No-Code Machine Learning Using Amazon AWS SageMaker Canvas from Coursera

What's inside

Syllabus

Introduction to Machine Learning

In this module, we will introduce the basics of machine learning, covering fundamental concepts and applications. You will gain an understanding of what machine learning is and how it works, setting the foundation for the rest of the course.

Introduction to AWS

In this module, we will explore Amazon Web Services (AWS), the platform that powers SageMaker Canvas. You’ll learn what AWS is, its key services, and how to sign in to the AWS console for cloud-based machine learning activities.

Introduction to SageMaker

In this module, we will dive into Amazon SageMaker, a powerful tool for building and training machine learning models. You’ll also get introduced to SageMaker Canvas, the no-code interface that enables you to create models without needing programming skills.

Setup

In this module, we will walk through setting up your SageMaker domain and user environment. Additionally, you'll learn how to configure data in S3 Buckets, ensuring everything is ready for building machine learning models in SageMaker.

SageMaker Canvas Interface Walkthrough

In this module, we will explore the SageMaker Canvas interface, guiding you through its various features and functionalities. This walkthrough will help you efficiently navigate and use SageMaker Canvas for machine learning tasks.

Project 1 - Banknote Authentication

In this module, we will apply what we've learned to build a model for banknote authentication. You'll gather training data, build a predictive model, and validate its performance through batch prediction and accuracy testing.

Project 2 - Spam SMS Detection

In this module, we will focus on detecting spam SMS messages using machine learning. You’ll learn how to prepare your data, build a model, and evaluate its predictions to ensure it accurately detects spam.

Project 3 - Customer Churn Prediction

In this module, we will predict customer churn using machine learning. You'll import relevant customer data, build a predictive model, and assess its ability to forecast churn rates accurately.

Project 4 - Wine Quality Prediction

In this module, we will create a model to predict wine quality. You will work with datasets, build a model, and test its performance, learning how to combine multiple data sources for better results.

Assignment

In this module, you will complete an assignment where you predict white wine quality. This hands-on exercise will reinforce your learning and improve your ability to apply machine learning techniques using SageMaker Canvas.

Other Important Features in SageMaker Canvas

In this module, we will cover the versioning feature in SageMaker Canvas. You'll learn how to manage different versions of your models, ensuring you can track changes and improvements over time.

Congratulations and Next Steps

In this module, we will conclude the course with tips on obtaining more datasets, getting help with SageMaker Canvas, and congratulating you on completing the course. You'll also receive guidance on your next steps in mastering no-code machine learning.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a strong foundation in machine learning concepts and AWS SageMaker Canvas, enabling learners to build models without prior coding experience

Teaches how to leverage machine learning to solve problems efficiently using AWS SageMaker Canvas, which can improve decision-making and streamline processes

Explores Amazon Web Services (AWS) and its key services, providing practical experience with cloud-based machine learning activities using SageMaker Canvas

Recommends basic familiarity with cloud platforms like AWS, which may require learners to acquire additional knowledge before fully benefiting from the course

Focuses on a no-code approach to machine learning, which may not be suitable for learners seeking in-depth knowledge of coding and algorithms

Engages with four machine learning projects, including spam SMS detection, customer churn prediction, and wine quality prediction, demonstrating practical applications

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in No-Code Machine Learning Using Amazon AWS SageMaker Canvas with these activities:

Review Machine Learning Fundamentals

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Solidify your understanding of core machine learning concepts before diving into the no-code approach.

Browse courses on Machine Learning Fundamentals

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Review key concepts like supervised and unsupervised learning.
Familiarize yourself with common ML algorithms.
Understand the basics of model evaluation metrics.

Brush Up on AWS Basics

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Gain a basic understanding of AWS services to better navigate SageMaker Canvas.

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Learn about AWS S3 for data storage.
Understand the AWS Management Console.
Familiarize yourself with AWS IAM for user management.

Explore SageMaker Canvas Tutorials

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Follow online tutorials to get hands-on experience with SageMaker Canvas features.

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Find tutorials on data import and preparation.
Practice building a simple model using Canvas.
Experiment with different prediction types.

Four other activities

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Review: Practical Machine Learning with Python

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Gain a deeper understanding of the underlying machine learning concepts.

