. This is the first beginner's part course of getting involved with Machine Learning and the unique technologies associated with the field. Most of my students have been waiting for me to launch this course.

In a world where nearly all manual tasks are being automated, the definition of manual is changing. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. We are living in an era of constant technological progress, and looking at how computing has advanced over the years, we can predict what's to come in the days ahead. One of the main features of this revolution that stands out is how computing tools and techniques have been democratized. In the past five years, data scientists have built sophisticated data-crunching machines by seamlessly executing advanced techniques.

There are literally countless resources available to learn R -- blogs, online tutorials, Massive Open Online Courses (MOOCs), platforms, and books. For somebody new to the field of Data Science, it is thus hard to identify really great resources. While everybody has a different way of acquiring new knowledge, I believe that the following books are particularly useful, especially for beginners. This book is co-authored by Hadley Wickham who is best known for the hugely popular R package ggplot2. This book provides a well-written overview of how to import, tidy, transform and model data in R. The book is also available free of charge as an online version. It includes many exercises after each section which makes the book particularly interesting for beginners.

As the world of Data Science progresses, more engineers and professionals need to deploy their work. This can be due to testing, obtaining user input, demonstrating model capabilities, or deploying a model to production. Due to this, we need to understand and know how to take a Data Science model and deploy it to a Web App and API using some of the most in-demand and popular libraries, including Flask, NodeJS, and ReactJS. Being able to deploy models will make a DS more versatile and in-demand, but it will also benefit the development and ops teams within the company. In this course, we will take a DS model and learn how to deploy it in a practical and hands-on manner, allowing us to simulate a real-world scenario that can be applied to industry practices.

Data augmentation is a technique to increase the diversity of dataset without an effort to collect any more real data but still help improve your model accuracy and prevent the model from overfitting. In this post, you will learn to implement the most popular and efficient data augmentation procedures for object detection task using Python and OpenCV. Firstly, let's import several libraries and prepare some necessary subroutines before going ahead. The below image is used as a sample image during this post. Random Crop selects randomly a region and crops it out to make a new data sample, the cropped region should have the same width/height ratio as the original image to maintain the shapes of objects.

Every day, thousands of people are moving towards the data science stream because of the allure of the field and great earning opportunities. Some might come from a specialized background, while others simply participate because of interest. To get a data science job you don't particularly need any degrees or certificates, however, having one will increase your chances of getting hired. The first thing you need to have is an appealing portfolio that includes your skills, knowledge, and ability to create solid data science projects. Along with this, you can also add some certificates to show that you have put effort, time, and money to enhance your skills and become a more qualified data scientist.

The 1st International Workshop on Adaptive Cyber Defense was held as part of the 2021 International Joint Conference on Artificial Intelligence. This workshop was organized to share research that explores unique applications of Artificial Intelligence (AI) and Machine Learning (ML) as foundational capabilities for the pursuit of adaptive cyber defense. The cyber domain cannot currently be reliably and effectively defended without extensive reliance on human experts. Skilled cyber defenders are in short supply and often cannot respond fast enough to cyber threats. Building on recent advances in AI and ML the Cyber defense research community has been motivated to develop new dynamic and sustainable defenses through the adoption of AI and ML techniques to both cyber and non-cyber settings. Bridging critical gaps between AI and Cyber researchers and practitioners can accelerate efforts to create semi-autonomous cyber defenses that can learn to recognize and respond to cyber attacks or discover and mitigate weaknesses in cooperation with other cyber operation systems and human experts. Furthermore, these defenses are expected to be adaptive and able to evolve over time to thwart changes in attacker behavior, changes in the system health and readiness, and natural shifts in user behavior over time. The Workshop (held on August 19th and 20th 2021 in Montreal-themed virtual reality) was comprised of technical presentations and a panel discussion focused on open problems and potential research solutions. Workshop submissions were peer reviewed by a panel of domain experts with a proceedings consisting of 10 technical articles exploring challenging problems of critical importance to national and global security. Participation in this workshop offered new opportunities to stimulate research and innovation in the emerging domain of adaptive and autonomous cyber defense.

Clustering has become a core technology in machine learning, largely due to its application in the field of unsupervised learning, clustering, classification, and density estimation. A frequentist approach exists to hand clustering based on mixture model which is known as the EM algorithm where the parameters of the mixture model are usually estimated into a maximum likelihood estimation framework. Bayesian approach for finite and infinite Gaussian mixture model generates point estimates for all variables as well as associated uncertainty in the form of the whole estimates' posterior distribution. The sole aim of this survey is to give a self-contained introduction to concepts and mathematical tools in Bayesian inference for finite and infinite Gaussian mixture model in order to seamlessly introduce their applications in subsequent sections. However, we clearly realize our inability to cover all the useful and interesting results concerning this field and given the paucity of scope to present this discussion, e.g., the separated analysis of the generation of Dirichlet samples by stick-breaking and Polya's Urn approaches. We refer the reader to literature in the field of the Dirichlet process mixture model for a much detailed introduction to the related fields. Some excellent examples include (Frigyik et al., 2010; Murphy, 2012; Gelman et al., 2014; Hoff, 2009). This survey is primarily a summary of purpose, significance of important background and techniques for Gaussian mixture model, e.g., Dirichlet prior, Chinese restaurant process, and most importantly the origin and complexity of the methods which shed light on their modern applications. The mathematical prerequisite is a first course in probability. Other than this modest background, the development is self-contained, with rigorous proofs provided throughout.

The advantages of Machine Learning/Deep Learning (ML/DL) techniques have been seen in a wide range of applications such as image recognition, traffic prediction, self-driving vehicles, and medical diagnosis. These techniques have also gained popularity within the Earth System Observation and Prediction (ESOP) community due to their ability to improve our understanding and prediction capabilities on the Earth's complex and wide-scale dynamics. Together with the increase in computing power, these techniques are valuable to automatically process and analyse a large scale of available data but they still present some limitations, as an example of DL methods that need large amounts of curated and labelled data. As a consequence, they have become a common language between academia and industry across several Earth Observation sectors. Therefore, one of the goals of the workshop is to share all domain experiences from the recent progress and synergies between ML/DL (combined with conventional tools) for satellite observations, weather and climate models, and post-processed model outputs.