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AWS Machine Learning Certification Exam | Complete Guide

Dr. Ryan Ahmed, Ph.D., MBA, SuperDataScience Team, and Ligency Team

Update 01/02/2020: Section #13 on Machine Learning Implementation and Operations is released.

Machine and Deep Learning are the hottest tech fields to master right now. Machine/Deep Learning techniques are widely adopted in many fields such as banking, healthcare, transportation and technology. Amazon has recently introduced the AWS machine Learning Certification Speciality exam and its quite challenging. AWS Certified Machine Learning Specialty is targeted at data scientists and developers who design, train and deploy AI/ML models to solve real-world challenging problems.

Read more

Update 01/02/2020: Section #13 on Machine Learning Implementation and Operations is released.

Machine and Deep Learning are the hottest tech fields to master right now. Machine/Deep Learning techniques are widely adopted in many fields such as banking, healthcare, transportation and technology. Amazon has recently introduced the AWS machine Learning Certification Speciality exam and its quite challenging. AWS Certified Machine Learning Specialty is targeted at data scientists and developers who design, train and deploy AI/ML models to solve real-world challenging problems.

The bad news: this exam is a very challenging AWS exam since it tests the candidate’s knowledge on multiple aspects such as (1) Data Engineering and Feature Engineering, (2) AI/ML Models selection, (3) Appropriate AWS services solution to solve business problem, (4) AI/ML models building, training, and deployment, (5) Model optimization and Hyperparameters tuning. You need to answer these questions in order to pass the exam:

o How to select proper ML technique to solve a given business problem?

o Which AWS service could work best for a given problem?

o How to design, implement and scale secure ML solutions?

o How to choose the most cost-effective solution?

The good news: With over 500+ slides and over 50 practice questions, this course is by far the most comprehensive course on the market that provides students with the foundational knowledge to pass the AWS Machine Learning Certification exam like a pro. This course covers the most important concepts without any fillers or irrelevant information.

Enroll now

What's inside

Learning objectives

  • Data engineering
  • Data types, python libraries (pandas, numpy, scikit learn, matplotlib, seaborn), data distributions, timeseries, feature engineering (imputation, binning, encoding, and normalization)
  • Aws services and algorithms
  • Amazon sagemaker, amazon s3 storage services, aws glue
  • Aws kinesis services (kinesis firehose, kinesis video streams, kinesis data streams, kinesis analytics)
  • Redshift, redshift spectrum, dynamodb, athena, amazon quicksight, elastic map reduce (emr)
  • Rekognition, lex, polly, comprehend, translate, transcribe, blazingtext word2vec, deepar, factorization machines, gradient boosted trees (xgboost)
  • Image classification (resnet), ip insights, k-means clustering, k-nearest neighbor (k-nn)
  • Latent dirichlet allocation (lda), linear learner (classification), linear learner (regression)
  • Neural topic modelling (ntm), object2vec, object detection, principal component analysis (pca), random cut forest, semantic segmentation, and seqence2sequence
  • Machine and deep learning basics
  • Show more
  • Show less

Syllabus

INTRODUCTION, DATA/ML LINGO, AWS DATA STORAGE
What makes this course unique?
AWS Machine Learning Exam Overview
Course Outline
Read more
EXTRA: Learning Path
Guidelines and Best Practices
Section Introduction



