Ultimate AWS Certified AI Practitioner AIF-C01
Unlock the Power of AI on AWS - Enroll in Our Ultimate Certification Course!
Are you ready to elevate your skills in artificial intelligence and explore the capabilities of AWS?
Join us for the "Ultimate AWS Certified AI Practitioner AIF-C01" course, where you'll gain the expertise to harness the full potential of AI technologies on the world's most comprehensive cloud platform.
Your Instructor
Alexandra Kropf is Mammoth Interactive's CLO and a software developer with extensive experience in full-stack web development, app development and game development. She has helped produce courses for Mammoth Interactive since 2016, including the Coding Interview series in Java, JavaScript, C++, C#, Python and Swift.
Mammoth Interactive is a leading online course provider in everything from learning to code to becoming a YouTube star. Mammoth Interactive courses have been featured on Harvard’s edX, Business Insider and more.
Over 12 years, Mammoth Interactive has built a global student community with 4 million courses sold. Mammoth Interactive has released over 350 courses and 3,500 hours of video content.
Founder and CEO John Bura has been programming since 1997 and teaching
since 2002. John has created top-selling applications for iOS, Xbox and
more. John also runs SaaS company Devonian Apps, building
efficiency-minded software for technology workers like you.
Course Curriculum
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Start01 Define basic AI terms (for example, AI, ML, deep learning, neural networks, computer vision, natural language processing [NLP], model, algorithm, training and inferencing, bias, fairness, fit, large language model [LLM]).
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Start02 Describe the similarities and differences between AI, ML, and deep learning.
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Start03 Describe various types of inferencing (for example, batch, real-time).
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Start04 Describe the different types of data in AI models (for example, labeled and unlabeled, tabular, time-series, image, text, structured and unstructured).
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Start05 Describe supervised learning, unsupervised learning, and reinforcement learning.
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Start01 Recognize applications where AI/ML can provide value (for example, assist human decision making, solution scalability, automation).
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Start02 Determine when AI/ML solutions are not appropriate (for example, cost-benefit analyses, situations when a specific outcome is needed instead of a prediction).
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Start03 Select the appropriate ML techniques for specific use cases (for example, regression, classification, clustering).
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Start04 Identify examples of real-world AI applications (for example, computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting).
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Start05 Explain the capabilities of AWS managed AI/ML services (for example, SageMaker, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly).