Spark, Ray, and Python for Scalable Data Science
He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and in a former life he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Jonathan is a dedicated educator, author, and speaker...
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| Format: | eBook |
| Language: | English |
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Addison-Wesley Professional
2021
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| Edition: | 1st edition |
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| Collection: | O'Reilly - Collection details see MPG.ReNa |
| Summary: | He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and in a former life he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Jonathan is a dedicated educator, author, and speaker with a passion for sharing the things he has learned in the most creative ways he can. He has run data science workshops at Strata and PyData (among others), built a Data Visualization course with Udacity, and served on the UC Berkeley Extension Data Science Advisory Board. Currently he is writing a book on practical Data Science applications using Python. When he is not working with students, you can find him blogging about data, visualization, and education at http://hopelessoptimism.com/. 7.5 Hours of Video Instruction Conceptual overviews and code-along sessions get you scaling up your data science projects using Spark, Ray, and Python. Overview Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond following along in discussions to coding machine learning tasks. Spark, Ray, and Python for Scalable Data Science LiveLessons show you how to scale machine learning and artificial intelligence projects using Python, Spark, and Ray. About the Instructor Jonathan Dinu is the founder of Zipfian Academy-an advanced immersive training program for data scientists and data engineers in San Francisco-and served as its CAO/CTO before it was acquired by Galvanize, where he now is the VP of Academic Excellence. Skill Level Beginner to Intermediate Learn How To Integrate Python and distributed computing Scale data processing with Spark Conduct exploratory data analysis with PySpark Utilize parallel computing with Ray Scale machine learning and artificial intelligence applications with Ray Who Should Take This Course This course is a good fit for anyone who needs to improve their fundamental understanding of scalable data processing integrated with Python for use in machine learning or artificial intelligence applications. Course Requirements A basic understanding of programming in Python (variables, basic control flow, simple scripts). Familiarity with the vocabulary of data processing at scale, machine learning (dataset, training set, test set, model), and AI. Lesson Descriptions Lesson 1: Introduction to Distributed Computing in Python Lesson 1 starts with an introduction to the dat .. |
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| Item Description: | Made available through: Safari, an O'Reilly Media Company |
| Physical Description: | 1 video file, approximately 7 hr., 23 min. |