What is Machine Learning and AI - Part 1
What is Machine Learning and AI - Part 1 Quiz
What is Machine Learning and AI - Part 2
What is Machine Learning and AI - Part 2 Quiz
Amazon Web Services
Amazon Web Services Quiz
AIML Data Lingo - Labeled vs. unlabeled
AIML Data Lingo - Labeled vs. unlabeled Quiz
AIML Data Lingo - Data Types
AIML Data Lingo - Data Types - quiz
Database vs. datalake vs. warehouse
Database vs. datalake vs. warehouse - Quiz
AWS Storage S3 DynamoDB RDS
AWS Storage S3 DynamoDB RDS Quiz
GET YOUR EXTRA MATERIALS
Section 1 Slides
AMAZON S3
Amazon S3 Partitions and Tags
Amazon S3 Partitions and Tags Quiz
S3 Storage Tiers and LifeCycle Polices
S3 Storage Tiers and LifeCycle Polices Quiz
S3 Encryption
S3 Encryption Quiz
S3 Security - Part 1
S3 Security - Part 1 Quiz
S3 Security - Part 2
S3 Security - Part 2 Quiz
Additional Information
Additional Information Quiz
Section 2 Slides
AWS DATA MIGRATION, GLUE, PIPELINE, STEP and BATCH
AWS Glue – part #1
AWS Glue – part #1 Quiz
AWS Glue – part #2
AWS Glue – part #2 Quiz
AWS Data Pipeline
AWS Data Pipeline Quiz
AWS Data Migration Service DMS
AWS Data Migration Service DMS Quiz
AWS Batch
AWS Batch Quiz
AWS Step Function
AWS Step Function Quiz
Section 3 Slides
DATA STREAMING AND KINESIS
Section Introduction Quiz
Kinesis Overview
Kinesis Overview Quiz
AWS Kinesis Video Streams - Part 1
AWS Kinesis Video Streams - Part 1 Quiz
AWS Kinesis Video Streams - Part 2
AWS Kinesis Video Streams - Part 2 Quiz
AWS Kinesis Data Streams - Part 1
AWS Kinesis Data Streams - Part 1 Quiz
AWS Kinesis Data Streams - Part 2
AWS Kinesis Data Streams - Part 2 - Quiz
AWS Kinesis Firehose
AWS Kinesis Firehose - Quiz
AWS Kinesis Analytics - Part 1
AWS Kinesis Analytics - Part 1 - Quiz
AWS Kinesis Analytics - Part 2
AWS Kinesis Analytics - Part 2 - Quiz
Section 4 Slides
JUPYTER NOTEBOOK, SCIKIT-LEARN, PYTHON PACKAGES, AND DISTRIBUTIONS
Jupyter Notebooks and Scikit Learn
Jupyter Notebooks and Scikit Learn Quiz
Python Packages (Pandas, Numpy, Matplotlib and Seaborn)
Python Packages (Pandas, Numpy, Matplotlib and Seaborn) Quiz
Data Visualization
Data Visualization - Quiz
Distributions (Normal, Standard, Poisson, Bernoulli)
Distributions (Normal, Standard, Poisson, Bernoulli) - Quiz
Time Series
Time Series - Quiz
GET YOUR EXTRA MATERIALS
Section 5 Slides
ATHENA, QUICKSIGHT, EMR
Athena - Part 1
Athena - Part 1 - Quiz
Athena - Part 2
Athena - Part 2 - Quiz
Amazon Quicksight - Part 1
Amazon Quicksight - Part 1 - Quiz
Amazon Quicksight - Part 2

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers key AWS services like SageMaker, S3, Glue, and Kinesis, which are essential for building and deploying machine learning solutions on the AWS platform
Includes practice questions that allow learners to assess their understanding and readiness for the AWS Machine Learning Certification exam
Explores data engineering concepts like feature engineering, data distributions, and time series analysis, which are crucial for preparing data for machine learning models
Examines various machine learning algorithms, including XGBoost, ResNet, and k-NN, which are commonly used in real-world applications and covered in the AWS certification exam
Requires familiarity with Python libraries like Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn, which may require additional learning for those new to Python programming
Features content that was last updated in early 2020, so learners should verify whether the AWS services and algorithms covered are still current and relevant for the latest exam version

Save this course

Save AWS Machine Learning Certification Exam | Complete Guide to your list so you can find it easily later:
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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AWS Machine Learning Certification Exam | Complete Guide with these activities:
Review Basic Statistics Concepts
Reinforce your understanding of fundamental statistical concepts. A solid grasp of statistics is crucial for understanding machine learning algorithms and interpreting results.
Browse courses on Hypothesis Testing
Show steps
  • Review key statistical terms and definitions.
  • Work through practice problems on hypothesis testing.
  • Review different types of distributions.
Read 'Python Data Science Handbook'
Familiarize yourself with core Python libraries for data science. This book will help you understand how to use these libraries effectively in your machine learning projects on AWS.
View Python Data Science Handbook: Essential Tools... on Amazon
Show steps
  • Read the chapters on NumPy and Pandas.
  • Practice using Matplotlib for data visualization.
  • Explore Scikit-Learn for model building.
Create a Cheat Sheet for AWS ML Services
Consolidate your knowledge of AWS machine learning services by creating a cheat sheet. This will serve as a quick reference guide for future projects.
Show steps
  • List all the AWS ML services covered in the course.
  • Summarize the key features and use cases of each service.
  • Organize the information in a clear and concise format.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a Compilation of AWS ML Resources
Improve your ability to find and use AWS ML resources. This compilation will serve as a starting point for future projects.
Show steps
  • Gather links to AWS documentation, blog posts, and tutorials.
  • Organize the resources by topic and service.
  • Add brief descriptions of each resource.
Build a Simple Classification Model on AWS
Gain hands-on experience with AWS SageMaker by building a simple classification model. This project will solidify your understanding of the model building, training, and deployment process.
Show steps
  • Choose a publicly available dataset.
  • Prepare the data using Pandas and NumPy.
  • Train a classification model using SageMaker.
  • Deploy the model and test its performance.
Practice Feature Engineering Techniques
Sharpen your feature engineering skills. This will help you improve the performance of your machine learning models.
Show steps
  • Practice imputation techniques on missing data.
  • Experiment with different encoding methods for categorical features.
  • Apply normalization and scaling techniques to numerical features.
Read 'Designing Machine Learning Systems'
Deepen your understanding of the machine learning lifecycle. This book will help you design and deploy robust and scalable machine learning systems on AWS.
View AI Engineering: Building Applications with... on Amazon
Show steps
  • Read the chapters on data engineering and feature engineering.
  • Explore different model deployment strategies.
  • Learn about monitoring and maintaining machine learning systems.

