Preparing for the AWS Certified Machine Learning Specialty exam? Don’t know where to start? This post is the AWS Certified Machine Learning Certificate Study Guide (with links to each objective in the exam domain).
I have curated a detailed list of articles from AWS documentation and other blogs for each objective of the AWS Certified Machine Learning (MLS-C01) exam. Please share the post within your circles so it helps them to prepare for the exam.
AWS Certified Machine Learning Online Course
Pluralsight (Free Trial) AWS Certified ML Specialty Learning Path LinkedIn Learning (Free Trial) AWS Machine Learning Essentials [Path] Udemy AWS Certified Machine Learning Course
AWS Certified Machine Learning Practice Test
Whizlabs Exam Questions AWS ML [145 Practice Tests & 3 Labs] Udemy Practice Test AWS Cert. ML Specialty 75 Practice Questions
AWS Certified Machine Learning Preparation
Coursera Getting Started with AWS Machine Learning Amazon e-book (PDF) Mastering Machine Learning on AWS
To view other AWS certificate study guides, click here
Full Disclosure: Some of the links in this post are affiliate links. I receive a commission when you purchase through them.
Data Engineering - 20%
Create data repositories for machine learning
Identify and implement a data-ingestion solution
Identify and implement a data-transformation solution
Data transformations in Amazon Machine Learning:
2. Orthogonal Sparse Bigram (OSB) Transformation
4. Remove Punctuation Transformation
5. Quantile Binning Transformation
6. Normalization Transformation
7. Cartesian Product Transformation
Amazon SageMaker Batch Transform
Example: Transform dataset from numpy.array format to the CSV format
Data Rearrangement: Create datasource based on a section of the input data
Exploratory Data Analysis - 24%
Sanitize and prepare data for modeling
Perform feature engineering
Analyze and visualize data for machine learning
Analyzing data for Machine Learning
1. Analyzing Data with Amazon Machine Learning
2. Explore, Analyze & Process data
Visualizing data for Machine Learning
1. Visualizing the distribution of data
2. Visualizing insights for binary models
3. Visualizing insights for Regression models
4. Visualizing insights for Multi-class Models
Amazon link (affiliate)
Modeling - 36%
Frame business problems as machine learning problems
A news article on Applying Machine Learning To Business Problems by Carlos Escapa
Slides from the same session by Carlos
Other interesting articles/experiences to read:
1. Resources from AWS: Formulating the Problem
2. Resources from Amazon: Solving Business Problems with Amazon ML
3. This course from Google is great for understanding the ML Problem framing
4. A Step-by-Step Guide to Machine Learning Problem Framing from Medium
5. Frame a problem as a machine learning problem from Data Science Central
Select the appropriate model(s) for a given machine learning problem
Amazon Machine Learning: Types of ML Models
Excellent article on how to select an ML algorithm (although for Azure ML)
Review all the built-in algorithms in SageMaker to understand their applicability:
Supervised Learning Algorithms:
DeepAR forecasting (Forecasts time series using Neural Networks)
Factorization Machines (Captures interactions between features)
Image Classification Algorithm (Multi-label image classification)
K-Nearest Neighbors Algorithm (Predicts similar items by evaluating distance)
Linear Learner Algorithm (for Classification or Regression problems)
Object2Vec Algorithm (General-purpose algorithm)
Object Detection Algorithm (Classifies objects in images)
Sequence-to-Sequence Algorithm (for text summarization)
Unsupervised Learning Algorithms:
IP Insights Algorithm (Learns the usage patterns of IPv4 addresses)
K-Means (Finds discrete groups within data)
Latent Dirichlet Allocation (LDA) Algorithm (Unsupervised learning algorithm)
Neural Topic Model (NTM) Algorithm (organize a corpus of documents into topics)
Principal Component Analysis (PCA) Algorithm (for reducing the dimensionality)
Random Cut Forest (RCF) Algorithm (Detects anomalous data points in a data set)
Other Machine Learning Algorithms:
BlazingText Algorithm (Text Classification)
Semantic Segmentation Algorithm (tags every pixel in an image with a label)
Train machine learning models
Perform hyperparameter optimization
Evaluate machine learning models
Evaluating ML Models:
Understand the Cross-validation technique for evaluating ML Models
Evaluating Model Fit: Underfitting vs. Overfitting
Evaluate Model Accuracy of the following:
Machine Learning Implementation and Operations - 20%
Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance
Performance:
1. Review the ML Model’s Predictive Performance
Availability:
1. Deploy Multiple Instances Across Availability Zones
Scalability:
1. Amazon SageMaker: Infinitely Scalable Machine Learning Algorithms
2. Review this Whitepaper: Power Machine Learning at Scale
3. Scaling Machine Learning from 0 to millions of users
Resiliency:
Recommend and implement the appropriate machine learning services and features for a given problem
Apply basic AWS security practices to machine learning solutions
Deploy and operationalize machine learning solutions
2 ways to deploy your model:
a. Amazon SageMaker hosting services (to set up an endpoint to get predictions)
Deploy a Model on Amazon SageMaker Hosting Services
Example: Deploying a Model to Amazon SageMaker Hosting Services
b. Amazon SageMaker batch transform (to get predictions on the entire dataset)
Overview: Deploying a model with Amazon SageMaker batch transform
Deploy the Model with Batch Transform
This brings us to the end of the AWS Certified Machine Learning – Specialty [MLS-C01] Exam Preparation Study Guide.
What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!
In case you are looking for other AWS certificate exams study guides, check out this page
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