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  • 2015-2019  (6)
  • Gift, Noah  (6)
  • Boston, MA : Safari  (6)
  • Wiesbaden : Springer Fachmedien Wiesbaden
  • Electronic videos ; local  (6)
  • Economic sociology
  • 1
    Online Resource
    Online Resource
    Pragmatic AI and Machine Learning Core Principles (2019)
    [Erscheinungsort nicht ermittelbar] : Addison-Wesley Professional | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 4 hr., 1 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: 4+ Hours of Video Instruction Machine Learning is the scientific study of models and algorithms that train a computer to make predictions without explicit instruction. Machine Learning is a subset of Artificial Intelligence, which can be defined as computers that mimic human problem-solving. This video demonstrates the core principles of Machine Learning and AI, including supervised Machine Learning, unsupervised Machine Learning, neural networks, and social network theory. Learn to master the foundational concepts of AI and Machine Learning. The LiveLessons video starts with an overview of Artificial Intelligence and covers applications of AI across industries and opportunities in AI for individuals, organizations, and ecosystems. It also covers the difference between narrow, general, and super AI. Description Shore up the foundational knowledge necessary to work with Artificial Intelligence and Machine Learning! This LiveLesson video covers the core principles of Artificial Intelligence and Machine Learning, including how to frame a problem in terms of Machine Learning and how Machine Learning is different than statistics. Learn about fundamental concepts including nearest neighbors, decision trees, and neural networks. The video wraps up covering timely machine learning topics such as cluster analysis, dimensionality reduction, and social networks. Access the code repository for this LiveLesson at https://github.com/noahgift/fundamentals_ai_ml . About the Instructor Noah Gift is lecturer and consultant at UC Davis Graduate School of Management MSBA program the Graduate Data Science program, MSDS, at Northwestern, the Graduate Data Science program at UC Berkeley. He is teaching and designing graduate Machine Learning, AI, Data Science courses and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students. Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect and AWS Academy Accredited Instructor, Google Certified Professional Cloud Architect, and Microsoft MTA on Python. Noah was selected to the SME Machine Learning team due to accomplishments in the area of Machine Learning on the AWS platform. He has published more than 100 technical publications, including several books on subjects ranging from Cloud Machine Learning to DevOps. Gift received an MBA fro...
    Note: Online resource; Title from title screen (viewed September 5, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Data Engineering with Python and AWS Lambda LiveLessons (2019)
    [Erscheinungsort nicht ermittelbar] : Addison-Wesley Professional | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 6 hr., 11 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: 7 Hours of Video Instruction Data Engineering with Python and AWS Lambda LiveLessons shows users how to build complete and powerful data engineering pipelines in the same language that Data Scientists use to build Machine Learning models. By embracing serverless data engineering in Python, you can build highly scalable distributed systems on the back of the AWS backplane. Users learn to think in the new paradigm of serverless, which means to embrace events and event-driven programs that replace expensive and complicated servers. Description Some of the many benefits of programming with AWS Lambda in Python include no servers to manage, continuous scaling, and subsecond metering. Several use cases include data processing, stream processing, IoT backends, mobile, and web applications. Learn to take advantage of a new paradigm in software architecture that will make your code easier to write, maintain, and deploy. AWS Lambda functions are the building blocks for creating sophisticated applications and services on AWS. In this LiveLesson, you learn to use Python to develop Lambda functions that communicate with key AWS services: API Gateway, SQS, and CloudWatch functions. You also learn how a new cloud-based development environment, Cloud9, can streamline writing, debugging, and deploying AWS Lambda functions. About the Instructors Noah Gift is a lecturer and consultant at both the UC Davis Graduate School of Management MSBA program and the Graduate Data Science program, MSDS, at Northwestern. He is teaching and designing graduate Machine Learning, AI, and Data Science courses, and consulting on Machine Learning and Cloud Architecture for students and faculty, including leading a multi-cloud certification initiative for students. Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect and AWS Academy Accredited Instructor, Google Certified Professional Cloud Architect, and Microsoft MTA on Python. Noah has published close to 100 technical publications, including two books on subjects ranging from Cloud Machine Learning to DevOps. Gift received an MBA from UC Davis, an M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo. Currently, he is consulting startups and other companies on Machine Learning, Cloud Architecture, and CTO level consulting as the founder of Pragmatic AI Labs. His most recent ...