Career center

Learners who complete AWS Machine Learning Certification Exam | Complete Guide will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer builds and maintains the infrastructure for machine learning models. This role requires a strong understanding of data engineering, model selection, and deployment, all of which are covered in this course through its focus on AWS services and algorithms. A machine learning engineer should be comfortable working with data pipelines and AWS tools, like Kinesis, SageMaker, and S3, which this course emphasizes. The course's practical approach to solving business problems using machine learning techniques makes it particularly useful for someone aiming to become a machine learning engineer.
See salaries and explore the career path for Machine Learning Engineer
Machine Learning Specialist
A Machine Learning Specialist focuses on the practical implementation of machine learning models and solutions. This course provides an excellent pathway to becoming a machine learning specialist, by delving deeply into the AWS ecosystem for machine learning. The course helps a machine learning specialist by giving exposure to services and concepts including SageMaker, Kinesis, and S3, as well as numerous models and algorithms. The course also includes best practices, which are essential for real-world deployment.
See salaries and explore the career path for Machine Learning Specialist
Cloud Machine Learning Engineer
A Cloud Machine Learning Engineer specializes in deploying and managing machine learning models on the cloud. This course offers a detailed look at AWS services related to machine learning, which is critical for a cloud machine learning engineer. The course covers key topics such as AWS SageMaker, data streaming with Kinesis, and the management of data. A practical understanding of these cloud services, along with the various machine learning models presented, forms an essential part of the role of cloud machine learning engineer.
See salaries and explore the career path for Cloud Machine Learning Engineer
Data Scientist
A Data Scientist analyzes large datasets to extract meaningful insights and develop predictive models. This course helps a data scientist by offering a deep dive into AWS machine learning services, along with practical applications of various machine learning models and algorithms. The course's focus on data engineering and feature engineering is particularly beneficial for a data scientist, as is also its study of tools including SageMaker and AWS data storage and processing technologies. This course's comprehensive coverage of topics directly relevant to the daily tasks of a data scientist makes it a great choice.
See salaries and explore the career path for Data Scientist
AI Developer
An AI Developer builds applications and systems that incorporate artificial intelligence. This course helps an aspiring AI developer gain practical knowledge of AWS's machine learning offerings, including models and services. The course's emphasis on deploying and scaling secure machine learning solutions is useful for an AI developer. The course introduces a variety of tools within the AWS ecosystem that an AI developer would employ in their daily work, such as SageMaker and Kinesis. The course's study of how to solve business problems is also useful for an AI developer.
See salaries and explore the career path for AI Developer
Data Engineer
A Data Engineer focuses on building and maintaining the infrastructure needed for data analysis and machine learning. The course provides strong insights into AWS data migration, storage, and pipeline services, including AWS Glue and Kinesis. These are all crucial for a data engineer in their day-to-day work. The course's coverage of data engineering practices and AWS services makes it highly relevant for anyone seeking to be a data engineer. Understanding of data types and distributions, which are taught in the course, are also essential.
See salaries and explore the career path for Data Engineer
Artificial Intelligence Specialist
An Artificial Intelligence Specialist develops and implements AI solutions by using techniques such as deep learning and natural language processing. This course helps an aspiring artificial intelligence specialist through its comprehensive coverage of AWS machine learning services. The course explores various machine learning models and their implementation. The course also presents the use of tools such as SageMaker, Kinesis, and others within the AWS ecosystem, which are crucial for an AI Specialist. The course provides a deeper understanding of how to deploy and scale secure machine learning solutions.
See salaries and explore the career path for Artificial Intelligence Specialist
Cloud Solutions Architect
A Cloud Solutions Architect designs and implements cloud infrastructure solutions. This course provides a strong base in AWS machine learning services, data engineering, and model deployment. Cloud solution architects use these skills to build robust, scalable and secure solutions, often incorporating machine learning. The course prepares anyone hoping to work as a cloud solutions architect by exploring AWS services such as S3, Kinesis, and SageMaker, which are often a part of data-focused cloud architect solutions. It also covers important principles such as cost optimization.
See salaries and explore the career path for Cloud Solutions Architect
Data Architect
A Data Architect designs and oversees the data management framework for an organization. This course helps a data architect by covering key AWS data services like S3, DynamoDB, and Redshift, as well as data migration tools and services. The course focuses on essential elements, such as data storage and pipelining. It also delves into technologies for data streaming. The course covers topics that are needed for a data architect to create robust and scalable data infrastructure.
See salaries and explore the career path for Data Architect
AI Consultant
An AI Consultant advises organizations on how to implement artificial intelligence solutions. This course may be useful to an AI consultant by providing a detailed understanding of AI and machine learning models, as well as how to use AWS services to build and deploy them. The course's focus on a wide range of AWS services and machine learning models is relevant to advising an organization on the right solutions. A consultant can make practical and informed recommendations based on the information in this course.
See salaries and explore the career path for AI Consultant
Business Intelligence Analyst
A Business Intelligence Analyst uses data to improve business operations and inform decision-making. This course may be useful for a business intelligence analyst, as it covers data analysis and visualization tools like Amazon Quicksight. It also explores data storage and management techniques that are frequently relevant to a business intelligence analyst. The course touches on database technologies such as Athena, which are used to query data. These elements of the course can help a business intelligence analyst perform their role more efficiently.
See salaries and explore the career path for Business Intelligence Analyst
Statistical Modeler
A Statistical Modeler creates and maintains statistical models to inform decision-making. This course may be useful for a statistical modeler by providing practical insights into various machine learning algorithms, an area that overlaps with statistical modeling. Also, the course explores Python libraries such as Pandas, NumPy, and ScikitLearn. These are tools that are often employed by a statistical modeler. The course can help a statistical modeler become familiar with new techniques and tools. A statistical modeler typically has an advanced degree.
See salaries and explore the career path for Statistical Modeler
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to solve business challenges and inform financial decisions. This course may be helpful to a quantitative analyst through its coverage of machine learning algorithms. Quantitative analysts use quantitative techniques to model and understand market behavior. The various models and algorithms covered by the course, like those involving time series, may be useful for quantitative work. A quantitative analyst typically has an advanced degree.
See salaries and explore the career path for Quantitative Analyst
Research Scientist
A Research Scientist conducts research to develop new technologies or advance scientific knowledge. This course touches on various machine learning models and algorithms, which are critical for research scientists working in AI and related fields. Although this course does not focus on academic research, many machine learning algorithms, such as gradient boosting and neural networks, are presented in this course. This may be helpful for those who pursue research as a career. A research scientist often needs a master's or PhD degree.
See salaries and explore the career path for Research Scientist
Data Analyst
A Data Analyst collects, processes, and analyzes data to extract insights for decision-making. The course helps a data analyst through its emphasis on data types, feature engineering, and data visualization. A wide range of data technologies are covered in the course, such as database and data storage technologies. This may be useful to a data analyst who hopes to gain knowledge of these in demand skills. The practical use of Python libraries like pandas, Numpy, MatplotLib and Seaborn, are also covered in the course.
See salaries and explore the career path for Data Analyst

