Free Online Course in “Simplifying Machine Learning Application Development” from edx

Edx website is offering a free online course in “Simplifying Machine Learning Application Development”. In this course, you will learn to integrate Machine Learning into your apps with training from AWS experts--and without a data science background. Learn from AWS Training and Certification expert instructors through lectures, demonstrations, discussions and hands-on exercises as the course explores this complex topic from the lens of the application developer.

About this Course:-

• Machine learning is one of the fastest growing areas in technology and a highly sought after skill set in today’s job market.
• This course will teach you, an application developer, how to use Amazon SageMaker to simplify the integration of Machine Learning into your applications. 
• Key topics include: an overview of Machine Learning and problems it can help solve, using a Jupyter Notebook to train a model based on SageMaker’s built-in algorithms and, using SageMaker to publish the validated model. 
• You will finish the class by building a serverless application that integrates with the SageMaker published endpoint.

What you'll Learn:-

• Key problems that Machine Learning can address and ultimately help solve.
• How to train a model using Amazon SageMaker’s built-in algorithms and a Jupyter Notebook instance.
• How to publish a model using Amazon SageMaker.
• How to integrate the published SageMaker endpoint with an application.

Course Syllabus:-

• Course Introduction

  • Welcome to Machine Learning with SageMaker on AWS.

  • Course Welcome and Student Information.

  • Meet the Instructors.

  • Introduce Yourself.

• Week 1

  • Introduction to Machine Learning with SageMaker on AWS.

    • Introduction to Week 1.

    • What we use ML for.

    • Diving Right In.

    • What is Amazon SageMaker.

  • WeeklyQuiz, Readings, Resources, Discussion.

    • Week 1 Notes and Resources.

    • Week 1 Quiz.

    • Week 1 Discussion.

• Week 2

  • Amazon SageMaker Notebooks and SDK.

    • Introduction to Week 2.

  • Amazon SageMaker Notebooks.

    • Introduction to Jupyter Notebooks.

    • Notebooks and Libraries: Cleaning and Preparing Data.

    • Exercise 2.1 Walkthrough.

    • Exercise 2.1: Create Your Notebook Instance (Optional).

  • Weekly Quiz, Readings, Resources, Discussion.

  • 
    

    • Week 2 Notes and Resources.

    • Week 2 Quiz.

    • Week 2 Discussion.

• Week 3

  • Amazon SageMaker Algorithms.

    • Introduction to Week 3.

  • ML and Amazon SageMaker Terminology.

    • SageMaker/ML Terminology and Algorithms.

    • Hyperparameter Tuning.

  • Amazon SageMaker Algorithms.

    • k-means Algorithm Walkthrough.

    • Introduction to Exercise 3.1.

    • Exercise 3.1: Using the k-means Algorithm (Optional).

    • XGBoost Algorithm Walkthrough (Part 1).

    • XGBoost Algorithm Walkthrough (Part 2).

    • XGBoost Algorithm Walkthrough (Part 3).

    • Introduction to Exercise 3.2

    • Exercise 3.2: Using the XGBoost Algorithm (Optional).

  • Weekly Quiz, Readings, Resources, Discussion.

    • Week 3 Notes and Resources.

    • Week 3 Quiz.

    • Week 3 Discussion.

• Week 4

  • Application Integration.

    • Introduction to Week 4.

  • Integrating Amazon SageMaker with your Applications.

    • Serverless Recap.

    • Exercise 4.1 Walkthrough.

    • Exercise 4.1: Python Movie Recommender (Optional).

    • Bring Your Own Models.

    • Bringing Your Own Models: MXNet and TensorFlow.

  • Weekly Quiz, Readings, Resources, Discussion.

    • Week 4 Notes and Resources.

    • Week 4 Quiz.

    • Class Wrap Up.

    • Course Survey.

    • Week 4 Discussion.

  • End of Course Assessment (Verified Certificate Track Only).

Conditions:-

• You should be good at English.
• The course is free but if you want to Pursue a Verified Certificate to highlight the knowledge and skills you gain, you should pay $99.00.
• The course needs 4 weeks with 2–4 hours of work per week.
• Prerequisites: 

  • Prior application development experience

  • Experience with the AWS Console

  • Recommended: AWS Developer Professional Series (Building on AWS, Deploying on AWS, Optimizing on AWS). 

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