Brief | Book | |
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Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. The author led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. |
Why? - build a strategic, smart and strong analytics capability to transform your institution and ensure a future proof competitive advantage. This type of transformation impacts top-line growth—such as those related to institutional transformation and data utilization—as well as productivity and performance. This discipline includes: Agile and rapid prototyping. Analytics capability assessment and transformation. Remember the conviction: #Analyticship:#BI,#ML,#AI,#BigData,#Analytics,#HiEd:
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Wednesday, August 12, 2020
Building Machine Learning Powered Applications
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