Dr. Ryan Ahmed, Ph.D., MBA
# Update 22/04/2021 – Added a new case study on AWS SageMaker Autopilot.
# Update 23/04/2021 – Updated code scripts and addressed Q&A bugs.
Machine and deep learning are the hottest topics in tech! Diverse fields have adopted ML and DL techniques, from banking to healthcare, transportation to technology.
AWS is one of the most widely used ML cloud computing platforms worldwide – several Fortune 500 companies depend on AWS for their business operations.
SageMaker is a fully managed service within AWS that allows data scientists and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.
In this course, students will learn how to create AI/ML models using AWS SageMaker.
Projects will cover various topics from business, healthcare, and Tech. In this course, students will be able to master many topics in a practical way such as: (1) Data Engineering and Feature Engineering, (2) AI/ML Models selection, (3) Appropriate AWS SageMaker Algorithm selection to solve business problem, (4) AI/ML models building, training, and deployment, (5) Model optimization and Hyper–parameters tuning.
The course covers many topics such as data engineering, AWS services and algorithms, and machine/deep learning basics in a practical way:
Data engineering: Data types, key python libraries (pandas, Numpy, scikit Learn, MatplotLib, and Seaborn), data distributions and feature engineering (imputation, binning, encoding, and normalization).
Instructor Details
Courses : 10
Specification: AWS SageMaker Practical for Beginners | Build 6 Projects
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11 reviews for AWS SageMaker Practical for Beginners | Build 6 Projects
Add a review
| Price | $11.99 |
|---|---|
| Provider | |
| Duration | 16 hours |
| Year | 2021 |
| Level | Intermediate |
| Language | English |
| Certificate | Yes |
| Quizzes | Yes |
$11.99
Ronit Marathe –
Great explanation of difficult concepts! The instructor makes understanding very easy and has covered complex models like ANN.
Aarthi Rajagopal –
The course content is very hands on, and this is a great way to learn AWS SageMaker.
Serdar Bozoglan –
Good instructor, clear explanations.
David Henderson –
Pitched as an intermediate course I was expecting greater detail, perhaps spreading itself too thin across ML concepts and AWS. There are far better courses, largely due to a singular focus, on ML. For AWS, SageMaker in particular, I expect as much would be gained by reading through the documentation and stack overflow as taking this course I have not taken any other courses on Udemy for AWS to compare or recommend. On a positive note, buying this course will provide some working examples of project code for spinning up pipelines in AWS using SageMaker. The hints to reduce cost are also very helpful. However, there was at least one exercise introducing leakage to the modelling process. The response to my query was to check whether this made a material difference to the accuracy of the resultant model. I should think that the principal is more important. Note for consideration of the above: I stopped the course at that point, approx 75% completed.
Abhigya –
Course content is amazing. This course is more focused towards data science part of model building. But nevertheless I am learning big concepts in simple language. Helps build a good portfolio of projects
Hossein Kordbacheh –
well on both theory and practical
Joe Boehm –
I am learning the mechanics of sagemaker well, and that’s what I really need to know. The math behind what it is doing is overwhelming to me.
Mahipal Gade –
yes. I am a beginner in AI with knowledge on python and basic understanding of AI concepts like image classification and regression
Jeff Anderson –
Great depth and clear explanations
Samuel Lincoln –
Excellent so far.
Lucas Pettit –
I’m forced to rate this course before i’ve even completed it. how am i supposed to really know??