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Ubiquitous Learning and Instructional Technologies
by Dr William Cope , Dr Mary Kalantzis- 4.5
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Approx. 14 hours to complete
Areas addressed include: learning management systems, intelligent tutors, computer adaptive testing, gamification, simulations, learning in and through social media and peer interaction, universal design for learning, differentiated instruction systems, big data and learning analytics, attention monitoring, and affect-aware systems. Individualized Learning in the Old Schools The Not-So-New School: Individualization in Computer Adaptive Learning...
An Introduction to Practical Deep Learning
by Andres Rodriguez , Nikhil Murthy , Hanlin Tang- 4.3
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Approx. 17 hours to complete
You will explore important concepts in Deep Learning, train deep networks using Intel Nervana Neon, apply Deep Learning to various applications and explore new and emerging Deep Learning topics. Introduction to Deep Learning and Deep Learning Basics Introduction to Deep Learning Introduction to Deep Learning and Deep Learning Basics Exercise 8: Reinforcement Learning...
Learning Technologies Foundations and Applications
by Wenhao David Huang- 4.5
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Approx. 8 hours to complete
This course, Learning Technologies Foundations and Applications, is one of the two four-week MOOC courses that form part of Instructional Design MasterTrack Certificate. Role of Learning Technologies and Career Paths Module 2: The Evolvement of Learning Technologies MODULE 3: Designing Technology-Enriched and -Enabled Learning Environments (TEELE) Technology-Enriched and Enabled Learning Environments (TEELE) Overview...
Python and Machine Learning for Asset Management
by John Mulvey - Princeton University , Lionel Martellini, PhD- 3
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Approx. 17 hours to complete
Supervised learning Unsupervised learning Machine Learning for Investment Decisions: A Brief Guided Tour Machine learning techniques for robust estimation of factor models References for module 2"Machine learning techniques for robust estimation of factor models" Machine learning techniques for efficient portfolio diversification Introduction to module 3 -Machine learning techniques for efficient portfolio diversification...
Reinforcement Learning for Trading Strategies
by Jack Farmer , Ram Seshadri- 3.7
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Approx. 12 hours to complete
In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. TD Learning Q Learning Benefits of Reinforcement Learning in Your Trading Strategy Idiosyncrasies and challenges of data driven learning in electronic trading...
Critical Thinking Skills for University Success
by Katherine Olston , Luke Alexander- 4.8
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Approx. 26 hours to complete
Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Resources Learning Outcomes...
Probabilistic Graphical Models 3: Learning
by Daphne Koller- 4.6
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Approx. 66 hours to complete
They are also a foundational tool in formulating many machine learning problems. Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional) Learning in Parametric Models Learning Undirected Models Learning BN Structure Structure Learning Overview Learning Tree Structured Networks Learning General Graphs: Heuristic Search Learning General Graphs: Search and Decomposability...
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Introduction to Applied Machine Learning
by Anna Koop- 4.7
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Approx. 7 hours to complete
This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project. The Machine Learning Process...
Communication Skills for University Success
by Katherine Olston , Lydia Dutcher- 4.8
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Approx. 27 hours to complete
Communication Weekly Topics & Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes Learning Outcomes...
Applied Machine Learning in Python
by Kevyn Collins-Thompson- 4.6
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Approx. 34 hours to complete
The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. Key Concepts in Machine Learning Python Tools for Machine Learning An Example Machine Learning Problem Module 2: Supervised Machine Learning - Part 1 Introduction to Supervised Machine Learning...
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