Enterprises that seek to become proficient in advanced manufacturing must incorporate manufacturing management tools and integrate data throughout the supply chain to be successful. This course will make students aware of what a digitally connected enterprise is, as they learn about the operational complexity of enterprises, business process optimization and the concept of an integrated product-process-value chain. Students will become acquainted with the available tools, technologies and techniques for aggregation and integration of data throughout the manufacturing supply chain and entire product life-cycle. They will receive foundational knowledge to assist in efforts to facilitate design, planning, and production scheduling of goods and services by applying product life cycle data. Main concepts of this course will be delivered through lectures, readings, discussions and various videos. This is the sixth course in the Digital Manufacturing & Design Technology specialization that explores the many facets of manufacturing’s “Fourth Revolution,” aka Industry 4.0, and features a culminating project involving creation of a roadmap to achieve a self-established DMD-related professional goal. To learn more about the Digital Manufacturing and Design Technology specialization, please watch the overview video by copying and pasting the following link into your web browser: https://youtu.be/wETK1O9c-CA

This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems. Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems. At the conclusion of this course, you should be able to: 1) Identify opportunities to apply ML to solve problems for users 2) Apply the data science process to organize ML projects 3) Evaluate the key technology decisions to make in ML system design 4) Lead ML projects from ideation through production using best practices

PLEASE NOTE: This course will close for new learner enrollment on January 22nd, 2021. If you would prefer to engage with our latest courses related to this material, please see the following courses: Contact Center AI: Conversational Design Fundamentals: https://www.coursera.org/learn/contact-center-ai-conversational-design-fundamentals/home/welcome. Contact Center AI: Building a Dynamic Virtual Agent: https://www.coursera.org/learn/contact-center-ai-building-a-dynamic-virtual-agent/home/welcome The Contact Center AI: Conversational Design Fundamentals and Contact Center AI: Building a Dynamic Virtual Agent courses have similar learning objectives to the ones in the course being deprecated in this notice. They walk learners through the process of identifying a use case, creating intents and entities for conversational agents, and using context and fulfillment for conversation awareness and third party system integrations. Since many use cases for virtual agents happen in the context of contact centers, we invite you to extend your expertise by taking additional courses in the Contact Center AI Specialization, although this is not required if your interest is solely on building automated conversational agents. Thanks, and happy learning, The Google Cloud Learning Services team Course Description: This course provides a deep dive into how to create a chatbot using Dialogflow, augment it with Cloud Natural Language API, and operationalize it using Google Cloud tools. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

In this 1-hour long project-based course, you will learn how to predict bike sharing demand with machine learning. Bike sharing services enable people to rent a bike from one location and drop it off at another location on an as-needed basis. The objective of this guided project is to predict bike sharing rental usage based on inputs such as temperature, season, humidity, wind speed. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

As data becomes the modern currency, so the ability to analyse the data quickly and accurately has become of paramount importance. Excel with its extraordinarily broad range of features and capabilities is one of the most widely used programs for doing this. In the first course of our Excel Skills for Data Analysis and Visualization Specialization, you will learn the fundamentals of Excel for data analysis. When you have completed the course, you will be able to use a range of Excel tools and functions to clean and prepare data for analysis; automate data analysis with the help of Named Ranges and Tables; and use logical and lookup functions to transform, link and categorise data. This course will enable you to build a strong foundation in the fundamentals, helping you to be more efficient in your day-to-day and developing the necessary skills to work with the more advanced techniques used in later courses. To make the content easy to relate to and to personalize the learning experience, we are going to follow Zara's journey through the course. Who is Zara? Well, she is no-one and everyone. You will find that Zara's trials and tribulations sound familiar, and together with Zara, you will develop your Excel skills along the way — and, importantly, have some fun doing it. The Excel Skills for Data Analytics and Visualization courses are the sequel to one of most successful specializations on Coursera, Excel Skills for Business, which has attracted hundreds of thousands of learners and top ratings. Transform your skills, your confidence, and your opportunities by adding this new set of skills to your repertoire.

In this 1.04 hour long project-based course, you will be able to read in external files using an input method called list, manipulate Missing values, place the DATALINES keyword in your program as well as INFILE statements and work with a Comma Separated Values (CSV) file. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this 2 hour long project-based course you will learn how to perform anomaly detection, its importance in machine learning, set up PyCaret anomaly detection, create, visualize & compare anomaly detection algorithms all this with just a few lines of code.

This is a self-paced lab that takes place in the Google Cloud console. This lab focuses on how to create new reporting tables using SQL JOINS and UNIONs.

In this project, you will learn to perform mediation analysis in RStudio. The project explains the theoretical concepts of mediation and illustrates the process with sample stress detection data. It covers the distinction between mediation and moderation process, explains the selection criteria for a suitable mediator. The project describes the mediation process with statistical models, diagnostic measures and conceptual diagram.

This is a self-paced lab that takes place in the Google Cloud console. In this hands-on lab you’ll learn how to use BigQuery to explore the cryptocurrency public datasets now available. This is a challange lab, and you are required to complete some simple SQL statements.