AMFASOFT CORPORATION (2) - Laserfiche WebLink

Home Browse Search

Sig Data and Deep Learning Program Description This course will teach students to work on various real world big data projects using different Big Data tools as a part of solution strategy. The course will provide students' knowledge and skills to process big data on platforms that can handle the variety, velocity, and volume of data. This comprehensive training on framework provides hands on experience for solving real time industry based big data projects to become an expert in Big Data. In this course we will learn about the basics of deep neural networks, and their applications to various tasks. The course aim is to present the mathematical, statistical, and computational challenges of building stable representations for high -dimensional data. Course Objectives • Student will learn how to format data using new technologies and techniques • Learn about the fundamentals of databases and learn basic principles of Big Data • Learn the basic tools for statistical analysis, R and Python, and several machine learning algorithms. • Learn the tools required for building Deep Learning models. • Explore multiple architectures and understand how to fine-tune and continuously improve models • Learn how the same task can be solved using multiple Deep Leaning approaches Course Delivery Option: Classroom, Online/Distance Leaming/Distance Learning Sequence and frequency of class sessions: Every 6 weeks Related Job Titles/Occupations Software Developers, Applications SCO Code 15-1132.00 Instruction Details Students will learn tools and analytical methods to use data for decision -making, collect and organize data at scale, and gain an understanding of how data analysis can help to inform change within organizations. This course will develop both the technical and computational skills that are in high demand across a range of industries. The course gives practical exercises to familiarize students with the format of big data and how to handle and analyze large, complex and data structures. Students will have significant familiarity with the subject and be able to apply Deep Learning to a variety of tasks. They will also be positioned to understand much of the current literature on the topic and extend their knowledge through further study. Program In Duration: Course Name Clock Hours Duration Maximum Completion Time Big Data and Deep Learning 480 hours 24 weeks 36 weeks Pre -Requisite: Basic Computer I{nowledge Instructional Material: Textbooks Big Data Fundamentals: Concepts, Drivers & Techniques (The Pearson Service Technology Series from Thomas Erl) by Thomas Erl, Wajid Khattak, or al. Deep Learning (Adaptive Computation and Machine Learning series) Part of: Adaptive Computation and Machine Learning series (21 Books) I by Ian Goodfellow , Yoshua Bengio , et al. Total Charges for Period of Attendance & Estimated charges for entire program: $8 279 00 Tuition Fee: $8,004; Registration Fee: $75 (Non-refundable); Boots: $200.00 Requirement for completing the program End of the program students are required to complete all the projects and lab work assigned during the program. Student will get the course completion certificate after completing the examination and the projects. Student may take the Big Data and Deep Learning certification exams 53