AWS SageMaker Practical for Beginners | Build 6 Projects
Master AWS SageMaker Algorithms (Linear Learner, XGBoost, PCA, Image Classification) & Learn SageMaker Studio & AutoML
4.62 (2368 reviews)
17 032
students
16 hours
content
Jun 2024
last update
$84.99
regular price
What you will learn
Train and deploy AI/ML models using AWS SageMaker
Optimize model parameters using hyperparameters optimization search.
Develop, train, test and deploy linear regression model to make predictions.
Deploy production level multi-polynomial regression model to predict store sales based on the given features.
Develop a deploy deep learning-based model to perform image classification.
Develop time series forecasting models to predict future product prices using DeepAR.
Develop and deploy sentiment analysis model using SageMaker.
Deploy trained NLP model and interact/make predictions using secure API.
Train and evaluate Object Detection model using SageMaker built-in algorithms.
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Comidoc Review
Our Verdict
The AWS SageMaker Practical for Beginners course offers a strong foundation in machine learning concepts while introducing the AWS SageMaker platform. However, the outdated content and code errors can be frustrating for learners expecting seamless integration with the most recent version of SageMaker (2.0). Despite these challenges, learners appreciate the instructor's ability to clarify complex ideas in a way that makes concepts more accessible—ultimately making this course recommended for those willing to overlook its technical shortcomings.
What We Liked
- Covers a wide range of machine learning models and projects, from linear regression to image classification and sentiment analysis
- Instructor explains complex concepts in an easy-to-understand manner, making the course accessible for beginners
- Course provides well-structured Jupyter notebooks and example datasets that enhance the learning experience
- Instructor's clear explanations of ideas make this one of the better courses for understanding AI concepts
Potential Drawbacks
- Some content is outdated, leading to discrepancies between the course material and current AWS SageMaker interfaces
- Code provided in the course may produce errors due to updates in underlying libraries, requiring additional troubleshooting
- A significant portion of the course focuses on machine learning concepts and algorithms rather than AWS SageMaker-specific content
- Instructor's teaching style can be repetitive, leading to a slower pace that may not appeal to all learners
Related Topics
2907240
udemy ID
24/03/2020
course created date
22/05/2020
course indexed date
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