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AWS Machine Learning Learning Path

AWS Machine Learning Learning Path

AWS AWS-MALE-LP

Free of Charge

Embark on a dynamic expedition into the realm of artificial intelligence and data science with our comprehensive AWS Machine Learning Learning Path Bundle. This thoughtfully curated bundle is tailored to empower both beginners and seasoned professionals with the skills and expertise needed to leverage AWS's cutting-edge machine learning services and drive transformative innovation.

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Free of Charge

Introduction to Machine Learning: Art of the Possible

Module 1

Introduction to Machine Learning: Art of the Possible

Introduction to Machine Learning: Art of the Possible

AWS LIS-AWSII-6079
Free of Charge
AWS
English
90 days
Languages Available: Deutsch | Español (Latinoamérica) | Français | ไทย | Italiano | 日本語 | 한국어 | Português (Brasil) | 中文（简体) | 中文（繁體) | Tiếng ViệtThis digital course is designed to help business decision makers understand the fundamentals of machine learning (ML). • Course level: Fundamental • Duration: 30 minutesActivitiesThis course includes presentations, videos, and knowledge assessments.Course objectivesIn this course, you will learn to: • Understand the basics of machine learning to help evaluate the benefits and risks associated with adopting ML in various business casesIntended audienceThis course is intended for: • Nontechnical business leaders and other business decision makers who are, or will be, involved in ML projects • Participants of the AWS Machine Learning Embark program, and Machine Learning Solutions Lab (MLSL) discovery workshopsPrerequisitesWe recommend that attendees of this course have: • Basic knowledge of computers and computer systems • Some basic knowledge of the concept of machine learningCourse outlineModule 1: How can machine learning help? • Define artificial intelligence • Define machine learning • Describe the different business domains impacted by machine learning • Describe the positive feedback loop (flywheel) that drives ML projects • Describe the potential for machine learning in underutilized marketsModule 2: How does machine learning work? • Describe artificial intelligence • Describe the difference between artificial intelligence and machine learningModule 3: What are some potential problems with machine learning? • Describe the differences between simple and complex models • Understand unexplainability and uncertainty problems with machine learning modelsModule 4: Conclusion
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Machine Learning for Business Challenges

Module 2

Machine Learning for Business Challenges

Machine Learning for Business Challenges

AWS LIS-AWSII-5676
Free of Charge
AWS
English
90 days
Languages Available: Deutsch | Español (Latinoamérica) | Español (España) | Français | Bahasa Indonesia | ไทย | Italiano | 日本語 | 한국어 | Português (Brasil) | 中文（简体) | 中文（繁體) | Tiếng ViệtMachine learning (ML) can help you solve business problems in ways that weren't possible before—but you've got to think big. Listen in as some of Amazon's own Machine Learning Scientists discuss how to make the most of ML. We'll cover ML terminology, business problems, use cases, and examples. By the end of this course, you'll have a better understanding of how to think about machine learning business challenges and decisions.
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The Elements of Data Science

Module 3

The Elements of Data Science

The Elements of Data Science

AWS LIS-AWSII-8411
Free of Charge
AWS
English
90 days
Languages Available: Deutsch | Español (Latinoamérica) | Español (España) | Français | Bahasa Indonesia | ไทย | Italiano | 日本語 | 한국어 | Português (Brasil) | 中文（简体) | 中文（繁體) | Tiếng ViệtLearn to build and continuously improve machine learning models with Data Scientist Harsha Viswanath, who will cover problem formulation, exploratory data analysis, feature engineering, model training, tuning and debugging, as well as model evaluation and productionizing.
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Math for Machine Learning

Module 4

Math for Machine Learning

Math for Machine Learning

AWS LIS-AWSII-6080
Free of Charge
AWS
English
90 days
Languages Available: Deutsch | Español (Latinoamérica) | Español (España) | Français | Bahasa Indonesia | ไทย | Italiano | 日本語 | 한국어 | Português (Brasil) | 中文（简体) | 中文（繁體) | Tiếng ViệtTo understand modern machine learning, you also need to understand vectors and matrices, linear algebra, probability theorems, univariate calculus, and multivariate calculus. This course, led by AWS Machine Learning Instructor Brent Werness, covers it all.
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Industry Partners

Amazon Web Services
Institution
AWS
The institution that is responsible for the courseware (and lab environment, if included) of this learning product.
E-learning Format
E-Learning
Language
English
Subscription duration
180 days
The time you will have access to the learning product once it has been activated.
Grace Period
90 days
Grace Period is the time between booking and automatic activation. After completing your order, you usually have 30 days until a product is automatically activated and the official subscription period begins.