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in AWS Machine Learning Certification Exam | Complete Guide.
Cover image
Python Data Science Handbook
Save
Provides a comprehensive overview of essential Python data science tools and techniques. It covers NumPy, Pandas, Matplotlib, and Scikit-Learn, which are heavily used in AWS machine learning. It valuable resource for understanding data manipulation, analysis, and visualization. This book is commonly used as a reference by data scientists.
Python Data Science Handbook: Essential Tools for...
Paperback
$$$
Cover image
AI Engineering
Save
Provides a comprehensive guide to designing, building, and deploying production-ready machine learning systems. It covers topics such as data engineering, model selection, deployment strategies, and monitoring. It valuable resource for understanding the end-to-end machine learning lifecycle. This book is helpful in providing additional depth to the existing course.
AI Engineering: Building Applications with...
Kindle Edition
$$$

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Effort
17 total hours
Via
Udemy
Instructors
Dr. Ryan Ahmed, Ph.D., MBA
SuperDataScience Team
Ligency Team
Language
English
Good to know
Covers key AWS services like SageMaker, S3, Glue, and Kinesis, which are essential for building and deploying machine learning solutions on the AWS platform
Includes practice questions that allow learners to assess their understanding and readiness for the AWS Machine Learning Certification exam
Explores data engineering concepts like feature engineering, data distributions, and time series analysis, which are crucial for preparing data for machine learning models
Examines various machine learning algorithms, including XGBoost, ResNet, and k-NN, which are commonly used in real-world applications and covered in the AWS certification exam
Requires familiarity with Python libraries like Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn, which may require additional learning for those new to Python programming
Features content that was last updated in early 2020, so learners should verify whether the AWS services and algorithms covered are still current and relevant for the latest exam version
Share and help others discover this course.
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