View Putting Knowledge to Work on Amazon

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Read the chapters on model evaluation and selection.
Focus on the sections related to the algorithms used in the course.

Review: Data Science from Scratch

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Reinforce your understanding of the mathematical foundations of machine learning.

View Data Science from Scratch: First Principles... on Amazon

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Review the chapters on statistics and probability.
Focus on the sections related to linear algebra and calculus.

Personal Project: Predict Housing Prices

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Apply your SageMaker Canvas skills to a real-world problem by predicting housing prices.

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Find a suitable housing price dataset online.
Import and prepare the data in SageMaker Canvas.
Build a model to predict housing prices.
Evaluate the model's performance and iterate.

Blog Post: My No-Code ML Journey

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Document your experience with no-code machine learning and share your insights with others.

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Outline your blog post structure.
Describe your experience with SageMaker Canvas.
Share tips and tricks for no-code ML.
Publish your blog post on a platform like Medium.

Career center

Learners who complete No-Code Machine Learning Using Amazon AWS SageMaker Canvas will develop knowledge and skills that may be useful to these careers:

Machine Learning Specialist

A machine learning specialist uses their skills to develop and implement machine learning models. This course focusing on no-code machine learning with Amazon AWS SageMaker Canvas directly applies to the work of a machine learning specialist, especially those who prefer to work with a no code environment. The course provides experience in all stages of the machine learning workflow from data preparation to model building and validation, including working with projects in banknote authentication, spam detection, customer churn, and wine quality. Completing this course enables the specialist to effectively utilize SageMaker Canvas to build and deploy models without coding.

See salaries and explore the career path for Machine Learning Specialist

Data Scientist

The role of a data scientist involves analyzing large sets of data, extracting insights, and building predictive models. This course introduces AWS SageMaker Canvas which helps a data scientist quickly create and validate machine learning models using a no code environment. The course projects, such as spam detection and customer churn prediction, provide a practical understanding of real world applications of machine learning. A data scientist that wishes to use SageMaker Canvas should take this course to gain experience with setting up environments, data management, model creation, and model evaluation. The course will help build a foundation in no code workflows.

See salaries and explore the career path for Data Scientist

Business Intelligence Developer

A business intelligence developer uses data, analytics, and machine learning to create reports, dashboards, and visualizations that inform business strategy. This course may be useful as machine learning models are of increasing importance in BI. The course covers data management, building predictive models, and validating accuracy within the context of AWS SageMaker Canvas. The project-based learning, including customer churn prediction, provides hands-on experience in building models that address common business challenges. The skills gained in no code model development will enable a business intelligence developer to produce more advanced analyses and insights.

See salaries and explore the career path for Business Intelligence Developer

Data Analyst

A data analyst interprets data and applies analytical techniques to provide insights to stakeholders and help with decision making. This course may be useful because machine learning techniques are increasingly a part of a data analyst's tool kit. The course covers working with data, building models, and performing predictions within the no code environment of AWS SageMaker Canvas. The projects, from spam SMS detection to wine quality prediction, will familiarize a data analyst with machine learning workflows and modeling. A data analyst can leverage this course to complement their analytical skills.

See salaries and explore the career path for Data Analyst

Analytics Consultant

An analytics consultant works with organizations to solve business problems through the use of data analysis and reporting. This course may be useful to an analytics consultant, as it teaches no code machine learning techniques. This course on SageMaker Canvas helps analytics consultants build predictive models that generate insights from datasets. The projects on spam detection, customer churn, and wine quality will improve the consultant's ability to apply machine learning to business problems. The consultant can better advise clients by gaining skills in working within the no code environment of SageMaker Canvas.

See salaries and explore the career path for Analytics Consultant

Product Manager

A product manager works to define and guide solutions for product development. This course may be useful to improve the product manager's understanding of machine learning. The course on SageMaker Canvas gives a product manager an understanding of how machine learning models are created and how they can be applied to solve problems. The projects, such as predicting customer churn and wine quality, will help a product manager understand possible use cases for machine learning. A product manager can gain a deeper appreciation for the capabilities of machine learning by completing this course.

