Pragmatic AI and Machine Learning Core Principles-0daytown

Machine Learning is the scientific study of models and algorithms that train a computer to make predictions without explicit instruction. Machine Learning is a subset of Artificial Intelligence, which can be defined as computers that mimic human problem-solving. This video demonstrates the core principles of Machine Learning and AI, including supervised Machine Learning, unsupervised Machine Learning, neural networks, and social network theory.

Learn to master the foundational concepts of AI and Machine Learning. The LiveLessons video starts with an overview of Artificial Intelligence and covers applications of AI across industries and opportunities in AI for individuals, organizations, and ecosystems. It also covers the difference between narrow, general, and super AI.

Description

Shore up the foundational knowledge necessary to work with Artificial Intelligence and Machine Learning! This LiveLesson video covers the core principles of Artificial Intelligence and Machine Learning, including how to frame a problem in terms of Machine Learning and how Machine Learning is different than statistics. Learn about fundamental concepts including nearest neighbors, decision trees, and neural networks. The video wraps up covering timely machine learning topics such as cluster analysis, dimensionality reduction, and social networks.

Access the code repository for this LiveLesson at https://github.com/noahgift/fundamentals_ai_ml.

About the Instructor

Noah Gift is lecturer and consultant at UC Davis Graduate School of Management MSBA program the Graduate Data Science program, MSDS, at Northwestern, the Graduate Data Science program at UC Berkeley. He is teaching and designing graduate Machine Learning, AI, Data Science courses and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students.

Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect and AWS Academy Accredited Instructor, Google Certified Professional Cloud Architect, and Microsoft MTA on Python. Noah was selected to the SME Machine Learning team due to accomplishments in the area of Machine Learning on the AWS platform. He has published more than 100 technical publications, including several books on subjects ranging from Cloud Machine Learning to DevOps. Gift received an MBA from UC Davis, an M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo. Currently, he is consulting startups and other companies on Machine Learning, Cloud Architecture, and CTO-level consulting as the founder of Pragmatic AI Labs. His most recent book is Pragmatic AI: An Introduction to Cloud-Based Machine Learning (Pearson, 2018).

Skill Level
Beginning to Intermediate

What You Will Learn
Learn key concepts in Machine Language, AI, and cloud computing and how these technologies can be used in business assessment and growth
Meet the future head on with core coverage of AI, ML, and data science essentials
Distinguish between narrow, general, and super AI
Frame ML problems
Reason about Gradient Descent
Use neural networks
Understand social network theory
Who Should Take This Course

Roles:
Data scientist (current or aspiring)
ML engineer who wants a stronger conceptual foundation
Business exec who needs to understand AI and ML concepts
Student who needs additional resources in a data-related course
Course Requirements

Prerequisites:
Basic understanding of high-school math and linear algebra

Homepage

https://www.informit.com/store/pragmatic-ai-and-machine-learning-core-principles-livelessons-9780136554677

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