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Amazon AWS SageMaker, AI and Machine Learning with Python Course

(12 customer reviews)
Product is rated as #10 in category Data Science

What you’ll learn

  • You will gain first-hand experience on how to train, optimize, deploy, and integrate ML in AWS cloud
  • AWS Built-in algorithms, Bring Your Own, Ready-to-use AI capabilities
  • Complete Guide to AWS Certified Machine Learning – Specialty (MLS-C01)
  • Includes a high-quality Timed practice test (a lot of courses charge a separate fee for practice test)

Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep

*** NEW Labs – A/B Testing, Multi-model endpoints ***

*** NEW section Emerging AI Trends and Social Issues. How to detect a biased solution, ensure model fairness and prove the fairness ***

*** New Endpoint focused section on how to make SageMaker Endpoint Changes with Zero Downtime ***

*** Lab notebook now use spot-training as the default option. Save over 60% in training costs ***

*** NEW: Nuts and Bolts of Optimization, quizzes ***

*** All code examples and Labs were updated to use version 2.x of the SageMaker Python SDK ***

*** Anomaly Detection with Random Cut Forest – Learn the intuition behind anomaly detection using Random Cut Forest.  With labs. ***

*** Bring Your Own Algorithm – We take a behind the scene look at the SageMaker Training and Hosting Infrastructure for your own algorithms. With Labs ***

*** Timed Practice Test and additional lectures for Exam Preparation added

Welcome to AWS Machine Learning Specialty Course!

I am Chandra Lingam, and I am your instructor

In this course, you will gain first-hand SageMaker experience with many hands-on labs that demonstrates specific concepts

We start with how to set up your SageMaker environment

If you are new to ML, you will learn how to handle mixed data types, missing data, and how to verify the quality of the model

These topics are very important for an ML practitioner as well as for the certification exam

SageMaker uses containers to wrap your favorite algorithms and frameworks such as Pytorch, and TensorFlow

The advantage of a container-based approach is it provides a standard interface to build and deploy your models

It is also straightforward to convert your model into a production application

In a series of concise labs, you will in fact train, deploy, and invoke your first SageMaker model

Like any other software project, ML Solution also requires continuous improvement

We look at how to safely incorporate new changes in a production system, perform A/B testing, and even rollback changes when necessary

All with zero downtime to your application

We then look at emerging social trends on the fairness of Machine learning and AI systems.

What will you do if your users accuse your model as racially biased or gender-biased? How will you handle it?

In this section, we look at the concept of fairness, how to explain a decision made by the model, different types of bias, and how to measure them

We then look at Cloud security – how to protect your data and model from unauthorized use

You will also learn about recommender systems to incorporate features such as movie and product recommendation

The algorithms that you learn in the course are state of the art, and tuning them for your dataset is especially challenging

So, we look at how to tune your model with automated tools

You will gain experience in time series forecasting

Anomaly detection and building custom deep learning models

With the knowledge, you gain here and the included high-quality practice exam, you will easily achieve the certification!

And something unique that I offer my students is a weekly study group meeting to discuss and clarify any questions

I am looking forward to seeing you!

Thank you!

Who this course is for:

  • This course is designed for anyone who is interested in AWS cloud based machine learning and data science
  • AWS Certified Machine Learning – Specialty Preparation

12 reviews for Amazon AWS SageMaker, AI and Machine Learning with Python Course

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  1. Sakthivel

    Used this course a basic guide for the AWS MLS exam. The code for this course, is maintained up-to-date in GIT, this helps doing hands-on exercises easy and serves as a reference material for doing similar use-cases. I used to get invitation for group live Q&A session with instructor but was never able to make it due to my time zone difference. The new sections added to the course recently are helpful. Thanks to Chandra for keeping the course material current. ML is a field of study and getting updated every day. In case you are planning to take on the AWS MLS exam, you’ll have to navigate to the various links (mostly from youtube) that are given in this course, learn relevant topics from there-on and the come-back to the course. Go thru AWS Whitepapers, blogs and AWS Machine-Learning section.
    My two cents of feedback, The end of course full question paper is very easy compared to the real test, the difficulty level can be increased to match the questions from test.
    Passed the AWS MLS test during mid Aug 21.

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  2. Peggy Chang

    I passed this exam last week and has written a review about it (https://towardsdatascience.com/aws-certified-machine-learning-specialty-97eacbd1a0fe). I am very impressed with the quality of this course. I particularly love the many hands-on labs and use cases presented to demonstrate specific concepts or implementations. This makes the course contents extra interesting and easy to understand, instead of just learning theories.

    Chandra has been actively maintaining and keeping the course materials up-to-date. I actually completed the course in late March this year, but I delayed my plan on taking the exam. When I went through the course again in September, I was surprised that new topics have been added, which I find all relevant and useful. I also appreciate his sharing of important points or discussions that were brought up in the Q&A forum. This is very good since not all students will read the Q&A forum.

    Well done, Chandra. Keep up the good work. All these reflects your dedication and effort that you’ve put in to help students learn and pass the exam. Thank you very much for this awesome course, I enjoyed every minute of it.

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  3. Hua Yang

    The course helped me with my certification! it has a lot of practical ML examples. it is updated with most of recent AWS ML application development. It has very detailed detailed explanation on ML model deployment architectures.

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  4. Sathya Shanmugavlu

    Excellent Hands on course on AWS Sagemaker ML

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  5. Cheuk Kin POON

    This course contains a lot of low level instructions in the lectures like “type this”, “click that”… etc. which I would consider not helpful. What I am truly looking for is a course that teaches high level concepts, architecture and design of AWS service so I have the knowledge that is useful when I want to create my own custom project. However this course contains very little bit of that.

    I have finished over half of the course and it seems to me that this course is mostly good if I was looking for some code to copy and paste over.

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  6. Preetha Govindarajan

    YES. Concepts on the cloud part were explained well. As I am preparing for the exam, I found this very very useful. With hands on part on Sagemaker, its quite interesting.

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  7. Kamal Balakrishnan

    This is amazing course really helped me to get hands on AWS algorithms and focusing on certification. This is definitely my template store for trying out different algorithms and hosting models. I passed the exam this week but it was bit of hard and the questions are tough (about 70%). I had machine learning courses and practice from multiple sources – udemy, cloud guru etc but the questions are little hard – more questions on deep learning layers and weights change, not direct questions but how to use tools metrics (Experiments, Debugger, Model monitor), questions on Auto ML and surprisingly few direct questions about ~5 and only few (~2) from Kinesis.

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  8. Salim Eryigit

    very good course with hands on labs and sample code. really satisifed with my experience.

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  9. Rana Masood Iqbal

    Hi Chandra, I’m once again very thankful to you for your ML Specialty (Previously for Security Specialty) course it helped me a lot to understand the ML ecosystem in general & in AWS, & by the way your course is such an amazing that I passed my ML Specialty exam today.

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  10. NATARAJ SALA

    GREAT WORK SIR, GREAT PDF’S AND QUIZ ARE GREAT, PERFECT EVERYTHING

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  11. Jeff Bach

    This is one of the most clear and well-organized online courses I have ever taken. Very easy to follow, with focused hands-on exercises. This was very helpful in passing my certification exam on first attempt with one month of preparation.

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  12. Josef Heiss

    A very good course. The instructor’s explanations are very helpful. Only, sometimes his monotonic voice made it herd to stay focused.

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    Amazon AWS SageMaker, AI and Machine Learning with Python Course
    Amazon AWS SageMaker, AI and Machine Learning with Python Course

    $159.99

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