    Note: Online resource; Title from title screen (viewed August 29, 2019)
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Python for Data Science Complete Video Course (Video Training) (2019)
    [Erscheinungsort nicht ermittelbar] : Addison-Wesley Professional | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 6 hr., 39 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: 9+ Hours of Video Instruction While there are resources for Data Science and resources for Machine Learning, there's a distinct gap in resources for the precursor course to Data Science and Machine Learning. This complete video course fills that gap--it is specifically designed to prepare students to learn how to program for Data Science and Machine Learning with Python. This is the antidote to the over-complicated universe of these hot new, growing technologies. With this course, students will learn the fundamentals of Python and get prepared specifically for Data Science. Noah Gift and Kennedy Behrman take students with zero programming background through enough Python to prepare them for their Data Science curriculum. Companies are looking for developers who can create insight-driven systems, as they are now becoming critical to business success. Very few professionals are adequately trained to handle both large-scale software engineering and Machine Learning/AI. This is an emerging field, and we are developing the training to meet this need in the marketplace. Description Notebook-based Data Science programming in Python is the emerging standard but there is a dearth of quality training material available for beginners. This 9-hour video provides foundational training on the Python language for the novice or beginner programmer looking to start in the Data Science field. The video serves as the 100-level course for a Data Science undergraduate or graduate program. The course has been designed around Colab notebook-based learning. Students would be able to run every exercise shown in the videos. The material focuses on a smaller, easier subset of Python that is needed just for Data Science coding. Skill Level Beginner What You Will Learn Learn Google Colab notebook Data Science programming Learn the essential subset of Python used in Data Science Learn to manipulate data using popular Python libraries such as pandas and numpy Learn to apply Python Data Science recipes to real-world projects Learn functional programming fundamentals unique to Data Science Who Should Take This Course Complete beginners to programming Statisticians and Analysts in the data industry looking to use Python for Data Science Sales, Product Managers, Data Analysts, Marketing who want to perform Data Science Software Engineers looking to level up into Data Science and Machine Learning tracks Students enrolled in a Data Science program Course Requirements General comp...
    Note: Online resource; Title from title screen (viewed April 2, 2019)
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    AWS Certified Machine Learning-Specialty (ML-S) (2019)
    [Erscheinungsort nicht ermittelbar] : Pearson IT Certification | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 5 hr., 38 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: More Than 7 Hours of Video Instruction Overview This course covers the essentials of Machine Learning on AWS and prepares a candidate to sit for the AWS Machine Learning-Specialty (ML-S) Certification exam. Four main categories are covered: Data Engineering, EDA (Exploratory Data Analysis), Modeling, and Operations. Description This 7+ hour Complete Video Course is fully geared toward the AWS Machine Learning-Specialty (ML-S) Certification exam. The course offers a modular lesson and sublesson approach, with a mix of screencasting and headhsot treatment. Data Engineering instruction covers the ingestion, cleaning, and maintenance of data on AWS. Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services. Machine Learning Modeling covers topics including feature engineering, performance metrics, overfitting, and algorithm selection. Operations covers deploying models, A/B testing, using AI services versus training your own model, and proper cost utilization. The supporting code for this LiveLesson is located at http://www.informit.com/store/aws-certified-machine-learning-specialty-ml-s-complete-9780135556511 . About the Instructor Noah Gift is a lecturer and consultant at both the UC Davis Graduate School of Management MSBA program and the Graduate Data Science program, MSDS, at Northwestern. He teaches and designs graduate machine learning, AI, data science courses, and consulting on machine learning and cloud architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students. Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect, AWS Academy accredited instructor, Google Certified Professional Cloud Architect, and Microsoft MTA on Python. Noah has published close to 100 technical publications including two books on subjects ranging from cloud machine learning to DevOps. Noah received an MBA from UC Davis, a M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo. Currently he consults for startups and other companies on machine learning, cloud architecture, and CTO-level consulting as the founder of Pragmatic AI Labs. His most recent publications are Pragmatic AI: An introduction to Cloud-Based Machine Learning (Pear...
