Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep
*** NOV-2020 NEW: Nuts and Bolts of Optimization, quizzes ***
*** NOV-2020 All code examples and Labs were updated to use version 2.x of the SageMaker Python SDK ***
*** SEP-2020 Anomaly Detection with Random Cut Forest - Learn the intuition behind anomaly detection using Random Cut Forest. With labs. ***
*** APR-2020 Bring Your Own Algorithm - We take a behind the scene look at the SageMaker Training and Hosting Infrastructure for your own algorithms. With Labs ***
*** JAN-2020 Timed Practice Test and additional lectures for Exam Preparation added
For Practice Test, look for the section: 2020 Practice Exam - AWS Certified Machine Learning Specialty
For exam overview, gap analysis and preparation strategy, look for 2020 - Overview - AWS Machine Learning Specialty Exam
There are several courses on Machine Learning and AI. What is unique about this course?
Here are the top reasons:
1. Cloud-based machine learning keeps you focused on the current best practices.
2. In this course, you will learn the most useful algorithms. Don’t waste your time sifting through mountains of techniques that are in the wild
4. Cloud-based service is straightforward to integrate with your application and has support for a wide variety of programming languages.
5. Whether you have small data or big data, the elastic nature of the AWS cloud allows you to handle them all.
6. There is also No upfront cost or commitment – Pay only for what you need and use
In this course, you will learn with hands-on labs and work on exciting and challenging problems
What exactly will you learn in this course?
Here are the things that you will learn in this course:
* You will learn how to deploy a Notebook instance on the AWS Cloud.
* You will gain insight into algorithms provided by SageMaker service
* Learn how to train, optimize and deploy your models
In the AI Services section of this course,
* You will learn about a set of pre-trained services that you can directly integrate with your application.
* Within a few minutes, you can build image and video analysis applications – like face recognition
* You can develop solutions for natural language processing, like finding sentiment, text translation, and conversational chatbots.
* Learning algorithms is one part of the story - You need to know how to integrate the trained models in your application.
* You will learn how to host your models, scale on-demand, handle failures
* Provide a clean interface for the applications using Lambda and API Gateway
* Data management is one of the most complex and time-consuming activities when working on machine learning projects.
* With AWS, you have a variety of powerful tools for ingesting, cataloging, transforming, securing, visualization of your data assets.
* We will build a data lake solution in this course.
Machine Learning Certification
* If you are planning to get AWS Machine Learning Specialty Certification, you will find all the resources that you need to pass the exam in this course.
* Timed Practice Exam and Quizzes
* The source code for this course available on Git and that ensures you always get the latest code
* The ideal student for this course is willing to learn, participate in the course Q&A forum when you need help, and you need to be comfortable coding in Python.
My name is Chandra Lingam, and I am the instructor for this course.
I have over 50,000 thousand students
I spend a considerable amount of time keeping myself up-to-date and teach cloud technologies from the basics.
I have the following AWS Certifications: Solutions Architect, Developer, SysOps, Solutions Architect Professional, Machine Learning Specialty.
I am looking forward to meeting you.