What is <MLEP>?
The Machine Learning Engineering Program
Overview
The program is designed and optimized to prepare data scientists, machine learning engineers, DevOps engineers, and software engineers for the AWS Certified Machine Learning – Specialty exam in seven (7) sessions.
1 x Statistical Foundations and Machine Learning Concepts
7 x Machine Learning Algorithms
2 x Model Evaluation and Optimization
4 x AWS Machine Learning and Data Engineering Services
3 x Data Preparation and Feature Engineering
6 x Amazon SageMaker Deep Dive
Prerequisites
Proficiency in at least one programming language (e.g., Python, JavaScript, Java, Ruby, R, PHP, C#) is recommended.
No prior experience on machine learning libraries, tools, frameworks, engines, services, and platforms such as Amazon SageMaker, scikit-learn, Tensorflow, PyTorch, and MXNet is needed and expected from the participants as these will be covered during the program.
Participants are requested to bring their own devices (BYOD) but are provided the cloud environment(s) for the lab exercises.
Course Outline
| Statistical Foundations and Machine Learning Concepts |
|---|
|
Building Blocks of a Machine Learning Algorithm |
|
Machine Learning Lifecycle |
|
Deep Learning Concepts |
|
Regularization |
|
Bias / Variance Tradeoff |
|
Others |
| Day 1 |
|---|
|
Course Introduction |
|
Getting Started with Machine Learning |
|
Introduction to Statistics and Machine Learning with R- |
|
Introduction to Statistics and Machine Learning with Python |
|
Getting Started with Algorithms |
|
Fundamental Algorithms |
|
Regression Trees with R |
|
Support Vector Machines with Python |
|
Model Evaluation - Part 1 |
|
Multiple Choice Quiz - Discussion |
| Day 2 |
|---|
|
Model Evaluation - Part 2 |
|
Confusion Matrix Metrics (Accuracy, Precision, Sensitivity, Specificity) |
|
Machine Learning Practices |
|
Data Preparation and Feature Engineering |
|
Data Imputation |
|
AWS ML Services |
|
Lambda + ML Services |
|
Multiple Choose Quiz + Discussion |
| Day 3 |
|---|
|
Getting Started with Amazon SageMaker |
|
SageMaker Algorithms - Introduction |
|
Converting SageMaker Python Notebook to R Notebook |
|
Introduction to Deep Learning |
|
Deep Learning: Deep Dive |
|
Hands-on: Introduction to Deep Learning |
|
Summary |
| Day 4 |
|---|
|
Handling Imbalanced Datasets
|
|
Data Preparation and Feature Engineering |
|
Open Notes: Concepts Sheet |
|
SageMaker Algorithms |
|
Converting SageMaker Python Notebook to R Notebook |
|
Multiple Choice Quiz + Discussion |
| Day 5 |
|---|
|
SageMaker Algorithms |
|
SageMaker: Hyperparameters |
|
Converting SageMaker Python Notebook to R Notebook |
|
Closed Notes: SageMaker Algorithms Sheet |
|
AWS Data Engineering Services |
|
AWS Terms |
|
Multiple Choice Quiz + Discussion |
| Day 6 |
|---|
|
Activation Functions |
|
Deep Learning |
|
Overfitting/Underfitting |
|
Open Notes: Hyperparameters |
|
Multiple Choice Quiz + Discussion |
|
AWS Certified Machine Learning Specialty Practice Exam |
| Day 7 |
|---|
|
SageMaker Deep Dive |
|
Challenge Lab: Solve a specific problem (Python) |
|
Research: Intermediate Concepts |
|
Discussion: Intermediate Concepts |
|
Multiple Choice Quiz + Discussion |
|
Summary + Tips |
Coach Profiles
Diwa del Mundo
Co-founder and President
apper.ph
AWS Certified Machine Learning - Specialty
Diwa “Wawi” del Mundo is the Co-founder and President of Apper.ph. He is one of the pioneers in the fintech, and cloud computing industry market, with his over 15 years working experience. He has since then established himself as a country expert in security, payments, and cloud architecture.
He currently holds 10 cloud certifications, with 3 specialty and 2 professional certifications from AWS. This is currently the most number of cloud badges in the country.
Joshua Arvin Lat
AWS Machine Learning Hero
CTO of Complete Business Online, Insites, and Jepto
AWS Certified Machine Learning - Specialty
Arvin is the Chief Technology Officer (CTO) at Complete Business Online, Insites, and Jepto. He previously served as the Director for Software Development and Engineering for multiple startups enabling him to solve complex business problems at speed and scale.
In addition to this, he has contributed and shared talks on machine learning and serverless engineering in international conferences like PyCon Malaysia and PyCon Thailand to help bridge the gap between reality and expectations on applied machine learning and machine learning engineering.