See salaries and explore the career path for Product Manager

Project Manager

A project manager oversees data science or machine learning projects, schedules milestones, and manages teams. This course may be useful to a project manager who wants to gain a deeper understanding of the tools and techniques that their team is using. The course familiarizes project managers with SageMaker Canvas and the no code methods of creating machine learning models. The various projects completed, such as spam detection and wine quality prediction, will give a project manager context for machine learning applications. Completing this course will improve the project manager's resource allocation and strategic planning.

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Technology Consultant

A technology consultant advises organizations on how to best leverage technology to reach their goals. This course may be useful as machine learning is an increasingly important part of technological strategy. The course introduces AWS SageMaker Canvas and allows a technology consultant to experiment with machine learning through a no code platform. The projects such as banknote authentication, spam SMS detection, customer churn prediction, and wine quality prediction give consultants examples of how machine learning can be applied to solve real world problems. By completing this course, the consultant will develop a practical understanding of the possibilities of machine learning.

See salaries and explore the career path for Technology Consultant

Market Research Analyst

A market research analyst studies market trends to inform product and marketing strategies. This course may be useful because machine learning models are increasingly critical in market analysis. This course introduces the AWS SageMaker Canvas platform and provides the skills to build and validate machine learning models within a no code environment. This course's practical projects, such as customer churn prediction, will inform a market research analyst how machine learning techniques can enhance market research methodologies. The analyst may leverage this course to gain a better understanding of data driven analysis.

See salaries and explore the career path for Market Research Analyst

Quantitative Analyst

A quantitative analyst applies mathematical and statistical methods to finance and risk management. This course may be useful as machine learning is becoming more important in quantitative analysis. The course introduces AWS SageMaker Canvas and its no code approach to building predictive models. The course projects, such as wine quality prediction, provide an understanding of how machine learning models can be built and validated. Completing this course can help build a foundation in machine learning techniques applicable to quantitative analysis.

See salaries and explore the career path for Quantitative Analyst

Research Associate

A research associate helps conduct research within a variety of academic, nonprofit, or private settings. This course may be useful because machine learning is an increasingly important aspect of research methods. This course covers AWS SageMaker Canvas and demonstrates how to set up an environment and apply machine learning. The projects, such as banknote authentication, spam SMS detection, and wine quality prediction, may help a research associate understand how these techniques can be applied in their research. This course helps the research associate apply machine learning in an efficient no code way.

See salaries and explore the career path for Research Associate

Financial Analyst

A financial analyst reviews financial data and provides recommendations to an organization on how to improve financial health. This course may be useful as machine learning is becoming an important tool in financial analysis. The course details how to set up an environment and create models using AWS SageMaker Canvas. The project based learning, such as predicting customer churn, gives them an understanding of how predictive models are built and validated. By completing this course, a financial analyst might better use machine learning to enhance their financial analysis.

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Operations Analyst

An operations analyst seeks to improve the efficiency and cost effectiveness of an organization's operations. This course may be useful because machine learning models can help identify bottlenecks and improve efficiency. This course demonstrates how to build models on the AWS SageMaker Canvas platform without writing code. The projects on spam SMS detection and customer churn prediction offer operations analysts practical experience in using machine learning to solve problems in operations. This course may help the operations analyst improve their analyses.

See salaries and explore the career path for Operations Analyst

Marketing Analyst

A marketing analyst measures the effectiveness of marketing campaigns and identifies new opportunities. This course may be useful, as machine learning is increasingly important in marketing analysis. The course introduces AWS SageMaker Canvas and how to set up an environment and build predictive models. The course projects, such as predicting customer churn, provide an example of how machine learning can help a marketing analyst. By completing this course, a marketing analyst may be able to improve their work with the aid of data driven machine learning.

See salaries and explore the career path for Marketing Analyst

Statistician

A statistician applies statistical methods to collect and analyze data. This course may be useful for a statistician who may wish to integrate machine learning into their tool kit. The course covers the features of AWS SageMaker Canvas and how to build machine learning models without writing code. The projects, such as predicting customer churn and wine quality, will give a statistician a view of how machine learning models work. A statistician could use this as a way to enter the field of machine learning.

See salaries and explore the career path for Statistician

No-Code Machine Learning Using Amazon AWS SageMaker Canvas

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