What you would learn in AWS Certified Machine Learning Specialty 2022 - Hands On! course?
Are you worried about getting this AWS Certified Machine Learning - Specialty test (MLS-C01)? It's a good idea to be! It's a fact that it's among the more challenging and sought-after AWS certifications. An in-depth understanding of AWS and SageMaker isn't enough to get this test - you need a thorough understanding of machine learning and the specifics of feature modeling and engineering, which aren't typically taught in textbooks or in classes. It's impossible to prepare adequately for this exam.
The certification prep course will be taught by Frank Kane, who was employed for nine years at Amazon within Machine Learning. Frank completed and passed the test on his first attempt and knows precisely what you need to know to pass it successfully on your own. Alongside Frank in this class will be Stephane Maarek, the AWS expert and a popular AWS-certified instructor at Udemy.
A one-hour online course and a 30-minute short assessment exam are included, based on the same topics and format as the actual test. You'll also get four labs that let you test your knowledge and gain experience in a model tune-up, feature engineering, and engineering data.
This course is divided into four domains tested in this test: data engineering, exploratory modeling, data analysis, and the implementation of machine learning and operations. Some of the subjects that we'll discuss include:
S3 data lakes
AWS and Glue, as well as Glue ETL
Kinesis data streams, firehose, and video streams
DynamoDB
Data Pipelines AWS Batch and Step Functions
Using scikit_learn
Basics of data science
Athena and Quicksight
Elastic MapReduce ( EMR)
Apache Sparkand MLLib
Features engineering(imputation outliers, binning and transforms encoders, normalization, and imputation)
Ground Truth
Deep Learning basics
Making neural networks more efficient and avoiding overfitting
Amazon SageMaker, including SageMaker Studio SageMaker Model Monitoring, SageMaker Autopilot, and SageMaker Debugger.
Regularization techniques
Evaluation of the performance of machine-learning models (precision-recall F1, matrix of confusion, etc.)
High-level ML-related services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
Build recommender systems using Amazon's Personalize
Monitoring industrial equipment using Lookout as well as Monitron
Security best practices using machine-learning on AWS
Course Content:
- What can you expect from exam day? AWS Certified Machine Learning Specialty test
- Amazon SageMaker's machine learning built-in algorithm (XGBoost, BlazingText, Object Detection, and more.)
- Features engineering techniques, which include Binning, imputation, outliers, and normalization
- High-level ML service: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
- Data engineering using S3, Glue, Kinesis, and DynamoDB
- Data analysis exploratory using the scikit_learn program, Athena, Apache Spark, and EMR
- Hyperparameter tuning and Deep Learning for deep neural networks
- Automated model tuning and operations using SageMaker
- Regularization of L1 and L2
- Implementing security best practices in machine learning pipelines
Download AWS Certified Machine Learning Specialty 2022 - Hands On! from below links NOW!
