AWS Machine Learning Certification Exam

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.

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
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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
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Show less

Syllabus

INTRODUCTION, DATA/ML LINGO, AWS DATA STORAGE

What makes this course unique?

AWS Machine Learning Exam Overview

Course Outline

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers Amazon SageMaker, a fully managed service, which can help learners build, train, and deploy machine learning models quickly, making it highly relevant for those seeking certification

Explores AWS Kinesis services, which are useful for real-time data processing and analysis, a key aspect of modern machine learning pipelines and a valuable skill for those in data-intensive roles

Examines feature engineering techniques like imputation, binning, encoding, and normalization, which are essential for preparing data for machine learning models and improving their performance in real-world applications

Includes Python libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn, which are fundamental tools for data manipulation, analysis, and visualization in the field of machine learning and data science

Discusses Amazon S3 storage services, which are critical for storing and managing large datasets used in machine learning projects, making it a practical skill for those working with cloud-based solutions

Features content released in 2020, which may not reflect the most current services and best practices in the rapidly evolving field of machine learning and AWS offerings, so learners should seek supplementary materials

Reviews summary

Comprehensive guide for aws ml certification

According to students, this course is a comprehensive guide specifically designed to prepare learners for the AWS Machine Learning Speciality Certification Exam. Learners say it provides a solid foundation covering a wide array of topics from data engineering to algorithms and AWS services. Many found the explanation of complex concepts clear and easy to understand, making the material digestible. The hands-on labs and practical examples are frequently highlighted as valuable, helping solidify understanding. While largely positive, some learners note the challenge of keeping up with rapid AWS changes, wishing for more frequent updates to match the latest exam nuances.

Complex topics made understandable.

"The instructor has a knack for explaining complicated machine learning concepts simply."

"I found the explanations on algorithms and model selection particularly clear and easy to follow."

"Even challenging topics were broken down well, aiding comprehension significantly."

Practical labs enhance learning.

"The labs and practical demonstrations were instrumental in understanding how to apply the concepts on AWS."

"I learn best by doing, and the hands-on sections in this course were incredibly valuable."

"Working through the examples helped solidify my knowledge and build confidence using SageMaker and other services."

Covers broad ML/AWS topics thoroughly.

"The breadth of topics covered, from data processing to deployment and algorithms, is impressive and necessary."

"It dives deep into various AWS ML services and how they fit together, which is crucial."

"I appreciated the coverage of both theoretical ML concepts and their practical application on AWS."

Excellent resource for certification exam.

"This course helped me pass the AWS Machine Learning Specialty exam. It covers everything needed."

"It's tailored specifically for the certification, hitting all the important topics and services."

"As someone preparing for the exam, I found the structure and content directly relevant to the syllabus."

AWS changes require updates.

"AWS services evolve quickly, and some parts of the course felt slightly behind the very latest changes or exam focus."

"Keeping up with AWS updates is hard, and while the core is solid, newer services or exam points might not be fully covered."

"A few specific details regarding service interfaces or exam question styles seemed slightly dated compared to the current state."

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

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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

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Review key statistical terms and definitions.
Work through practice problems on hypothesis testing.
Review different types of distributions.

Read 'Python Data Science Handbook'

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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

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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

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Consolidate your knowledge of AWS machine learning services by creating a cheat sheet. This will serve as a quick reference guide for future projects.

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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

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Show all seven activities

Create a Compilation of AWS ML Resources

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Improve your ability to find and use AWS ML resources. This compilation will serve as a starting point for future projects.

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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

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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.

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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

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Sharpen your feature engineering skills. This will help you improve the performance of your machine learning models.

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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'

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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

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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.

Python Data Science Handbook

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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

$$$

AI Engineering

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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

$$$

Complete Guide

What's inside

Learning objectives

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Reviews summary

Comprehensive guide for aws ml certification

Activities

Career center

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