AWS Certified Machine Learning Specialty MLS-C01 [2024]

Experience AWS SageMaker: A Practical Course with Hands-On Learning, Practice Tests and Certification Preparation

4.50 (3738 reviews)

Udemy

platform

English

language

Data Science

What you will 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)

Zero Downtime Model Deployment

How to Integrate and Invoke ML from your Application

Automated Hyperparameter Tuning

Why take this course?

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

Welcome to AWS Machine Learning Specialty Course!

In this course, you will gain practical experience with AWS SageMaker through hands-on labs that demonstrate specific concepts. We will begin by setting up your SageMaker environment. If you are new to machine learning, you will learn how to handle mixed data types, missing data, and how to verify the quality of the model. These topics are essential for machine learning practitioners and the certification exam.

SageMaker uses containers to package algorithms and frameworks, such as Pytorch and TensorFlow. The container-based approach provides a standard interface for building and deploying your models, and it is easy to convert your model into a production application. Through a series of concise labs, you will train, deploy, and invoke your first SageMaker model.

Like any other software project, a machine-learning solution also requires continuous improvement. We will look at how to safely incorporate new changes in a production system, perform A/B testing, and even roll back changes when necessary, all with zero downtime to your application.

We will also discuss emerging social trends in the fairness of machine learning and AI systems. What will you do if your users accuse your model of being racially or gender-biased? How will you handle it? In this section, we will cover the concept of fairness, how to explain a decision made by the model, different types of bias, and how to measure them.

We will also cover cloud security and how to protect your data and model from unauthorized use. You will learn about recommender systems and how to incorporate features such as movie and product recommendations. The algorithms you learn in the course are state-of-the-art, and tuning them for your dataset can be challenging. We will look at how to tune your model with automated tools, and you will gain experience in time series forecasting, anomaly detection, and building custom deep-learning models.

With the knowledge you gain in this course, and the included high-quality practice exam, you will be well-prepared to achieve the AWS Certified Machine Learning - Specialty certification. I am looking forward to meeting you and helping you succeed in this course. Thank you!

Screenshots

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Our review

🌟 **Overall Course Rating:** 4.46 ### Course Review **Pros:** - 🚀 **Comprehensive Content**: The course offers a thorough understanding of machine learning applications on AWS, covering both theoretical and practical aspects ([Content 1](#), [Content 2](#), [Content 3](#)). - 💬 **Interactive Learning**: The instructor's responsiveness to questions, especially regarding AWS console changes, indicates a dynamic learning environment that adapts to real-world updates ([Content 7](#), [Content 10](#)). - ✅ **Exam Preparation**: The course is designed to prepare students for the AWS ML Specialty exam, with content that aligns closely with the topics covered in the exam ([Content 4](#), [Content 8](#), [Content 12](#)). - 📚 **Rich Learning Materials**: Students have access to detailed PDF presentations and numerous example notebooks for practical application and last-minute revision ([Content 11](#), [Content 14](#)). - 🤝 **Community Support**: The course likely fosters a community where students can interact, share knowledge, and support each other in their learning journey. **Cons:** - 📈 **Keeping Pace with AWS Updates**: While the course is rich in content, staying up-to-date with the rapidly evolving AWS services could be a challenge that needs continuous attention ([Content 13](#)). - 🎙️ **Presentation Style**: Some learners might find the monotone delivery of lectures less engaging and prefer a more dynamic or varied presentation style ([Content 10](#)). - 🤔 **Concept Explanation**: There is a suggestion that more time could be dedicated to explaining concepts before diving into practical examples using notebooks ([Content 15](#)). ### Course Highlights: - A solid foundation in cloud concepts and machine learning principles. - Detailed coverage of AWS SageMaker and best practices like spot training. - A final revision document that encapsulates all essential topics with precision. - Real-world examples and quizzes that match the lecture content, reinforcing learning. - A community of learners who have successfully passed the AWS ML Specialty exam. ### Testimonials: - Success stories from students who have cleared the AWS ML Specialty exam with scores as high as 955 ([Content 6](#), [Content 13](#)). - Positive feedback on the course's depth and quality of information, describing it as "great" and "solid" ([Content 9](#), [Content 11](#)). ### Recommendations for Improvement: - Incorporate Improv training or offer tips for maintaining engagement in longer lectures ([Content 10](#)). - Ensure the course content is updated regularly to reflect the latest AWS services and features ([Content 13](#)). - Possibly integrate more varied teaching methods to cater to different learning styles ([Content 15](#)). ### Conclusion: This course is highly recommended for individuals aiming to gain a deep understanding of machine learning on AWS and prepare for the AWS ML Specialty certification. With its rich content, responsive instructor, and community support, it stands out as a valuable resource in the field of cloud computing and machine learning. However, students are encouraged to stay informed about the latest AWS developments to ensure their knowledge remains relevant and up-to-date.

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