BESTSELLER, 4.5 (28,341 ratings), Created by 365 Careers, 365 Careers Team, English [Auto-generated], Italian [Auto-generated], 1 more The course provides the entire toolbox you need to become a data scientist Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow Impress interviewers by showing an understanding of the data science field Learn how to pre-process data Understand the mathematics behind Machine Learning (an absolute must which other courses don't teach!) Start coding in Python and learn how to use it for statistical analysis Perform linear and logistic regressions in Python Carry out cluster and factor analysis Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn Apply your skills to real-life business cases Use state-of-the-art Deep Learning frameworks such as Google's TensorFlowDevelop a business intuition while coding and solving tasks with big data Unfold the power of deep neural networks Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations Understand the mathematics behind Machine Learning (an absolute must which other courses don't teach!) No prior experience is required. We will start from the very basics You'll need to install Anaconda. No prior experience is required. You'll need to install Anaconda.

Is actual knowledge needed to beat this test? The stories were supposed to capture a new step forward in artificial intelligence. A "Breakthrough for A.I. Technology: Passing an 8th-Grade Science Test," said the New York Times. "AI Aristo takes science test, emerges multiple-choice superstar," said TechXPlore. Both stories were talking about Aristo (indicating a child version of Aristotle), a project of Paul Allen's Allen Institute for Artificial Intelligence, where the headline read, "How to tutor AI from an'F' to an'A.'"

Successful adoption of artificial intelligence (AI) at the workplace ultimately depends on employees accepting and embracing their changing role in the future of work, says a recent white paper by global training and development organization Dale Carnegie, "Beyond Technology: Preparing People for Success in the Era of AI". The paper was based on responses from over 3,500 employees. Mark Marone, director of research and thought leadership for Dale Carnegie and Associates and the author of the paper, believes AI can a significant effect on work culture, employee trust and ethical decision-making. How is technology affecting corporate culture and employee engagement? Let's start with the positive side because in our research, most people expect AI's impact to be positive.

Low-rank metric learning aims to learn better discrimination of data subject to low-rank constraints. It keeps the intrinsic low-rank structure of datasets and reduces the time cost and memory usage in metric learning. However, it is still a challenge for current methods to handle datasets with both high dimensions and large numbers of samples. To address this issue, we present a novel fast low-rank metric learning (FLRML) method.FLRML casts the low-rank metric learning problem into an unconstrained optimization on the Stiefel manifold, which can be efficiently solved by searching along the descent curves of the manifold.FLRML significantly reduces the complexity and memory usage in optimization, which makes the method scalable to both high dimensions and large numbers of samples.Furthermore, we introduce a mini-batch version of FLRML to make the method scalable to larger datasets which are hard to be loaded and decomposed in limited memory. The outperforming experimental results show that our method is with high accuracy and much faster than the state-of-the-art methods under several benchmarks with large numbers of high-dimensional data. Code has been made available at https://github.com/highan911/FLRML

More and more companies have deployed machine learning (ML) clusters, where deep learning (DL) models are trained for providing various AI-driven services. Efficient resource scheduling is essential for maximal utilization of expensive DL clusters. Existing cluster schedulers either are agnostic to ML workload characteristics, or use scheduling heuristics based on operators' understanding of particular ML framework and workload, which are less efficient or not general enough. In this paper, we show that DL techniques can be adopted to design a generic and efficient scheduler. DL2 is a DL-driven scheduler for DL clusters, targeting global training job expedition by dynamically resizing resources allocated to jobs. DL2 advocates a joint supervised learning and reinforcement learning approach: a neural network is warmed up via offline supervised learning based on job traces produced by the existing cluster scheduler; then the neural network is plugged into the live DL cluster, fine-tuned by reinforcement learning carried out throughout the training progress of the DL jobs, and used for deciding job resource allocation in an online fashion. By applying past decisions made by the existing cluster scheduler in the preparatory supervised learning phase, our approach enables a smooth transition from existing scheduler, and renders a high-quality scheduler in minimizing average training completion time. We implement DL2 on Kubernetes and enable dynamic resource scaling in DL jobs on MXNet. Extensive evaluation shows that DL2 outperforms fairness scheduler (i.e., DRF) by 44.1% and expert heuristic scheduler (i.e., Optimus) by 17.5% in terms of average job completion time.

The work presented in this paper aims to explore how, and to what extent, an adaptive robotic coach has the potential to provide extra motivation to adhere to long-term rehabilitation and help fill the coaching gap which occurs during repetitive solo practice in high performance sport. Adapting the behavior of a social robot to a specific user, using reinforcement learning (RL), could be a way of increasing adherence to an exercise routine in both domains. The requirements gathering phase is underway and is presented in this paper along with the rationale of using RL in this context.

Text-based Question Generation (QG) aims at generating natural and relevant questions that can be answered by a given answer in some context. Existing QG models suffer from a "semantic drift" problem, i.e., the semantics of the model-generated question drifts away from the given context and answer. In this paper, we first propose two semantics-enhanced rewards obtained from downstream question paraphrasing and question answering tasks to regularize the QG model to generate semantically valid questions. Second, since the traditional evaluation metrics (e.g., BLEU) often fall short in evaluating the quality of generated questions, we propose a QA-based evaluation method which measures the QG model's ability to mimic human annotators in generating QA training data. Experiments show that our method achieves the new state-of-the-art performance w.r.t. traditional metrics, and also performs best on our QA-based evaluation metrics. Further, we investigate how to use our QG model to augment QA datasets and enable semi-supervised QA. We propose two ways to generate synthetic QA pairs: generate new questions from existing articles or collect QA pairs from new articles. We also propose two empirically effective strategies, a data filter and mixing mini-batch training, to properly use the QG-generated data for QA. Experiments show that our method improves over both BiDAF and BERT QA baselines, even without introducing new articles.

AI is constantly evolving and reaching new milestones all the time. Since nearly two-thirds of Americans rely on Twitter as their primary news source, we've decided to share the top influencers that Lionbridge AI follows to stay in the know. If you don't already, give us a follow us too @LionbridgeAI for daily updates on what's new, from Lionbridge and the AI and machine learning industry at large. AI For Everyone is now available on @Coursera! This course will help non-engineers and engineers work together to leverage AI capabilities and build an AI strategy. If you want your company to embrace AI, this is the course to get your CEO to take! https://t.co/bzpf1ed8DL

We all remember a favorite teacher. But I wonder if what we recall is the social engagement or the actual teaching ability. I would imagine that it's a combination of both. Yesterday's and today's great teachers have that knack for bringing information to life and igniting a desire to learn. And central to this is the ability to customize content around a student's needs or aptitudes.

Today, CUNY's continuing education programs teach job-specific tools ranging from business management to plumbing, but in-depth courses can pose a significant cost burden if not paid for by an employer. Lifelong learning dollars could also be used to earn specific industry-recognized credentials in fields like video production, solar installation, or IT support, or retrain for a tech career at a bootcamp like General Assembly or Flatiron School, which deliver a strong return on investment but come at a high upfront cost.