AWS SageMaker Practical for Beginners | Build 6 Projects
Why take this course?
🎉 AWS SageMaker Practical for Beginners: Master AI/ML & Build 6 Projects 🎓
Course Instructor: Dr. Ryan Ahmed, Ph.D., MBA
Total Course Updates: Last updated on 23/04/2021 with new case studies and code script improvements.
Unlock the Power of AI/ML with AWS SageMaker 🚀
Machine and Deep Learning are revolutionizing industries across the globe! From fintech to healthcare, transportation to technology, these technologies are transforming the way we live and work.
As a leading cloud platform in ML, AWS (Amazon Web Services) is the backbone for numerous Fortune 500 companies. Its SageMaker service stands out by enabling data scientists and AI practitioners to swiftly build, train, and deploy machine learning models.
Your Journey with AWS SageMaker Begins Here! 🛠️
In this comprehensive course, you'll dive into the world of AI/ML using AWS SageMaker. You'll learn hands-on by working on projects that cover a wide range of practical applications in diverse sectors.
Course Highlights:
🧠 Data Engineering and Feature Engineering:
- Understand key Python libraries (Pandas, Numpy, Scikit Learn, MatplotLib, Seaborn).
- Learn data distributions and master feature engineering techniques like imputation, binning, encoding, and normalization.
🚀 AWS Services and Algorithms:
- Gain proficiency in Amazon SageMaker with linear learner, XGBoost, PCA, image classification, and more.
- Explore AWS S3 Storage services for data storage needs.
- Utilize SageMaker Studio and AutoML to streamline your AI/ML workflows.
🔬 Machine and Deep Learning Fundamentals:
- Discover various neural network types (ANNs, CNNs).
- Learn about activation functions, machine learning training strategies, and evaluation metrics like precision, recall, F1-score, and RMSE.
- Understand ensemble learning, decision trees, random forests, and more.
Practice-Driven Projects:
📈 Project #1: Predict employee salaries using AWS SageMaker Linear Learner. 🏥 Project #2: Estimate medical insurance premiums with a multiple linear regression model. 🛒 Project #3: Forecast retail store sales using XGboost and hyperparameter tuning. 🔬 Project #4: Conduct dimensionality reduction with PCA and predict cardiovascular disease using an XGBoost classification model. 🚦 Project #5: Build a traffic sign classifier using SageMaker and Tensorflow. 🧩 Project #6: Get hands-on with AWS SageMaker Studio, AutoML, and debugging models.
Who is this course for?
This course is designed for:
- Beginners in Data Science who are eager to enhance their careers by building a strong portfolio.
- Seasoned Consultants aiming to leverage AI/ML in transformative business solutions using SageMaker.
- Tech Enthusiasts new to the field of AI and ML, seeking practical experience with AWS SageMaker.
Join Us and Elevate Your Data Science Skills! 🌟
Enroll in this course today and take your first step towards mastering AWS SageMaker and solving real-world problems with AI/ML. Let's embark on this transformative journey together! 🚀✨
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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