AWS Certified Machine Learning Specialty 2020 - Hands On!
- 9.5 hours on-demand video
- 2 articles
- 1 Practice Test
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- What to expect on the AWS Certified Machine Learning Specialty exam
- Amazon SageMaker's built-in machine learning algorithms (XGBoost, BlazingText, Object Detection, etc.)
- Feature engineering techniques, including imputation, outliers, binning, and normalization
- High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
- Data engineering with S3, Glue, Kinesis, and DynamoDB
- Exploratory data analysis with scikit_learn, Athena, Apache Spark, and EMR
- Deep learning and hyperparameter tuning of deep neural networks
- Automatic model tuning and operations with SageMaker
- L1 and L2 regularization
- Applying security best practices to machine learning pipelines
- Associate-level knowledge of AWS services such as EC2
- Some existing familiarity with machine learning
- An AWS account is needed to perform the hands-on lab exercises
[ Updated for 2020's latest SageMaker features and new AWS ML Services. Happy learning! ]
Nervous about passing the AWS Certified Machine Learning - Specialty exam (MLS-C01)? You should be! There's no doubt it's one of the most difficult and coveted AWS certifications. A deep knowledge of AWS and SageMaker isn't enough to pass this one - you also need deep knowledge of machine learning, and the nuances of feature engineering and model tuning that generally aren't taught in books or classrooms. You just can't prepare enough for this one.
This certification prep course is taught by Frank Kane, who spent nine years working at Amazon itself in the field of machine learning. Frank took and passed this exam on the first try, and knows exactly what it takes for you to pass it yourself. Joining Frank in this course is Stephane Maarek, an AWS expert and popular AWS certification instructor on Udemy.
In addition to the 9-hour video course, a 30-minute quick assessment practice exam is included that consists of the same topics and style as the real exam. You'll also get four hands-on labs that allow you to practice what you've learned, and gain valuable experience in model tuning, feature engineering, and data engineering.
This course is structured into the four domains tested by this exam: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Just some of the topics we'll cover include:
S3 data lakes
AWS Glue and Glue ETL
Kinesis data streams, firehose, and video streams
Data Pipelines, AWS Batch, and Step Functions
Data science basics
Athena and Quicksight
Elastic MapReduce (EMR)
Apache Spark and MLLib
Feature engineering (imputation, outliers, binning, transforms, encoding, and normalization)
Deep Learning basics
Tuning neural networks and avoiding overfitting
Amazon SageMaker, in depth
Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)
High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
Security best practices with machine learning on AWS
Machine learning is an advanced certification, and it's best tackled by students who have already obtained associate-level certification in AWS and have some real-world industry experience. This exam is not intended for AWS beginners.
If there's a more comprehensive prep course for the AWS Certified Machine Learning - Specialty exam, we haven't seen it. Enroll now, and gain confidence as you walk into that testing center.
- Individuals performing a development or data science role seeking certification in machine learning and AWS.
Get the most from this course - learn how to adjust the video playback speed, enable closed captions, and ensure good video streaming.
Topics covered include normal distributions, Poisson distributions, binomial distributions, Bernoulli distributions, and the difference between probability density functions and probability mass functions.
There are lots of visualization choices; bar and line graphs, heat maps, tree maps, pivot tables, and much more - all of which are offered by QuickSight. Let's talk about how to decide which kind of graph is most appropriate for illustrating different aspects of your data.
Zeppelin notebooks run on your EMR cluster to control Spark, but EMR notebooks can run outside of your cluster and control the provisioning of the cluster itself, too. We'll also discuss the security features available with EMR, and how to choose an instance type for the master, core, and task nodes of your cluster.
We'll introduce what the world of feature engineering is all about, and why it is so important to getting good results from your machine learning models. And, we'll dive into the "curse of dimensionality," and why more features usually isn't better.
A big part of feature engineering is dealing with missing data. We'll discuss various approaches, including mean imputation, dropping, and using machine learning for imputation including KNN, deep learning, and regression methods such as MICE.
We'll round out our tour of feature engineering with a discussion of binning numerical data, transforming data to create new features to discover sub-linear and super-linear patterns, one-hot encoding, scaling and normalization, and the importance of shuffling your training data.
Humans can be the most important tool for creating missing data, especially labels. We'll talk about how Amazon SageMaker Ground Truth manages human labeling tasks and optimizes them, as well as using unsupervised techniques such as Rekognition and Comprehend to fabricate features and labels from existing data.
As TF-IDF (Term Frequency - Inverse Document Frequency) may be new to you, we'll start by reviewing how TF-IDF works and how it fits into a search engine solution.
Hyperparameter tuning of deep neural networks is a complex subject. We'll talk about how deep neural nets are trained with gradient descent, and how your choice of learning rate and batch size affects your training. Sometimes it's counter-intuitive!
The heart of AWS's machine learning offering is SageMaker. We'll cover what it does and its architecture at a high level, and how it's used together with ECR and S3.
Polly is the AWS service for text-to-speech. There are many ways to control it that we'll talk about.
We'll go in depth on how SageMaker containers work and their expected format, and how production variants can be used to divide traffic between different versions of a model.
Elastic Inference can accelerate deep learning inference deployments at a lower cost than deploying dedicated GPU instances. Automatic scaling can automatically add and remove inference nodes in response to load, as measured by CloudWatch. We'll also talk about ensuring your SageMaker resources are spread across multiple availability zones.
What to expect on your test day, and how to make sure you're in top form for it. We'll also cover some strategies on how to manage your time during the exam, and find the best answers.
This 10-question warmup test should give you a good idea of how prepared you really are for the full practice exam, and for the real one - without investing 3 hours in the process. We chose these questions to be representative of the domains covered by the real exam, and some of the more difficult topics you'll be expected to know on it. If you're surprised by the topics and level of detail you encounter, you know you have more preparation and studying to do.
The AWS Certified Machine Learning Specialty exam goes beyond AWS topics, and tests your knowledge in feature engineering, model tuning, and modeling as well as how deep neural networks work. You need to both have expert-level knowledge of AWS's machine learning services (especially SageMaker), and expert-level knowledge in machine learning and AI in general. Many of the questions seem specifically designed to confound people who have only learned the theory of AI but have not applied it in practice.