Are you ready to take the PCAP – Certified Associate in Python Programming exam? The last three exams are in the form of practice tests and consists of 240 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.

In their paper FAIR-FATE: Fair Federated Learning with Momentum, Teresa Salazar, Miguel Fernandes, Helder Araujo, and Pedro Henriques Abreu develop a fairness-aware federated learning algorithm which aims to achieve group fairness while maintaining classification performance. Here, Teresa tells us more about their work. With the widespread use of machine learning algorithms to make decisions which impact people's lives, the area of fairness-aware machine learning has been receiving increasing attention. Fairness-aware machine learning algorithms ensure that predictions do not prejudice unprivileged groups of the population with respect to sensitive attributes such as race or gender. However, the focus has been on centralized machine learning, with decentralized methods receiving little attention.

There's an age-old adage in biology: structure determines function. In order to understand the function of the myriad proteins that perform vital jobs in a healthy body--or malfunction in a diseased one--scientists have to first determine these proteins' molecular structure. But this is no easy feat: protein molecules consist of long, twisty chains of up to thousands of amino acids, chemical compounds that can interact with one another in many ways to take on an enormous number of possible three-dimensional shapes. Figuring out a single protein's structure, or solving the "protein-folding problem, can take years of finicky experiments. But earlier this year an artificial intelligence program called AlphaFold, developed by the Google-owned company DeepMind, predicted the 3-D structures of almost every known protein--about 200 million in all. DeepMind CEO Demis Hassabis and senior staff research scientist John Jumper were jointly awarded this year's $3-million Breakthrough Prize in Life ...

Joe Levy is working hard to help avoid another mass school shooting tragedy. He says his technology, designed to spot a gun using existing CCTV cameras, could make a critical difference in future life-or-death situations. Seventeen people died in 2018 when a 19-year-old student opened fire at Stoneman Douglas High School, in Parkland, Florida, USA. Fifteen died in the Columbine High School massacre, near Denver, Colorado, in 1999 when a 17-year-old and an 18-year-old shot fellow students. And 22 people died in May of this year when an 18-year-old rampaged through the Robb Elementary School, in Uvalde, Texas – one of the worst school shootings in US history.

New Created by Dr. KM Mohsin Let me tell you my story. I graduated with my Ph. D. in computational nano-electronics but I have been working as a data scientist in most of my career. My undergrad and graduate major was in electrical engineering (EE) and minor in Physics. After first year of my job in Intel as a "yield analysis engineer" (now they changed the title to Data Scientist), I literally broke into data science by taking plenty of online classes. I took numerous interviews, completed tons of projects and finally I broke into data science. I consider this as one of very important achievement in my life. Without having a degree in computer science (CS) or a statistics I got my second job as a Data Scientist. Since then I have been working as a Data Scientist.

Language model (LM) pre-training is useful in many language processing tasks. But can pre-trained LMs be further leveraged for more general machine learning problems? We propose an approach for using LMs to scaffold learning and generalization in general sequential decision-making problems. In this approach, goals and observations are represented as a sequence of embeddings, and a policy network initialized with a pre-trained LM predicts the next action. We demonstrate that this framework enables effective combinatorial generalization across different environments and supervisory modalities. We begin by assuming access to a set of expert demonstrations, and show that initializing policies with LMs and fine-tuning them via behavior cloning improves task completion rates by 43.6% in the VirtualHome environment. Next, we integrate an active data gathering procedure in which agents iteratively interact with the environment, relabel past "failed" experiences with new goals, and update their policies in a self-supervised loop. Active data gathering further improves combinatorial generalization, outperforming the best baseline by 25.1%. Finally, we explain these results by investigating three possible factors underlying the effectiveness of the LM-based policy. We find that sequential input representations (vs. fixed-dimensional feature vectors) and LM-based weight initialization are both important for generalization. Surprisingly, however, the format of the policy inputs encoding (e.g. as a natural language string vs. an arbitrary sequential encoding) has little influence. Together, these results suggest that language modeling induces representations that are useful for modeling not just language, but also goals and plans; these representations can aid learning and generalization even outside of language processing.

Learn to implement 10 Machine Learning algorithms from scratch with just Python and NumPy. A library hides the implementation details and if you're really looking to understand what goes behind the covers and understand how things work, this course has you covered. This is a course by AssemblyAI where you don't rely on libraries like Pytorch or Tensorflow to implement the Machine learning algorithms but you implement them yourself from scratch with nothing but Python and NumPy. You need basic Python, object oriented programming and the basics of NumPy to follow along as it's a practical course with a lot of code. However scary math formulas are referred too.If you do have experience with Andrew Ng's deep learning courses which require high school level math and teach the basics of the notations you should not face any issues even on that part.

In this study, learning modalities offered by public schools across the United States were investigated to track changes in the proportion of schools offering fully in-person, hybrid and fully remote learning over time. Learning modalities from 14,688 unique school districts from September 2020 to June 2021 were reported by Burbio, MCH Strategic Data, the American Enterprise Institute's Return to Learn Tracker and individual state dashboards. A model was needed to combine and deconflict these data to provide a more complete description of modalities nationwide. A hidden Markov model (HMM) was used to infer the most likely learning modality for each district on a weekly basis. This method yielded higher spatiotemporal coverage than any individual data source and higher agreement with three of the four data sources than any other single source. The model output revealed that the percentage of districts offering fully in-person learning rose from 40.3% in September 2020 to 54.7% in June of 2021 with increases across 45 states and in both urban and rural districts. This type of probabilistic model can serve as a tool for fusion of incomplete and contradictory data sources in support of public health surveillance and research efforts.

We develop an implementable stochastic proximal point (SPP) method for a class of weakly convex, composite optimization problems. The proposed stochastic proximal point algorithm incorporates a variance reduction mechanism and the resulting SPP updates are solved using an inexact semismooth Newton framework. We establish detailed convergence results that take the inexactness of the SPP steps into account and that are in accordance with existing convergence guarantees of (proximal) stochastic variance-reduced gradient methods. Numerical experiments show that the proposed algorithm competes favorably with other state-of-the-art methods and achieves higher robustness with respect to the step size selection.

Interaction is a fundamental part of using any computer system but it is still an issue for people with special needs. In order to improve this situation, this paper describes a new device-interaction model based on adaptation rules for user models. The aim is the adaptation at the interaction level, taking into account the interaction device features in order to improve the usability through the user experience in the education sector. In the evaluation process, several students from a special education center have participated. These students have either a physical or sensory disability or autism. The results are promising enough to consider that this model will be able to help students with disabilities to interact with a computer system which will inevitably provide tremendous benefits to their academic and personal development.