3  5 reviews on Udemy

AWS Machine Learning Specialty Practice 2021

Prepare well for your AWS Machine Learning Specialty certification
Course from Udemy
 43 students enrolled
This full practice exam is intended to help you prepare for the AWS Certified Machine Learning – Specialty exam. I have created those questions right after I passed the actual certification exam. I have prepared these questions to cover all the areas and their corner cases.

Realistic practice exam based on the most recent AWS Certified Machine Learning Specialty exam.

  • Just like the actual exam this practice test has

    • Test 1: 30 questions

    • Test 2: 65 questions and takes 170 minutes

  • Questions are mapped based on the actual exam domains:

    • Data Engineering

    • Exploratory Data Analysis

    • Modeling

    • Machine Learning Implementation and Operations.

  • Suggested background knowledge

    • Data Engineering

      • AWS services:

        • Glue, EMR ( Apache Spark, Hive metastore),  Athena

        • Kinesis family (Streams, data analytics, firehose, video streams)

        • S3, QuickSight

        • Data/File formats (Avro, Parquet, CSV, protobuf recordIO)

    • Exploratory Data Analysis

      • Handling missing values (Imputation: median, mean, most frequent, using ML model)

      • Feature scaling

      • Feature engineering

      • Handling outliers

      • One-hot encoding

      • Binning

      • Text preprocessing

    • Modeling

      • supervised machine learning ( Classification and Regression Algorithms)

      • unsupervised machine learning ( K-Means clustering, PCA)

      • Hyperparameter tuning ( supervised machine learning, deep learning)

      • Performance metrics ( accuracy, RMSE, F1 score, AUC, ROC, Precision, Recall)

      • Tuning deep learning networks ( how to prevent overfitting)

      • AWS ML services: Lex, Polly, Transcribe, Translate, Comprehend

      • SageMaker built-in algorithms: BlazingText, Object2Vec, DeepAR, LDA, Linear Learner, etc.

    • ML Implementation and Operations

      • Amazon SageMaker train and deploy a model

        • Inference pipeline, batch transform, inference endpoints, production variants, hosting services

      • Amazon SageMaker security (data encryption at rest and in transit)

      • Distributed training on Amazon SageMaker ( Using GPUs)

      • AWS SageMaker roles

      • Bring your own model container ( e.g. developed using scikit-learn)

      • Customize SageMaker built-in algorithm containers

      • How to develop and deploy deep learning models on frameworks such as Tensoflow, MXNeT

AWS Machine Learning Specialty Practice 2021
$ 19.99
per course
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