Master AWS Machine Learning: Score 900+ on Certification!

Table of Contents

1. Introduction
2. My Journey to AWS Certified Machine Learning Specialty Exam
3. Learning the Fundamentals with Andrew's Course
4. Applying Machine Learning Knowledge at Work
5. AWS Side of the Exam Preparation
   • 5.1 A Cloud Guru's Course by Brock Tubre and Scott Pletcher
   • 5.2 Linux Academy's Course by Mike Chambers
   • 5.3 Stefane Marek's Course with Frank Kane's Expertise
6. Practical Projects and Kaggle Competitions
7. Final Tips and Advice
8. Conclusion

Introduction

In this article, I will be discussing my experience with the AWS Certified Machine Learning Specialty Exam. Unlike other exams, this one tests not only your knowledge of using AWS but also your understanding of machine learning concepts. I will share the resources I used to prepare for the exam, hands-on preparation techniques, and strategies for obtaining a high score and comprehensive understanding of the exam content. Passing this exam is not just about cramming information; it is about verifying and showcasing your skills.

My Journey to AWS Certified Machine Learning Specialty Exam

Before diving into the exam preparation, let me give You some background on where I was before I started studying for the AWS Machine Learning Specialty Exam. A few months ago, I had some experience with machine learning, but it was not structured. I had worked with TensorFlow and built simple machine learning models. Additionally, I had created a visualizer for support vector machines as a side project. However, my knowledge was limited to the basics of neural networks. In essence, I was a confident beginner seeking a deeper understanding of the subject.

Learning the Fundamentals with Andrew's Course

To solidify my foundation in machine learning, I opted for a course called "Introduction to Machine Learning" by Andrew. Andrew is a highly knowledgeable instructor who has worked at Google Brain. In this course, Andrew covers the fundamentals of machine learning, starting with linear regressions and logistic regressions. He gradually progresses to more complex models such as decision trees, boosted decision trees, and neural networks. He also explores their applications in signal processing, image classification, and object detection. This course provided me with a strong understanding of the basics and allowed me to work on actionable projects.

Applying Machine Learning Knowledge at Work

During my internship with an Australian bank, I had the opportunity to work on a team that handles property price predictions. Our system, which is the most accurate in Australia, predicts house prices Based on various features. My role involved working on models that analyze property images to identify objects of interest that impact the sale price. For example, the presence of air conditioners increases a house's value. Although I am bound by an NDA, I can say that this project required a comprehensive understanding of machine learning concepts and extended beyond what I have described. This hands-on experience enabled me to Apply my knowledge practically and enhance my skills further.

AWS Side of the Exam Preparation

To prepare for the AWS Certified Machine Learning Specialty Exam specifically, I utilized several online courses. Let's explore each of them.

5.1 A Cloud Guru's Course by Brock Tubre and Scott Pletcher

I began with the course on A Cloud Guru taught by Brock Tubre and Scott Pletcher. This course provided an excellent introduction to the exam's content. However, I found that it did not make me feel entirely confident about the exam. At the time, I was unaware of other alternatives, so I initially booked the exam.

5.2 Linux Academy's Course by Mike Chambers

Realizing that I needed more in-depth knowledge, I discovered the course on Linux Academy presented by Mike Chambers. This course delved into a variety of practical demonstrations and tackled different aspects of machine learning. One notable demonstration involved identifying a specific object, a plushy penguin named Pinehead, within images, similar to the "hot dog or not" app featured in the TV Show Silicon Valley. This hands-on approach greatly enhanced my understanding of the machine learning process and its applications.

5.3 Stefane Marek's Course with Frank Kane's Expertise

Lastly, I enrolled in Stefane Marek's course, which exceeded my expectations. Stefane is a thorough instructor who not only prepares you for the exam but also equips you with practical knowledge for real-life scenarios. For this course, he brought in Frank Kane, a machine learning expert, to instruct on the intricacies of machine learning on AWS. This collaboration enhanced the course's content and provided valuable insights into the nuances of machine learning.

Practical Projects and Kaggle Competitions

To solidify my understanding and gain practical experience, I engaged in various projects outside of the courses. Kaggle, a platform offering diverse data sets and competitions, proved to be an excellent resource. For instance, one competition involved predicting the survival of passengers on the Titanic based on their characteristics. Through this project, I built a model that achieved an accuracy rate of around 80-82%. Kaggle offers numerous opportunities to work on classification and regression problems, enabling aspiring machine learning practitioners to apply their knowledge effectively.

Final Tips and Advice

To succeed in the AWS Certified Machine Learning Specialty Exam, it is crucial to apply the knowledge acquired and not solely rely on lectures. Practical projects are an essential aspect of exam preparation. If you do not have the opportunity to work on projects related to your job, there are plenty of alternatives available. Websites like Kaggle provide a platform to dive into various data science and machine learning projects. You don't need to be an expert; having a Core understanding and knowing the principles will enable you to improve your models and find solutions.

Conclusion

Preparing for and passing the AWS Certified Machine Learning Specialty Exam is challenging but entirely achievable. By following a structured learning path, such as the courses I Mentioned, and actively working on practical projects, anyone with minimal experience can succeed. Dedication, hard work, and the application of knowledge are key factors in attaining a good score. Best of luck on your exam journey, and remember that this credential is a testament to your machine learning skills.