AWS Certified Machine Learning Specialty (ML-S) Crash Course
Published by Pearson
This is a preparation course for the AWS Certified Machine Learning – Specialty certification. Passing this exam is proof of your ability to build, train, tune, and deploy machine learning models using the AWS Cloud.
According to Amazon, this certification is intended for individuals who perform a development or data science role. It validates your skills to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.
What you’ll learn and how you can apply it
In this course you will learn:
- The most important machine learning algorithms
- The best practices to apply when solving a real-world machine learning problem
- SageMaker for model training, optimization, and hosting
- Manage and develop machine learning techniques by using Amazon products and technologies
- Integration of services into your application
- Hosting, scaling, and fault tolerance
- Streamline data management (Ingest, Catalog, Transform, and Visualize data)
- Provide a clean interface with Lambda and API Gateway
This live event is for you because...
- You would like to take your machine/deep learning and data science skills to the next level
- You would like to perform model training, optimization, and hosting on the reliable AWS
- You would like to prepare well for a certification that is recognized all over the world
- You would like to have a career as a highly qualified machine learning specialist
- You would like to work as a data engineer, analyst, or scientist
- These are the top five job titles for people who have this certification (according to https://www.youracclaim.com ): (1) Data Scientist (2) Senior Data Scientist (3) Machine Learning Engineer (4) Data Engineer (5) Software Engineer
Prerequisites
To get the most out of this course, attendees should have experience with AWS and Python, and have an AWS account. To brush up, review the following:
- Book: Learning Amazon Web Services (AWS): A Hands-On Guide to the Fundamentals of AWS Cloud, by Mark Wilkins https://www.oreilly.com/library/view/learning-amazon-web/9780135301104/
- Video: MTA 98-381: Introduction to Programming Using Python, by Brian Overland https://learning.oreilly.com/videos/mta-98-381-introduction/9780135301623
Recommended Preparation
- Learning Path: Amazon Web Services (AWS) Basics to Advanced. By Richard A Jones. https://www.oreilly.com/learning-paths/learning-path-amazon/9780135116548/
- Video: Essential Machine Learning and AI with Python and Jupyter Notebook. By: Noah Gift. https://learning.oreilly.com/videos/essential-machine-learning/9780135261118
- Live Online Training: AWS Machine Learning and Artificial Intelligence Primer by Noureddin Sadawi. Search the O’Reilly Learning Platform for an upcoming date
- AWS Certified Machine Learning-Specialty (ML-S). By Noah Gift. https://learning.oreilly.com/videos/aws-certified-machine/9780135556597/
Recommended Follow-up
- AWS Certified Machine Learning-Specialty (ML-S) (Pearson Practice Test)
- AWS Certified Machine Learning-Specialty (Pearson Practice Test): https://learning.oreilly.com/certifications/9780135954904/
- Live Online Training. AWS Certified Security - Specialty Crash Course, by Chad Smith. https://www.oreilly.com/live-training/courses/aws-certified-security-specialty-crash-course/0636920053667/
- Book: Data Just Right - Introduction to Large-Scale Data & Analytics, by Manoocherhri https://learning.oreilly.com/library/view/data-just-right/9780133359084/
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
DAY 1
Part 1 (60 mins)
- Review of core machine learning concepts
- Different methods for model evaluation
- Overview of the SageMaker Service
Q&A - 10 minutes
Break - 10 minutes
Part 2 (1 hour, 10 mins)
- Overview of the XGBoost algorithm
- Dimensionality reduction and the principal components analysis (PCA)
- Recommender systems and factorization machine (FM)
- Timeseries analysis and Amazon’s DeepAR
Q&A - 10 minutes
Break - 10 minutes
Part 3 (60 minutes)
- Model optimization and hyperparameter tuning
- Anomaly detection
- AI services on AWS
Q&A - 10 minutes
DAY 2
Part 1 (60 minutes)
- Data lake on AWS
- Deep learning and neural networks
Q&A - 10 minutes
Break - 10 minutes
Part 2 (60 minutes)
- Use your own algorithm on AWS
- AWS storage services
- Databases on AWS
Q&A - 10 minutes
Break - 10 minutes
Part 3 (1 hour, 10 mins)
- Machine learning speciality (MLS-C01) - exam overview
- Ideas, tips and tricks for exam preparation
- Practice questions
Q&A - 10 minutes
Course wrap up
Your Instructor
Noureddin Sadawi
Dr. Noureddin Sadawi is a consultant in machine/deep learning and data science. He has several years’ experience in various areas involving data manipulation and analysis. He received his PhD from the University of Birmingham, United Kingdom. He is the winner of two international scientific software development contests - at TREC2011 and CLEF2012.
Noureddin is an avid scientific software researcher and developer with a passion for learning and teaching new technologies. He is an experienced scientific software developer and data analyst; over the last few years he has been using Python as his preferred programming language. Also, he has been involved in several projects spanning a variety of fields such as bioinformatics, textual/image/video data analysis, drug discovery, omics data analysis and computer network security. He has taught at multiple universities in the UK and has worked as a software engineer in different roles. He is the founder of SoftLight LTD (https://www.softlight.tech/), a London-based company that specialises in data science and machine/deep learning. Recently, he has joined the University of Oxford as a part-time lecturer.