Search result for Courses taught by Ilkay Altintas
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Introduction to Big Data
by Ilkay Altintas , Amarnath Gupta- 4.6
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Approx. 17 hours to complete
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems....
Big Data Modeling and Management Systems
by Ilkay Altintas , Amarnath Gupta- 4.4
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Approx. 13 hours to complete
Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. Through guided hands-on tutorials, you will become familiar with techniques using real-time and semi-structured data examples....
Big Data Integration and Processing
by Ilkay Altintas , Amarnath Gupta- 4.4
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Approx. 18 hours to complete
At the end of the course, you will be able to: *Retrieve data from example database and big data management systems *Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications *Identify when a big data problem needs data integration...
Basic Data Processing and Visualization
by Julian McAuley , Ilkay Altintas- 4.3
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Approx. 11 hours to complete
This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization. Week 1: Introduction to Data Products...
Deploying Machine Learning Models
by Ilkay Altintas , Julian McAuley- 3.5
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Approx. 11 hours to complete
In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e. g. Introduction Introduction to Recommender Systems Recommender Systems versus Other Forms of Supervised Learning...
Big Data - Capstone Project
by Ilkay Altintas , Amarnath Gupta- 4.4
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Approx. 21 hours to complete
Welcome to the Capstone Project for Big Data! In this culminating project, you will build a big data ecosystem using tools and methods form the earlier courses in this specialization. You will analyze a data set simulating big data generated from a large number of users who are playing our imaginary game "Catch the Pink Flamingo"....
Design Thinking and Predictive Analytics for Data Products
by Julian McAuley , Ilkay Altintas- 4.5
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Approx. 8 hours to complete
This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models....
مقدمة عن البيانات الضخمة
by Ilkay Altintas , Amarnath Gupta- 4.5
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Approx. 17 hours to complete
مقدمة عن البيانات الضخمة هل أنت مهتم بزيادة معرفتك بأبرز سمات البيانات الضخمة؟ هذه الدورة التدريبية مخصصة للمستجدين في علوم البيانات والمهتمين بفهم أسباب ظهور عصر البيانات الضخمة. فهي مخصصة لمن يريدون الإلمام بالمصطلحات والمفاهيم الأساسية الخاصة بمشكلات البيانات الضخمة وتطبيقاتها وأنظمتها. وفي نهاية الدورة التدريبية، ستتمكن مما يلي: * تثبيت البرامج وتشغيلها باستخدام إطار عمل Hadoop!...
Meaningful Predictive Modeling
by Julian McAuley , Ilkay Altintas- 4.4
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Approx. 9 hours to complete
This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better?...
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