This course, run by IBM, is a great introduction to using Python for ML. It is incredibly useful for grasping how to leverage both supervised and unsupervised learning algorithms, giving you all the knowledge you need to start applying both methods in your day-to-day work. If you want to expand your Python knowledge for data science applications, look no further than this course.

Note: By registering for Day 1 you will automatically be registered for Day 2. You cannot register for Day 2. This page is a placeholder. This workshop teaches you techniques for training deep neural networks on multi-GPU technology to shorten the training time required for data-intensive applications. Working with deep learning tools, frameworks, and workflows to perform neural network training, you'll learn concepts for implementing PyTorch multi-GPUs to reduce the complexity of writing efficient distributed software and to maintain accuracy when training a model across many GPUs. Workshop format: Interactive presentation with hands-on exercises Target audience: This workshop is intended for researchers that would like to use multiple GPUs to train deep learning models in PyTorch. Knowledge prerequisites: Participants should be comfortable with training deep learning models using a single GPU.

HELM Learning (Helping Everyone Learn More) is the first online peer-to-peer learning platform which allows students (typically middle-to-high school students) to teach classes and students (typically elementary-to-middle school students) to learn from classes for free. This method of class structure (peer-to-peer learning) has been proven effective for learning, as it promotes teamwork and collaboration, and enables active learning. HELM is a unique platform as it provides an easy process for students to create, teach and learn topics in a structured, peer-to-peer environment. Since HELM was created in April 2020, it has gotten over 4000 student sign ups and 80 teachers, in 4 continents around the world. HELM has grown from a simple website-and-Google-Form platform to having a backend system coded with Python, SQL, JavaScript and HTML, hosted on an AWS service. This not only makes it easier for students to sign up (as the students' information is saved in an SQL database, meaning they can sign up for classes without having to put in their information again, as well as getting automated emails about their classes), but also makes it easier for teachers to teach (as supplemental processes such as creating Zoom links, class recording folders, sending emails to students, etc. are done automatically). In addition, HELM has a recommendation machine learning algorithm which suggests classes and subjects students would enjoy taking, based on the previous classes a student has taken. This has created an easier experience for students to sign up for classes they are interested in.

Amidst the COVID-19 pandemic, the authors of this paper organized a Reinforcement Learning (RL) course for a graduate school in the field of data science. We describe the strategy and materials for creating an exciting learning experience despite the ubiquitous Zoom fatigue and evaluate the course qualitatively. The key organizational features are a focus on a competitive hands-on setting in teams, supported by a minimum of lectures providing the essential background on RL. The practical part of the course revolved around Hearts Gym, an RL environment for the card game Hearts that we developed as an entry-level tutorial to RL. Participants were tasked with training agents to explore reward shaping and other RL hyperparameters. For a final evaluation, the agents of the participants competed against each other.

Learning a new language can be confusing and challenging. You are bombarded with YouTube videos claiming to teach you Python in 10 minutes. In the end, you get more confused and give up. Find out ways that work for you." Even if you understand the basics, you will get more confused in selecting and learning new tools. Furthermore, without structured learning, you will fail to pass any coding interview or test. Just like any skill, you need persistence and practice. In this blog, I have converted my Python learning experience into 7 easy to follow tips. Let's start the journey of becoming an expert Python programmer. Learning everything about Python is not necessary, but you need to build a base. For that, you need to understand the basics. There are plenty more things to learn, but for the starter stick to basics and practice. It is ok to make mistakes, forget the syntax, and get stuck in simple things. Do not force yourself to memorize. The most important thing is that you learn ...

This is another Free Coursera course to learn how deep learning with neural networks can be used to classify images and detect objects in images and videos. In this course, you will use convolutional neural networks (CNNs) to classify images and detect objects.

In this work, we propose a meta algorithm that can solve a multivariate global optimization problem using univariate global optimizers. Although the univariate global optimization does not receive much attention compared to the multivariate case, which is more emphasized in academia and industry; we show that it is still relevant and can be directly used to solve problems of multivariate optimization. We also provide the corresponding regret bounds in terms of the time horizon $T$ and the average regret of the univariate optimizer, when it is robust against nonnegative noises with robust regret guarantees.

Are you ready to take the PCAP – Certified Associate in Python Programming exam? This course is in the form of practice tests and consists of 420 questions that may appear during the PCAP – Certified Associate in Python Programming exam. Where necessary, explanations are added to the questions. This course allows you to confirm your proficiency and give you the confidence you need to earn the PCAP – Certified Associate in Python Programming certification. PCAP – Certified Associate in Python Programming certification is a professional, high-stakes credential that measures the candidate's ability to perform intermediate-level coding tasks in the Python language, including the ability to design, develop, debug, execute, and refactor multi-module Python programs, as well as measures their skills and knowledge related to analyzing and modeling real-life problems in OOP categories with the use of the fundamental notions and techniques available in the object-oriented approach.

It will be no surprise to readers that Python is one of the languages most associated with the practice of data science. While it could be reasonably argued that Python is the absolute top data science programming language, it would be difficult to argue that, along with R and SQL, Python is not one of the top 3. Regardless of the exact rank of the language, there is no denying that Python is a useful tool for implementing data science in practice. Its ecosystem provides a rich tapestry of libraries covering the entire spectrum of data science pipelines and related data processing and analysis tasks. Knowing data science, Python, and their intersection is a fantastic way to ensure your usefulness as a data scientist. This Python data science course will take you from knowing nothing about Python to coding and analyzing data with Python using tools like Pandas, NumPy, and Matplotlib.

COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019. Not long after, the virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This caused many changes around the world and in the United States, including an educational shift towards online learning. In this paper, we seek to understand how the COVID-19 pandemic and increase in online learning impact college students' emotional wellbeing. We use several machine learning and statistical models to analyze data collected by the Faculty of Public Administration at the University of Ljubljana, Slovenia in conjunction with an international consortium of universities, other higher education institutions, and students' associations. Our results indicate that features related to students' academic life have the largest impact on their emotional wellbeing. Other important factors include students' satisfaction with their university's and government's handling of the pandemic as well as students' financial security.