    Note: Online resource; Title from title screen (viewed February 28, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 5
    Online Resource
    Online Resource
    Building AI Applications on Google Cloud Platform (2019)
    [Erscheinungsort nicht ermittelbar] : Addison-Wesley Professional | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 3 hr., 28 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: 4+ Hours of Video Instruction Overview There is a rapid evolution occurring in machine learning with tools like AutoML that basically automate many of the tedious aspects of machine learning and allow developers to focus on getting results into production. Noah Gift illustrates just now to harness this technology and deploy it successfully on Google Cloud Platform, demonstrating for developers how to employ the current best practices and automated tools to create analytics applications that solve real-world problems. Developers who want to take their Data Science skills to the next level and build AutoML applications in the Cloud will benefit from this unique course, as they learn how to use AutoML, Big Query, Python, and Google App Engine to create sophisticated AI. Description Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology. Developers use Cloud AutoML’s graphical user interface to train, evaluate, improve, and deploy models based on their data. This LiveLesson covers programming components essential to the development of AI and Analytics applications. The focus is on building real-world software engineering applications on the Google Cloud Platform. Several emerging technologies are used to demonstrate the process, including AutoML and Google BigQuery. The Python language is used throughout the course, as Python is becoming the de facto standard language for AI application development in the cloud. Download the supplemental files for this LiveLesson from http://www.informit.com/store/building-ai-applications-on-google-cloud-platform-livelessons-9780135973509 . About the Instructor Noah Gift is a lecturer at the UC Davis Graduate School of Management, MSBA program, the Graduate Data Science program, MSDS, at Northwestern and the Graduate Information and Data Science Program at UC Berkeley. He is teaching and designing graduate machine learning, AI, Data Science, and Cloud Architecture courses. These responsibilities include leading a multi-cloud certification initiative for students. Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect, AWS Certified Big Data-Specialist, AWS Certified Machine Learning-Specialist, AWS Academy Accredited I...
    Note: Online resource; Title from title screen (viewed August 8, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 6
    Online Resource
    Online Resource
    Essential Machine Learning and AI with Python and Jupyter Notebook (2018)
    [Erscheinungsort nicht ermittelbar] : Addison-Wesley Professional | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 8 hr., 16 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: 8+ Hours of Video Instruction Learn just the essentials of Python-based Machine Learning on AWS and Google Cloud Platform with Jupyter Notebook. Description This 8-hour LiveLesson video course shows how AWS and Google Cloud Platform can be used to solve real-world business problems in Machine Learning and AI. Noah Gift covers how to get started with Python via Jupyter Notebook, and then proceeds to dive into nuts and bolts of Data Science libraries in Python, including Pandas, Seaborn, scikit-learn, and TensorFlow. EDA, or exploratory data analysis, is at the heart of the Machine Learning; therefore, this series also highlights how to perform EDA in Python and Jupyter Notebook. Software engineering fundamentals tie the series together, with key instruction on linting, testing, command-line tools, data engineering APIs, and more. The supporting code for this LiveLesson is located at http://www.informit.com/store/essential-machine-learning-and-ai-with-python-and-jupyter-9780135261095 . About the Instructor Noah Gift is lecturer and consultant at UC Davis Graduate School of Management in the MSBA program. He is teaching graduate machine learning and consulting on Machine Learning and Cloud Architecture for students and faculty. He has published close to 100 technical publications, including two books on subjects ranging from Cloud Machine Learning to DevOps. He is also a certified AWS Solutions Architect and an SME (Subject Matter Expert for Machine Learning for AWS). He has an MBA from UC Davis, an MS in Computer Information Systems from Cal State Los Angeles, and a BS in Nutritional Science from Cal Poly San Luis Obispo. Professionally, Noah has approximately 20 years of experience of programming in Python and is a member of the Python Software Foundation. He has worked in roles ranging from CTO, General Manager, Consulting CTO and Cloud Architect. This experience has been with a wide variety of companies including ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios, and Linden Lab. In the past ten years, he has been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had global scale. He is the founder of Pragmatic AI Labs, a training, consulting, and AI/ML product company that specializes in cloud native Machine Learning and AI Solutions. Skill Level Beginner What You Will Learn Introduces Data Science concepts and Python funda...
    Note: Online resource; Title from title screen (viewed August 24, 2018)
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
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