App: AWS Machine Learning Prep PRO
Developer: DjamgaTech Corp
Genres: Bildung, Produktivität

Use this App to learn about Machine Learning on AWS and prepare for the AWS Machine Learning Specialty Certification MLS-C01.
Earning AWS Certified Machine Learning  Specialty validates expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.

The App provides hundreds of quizzes and practice exam about:
- Machine Learning Operation on  AWS 
- Modelling
- Data Engineering
- Computer Vision, 
- Exploratory Data Analysis, 
- ML implementation & Operations
- Machine Learning Basics Questions and Answers 
- Machine Learning Advanced Questions and Answers 
- Scorecard
- Countdown timer
- Machine Learning Cheat Sheets
- Machine Learning Interview Questions and Answers 
- Machine Learning Latest News
- No AD

The App covers Machine Learning Basics and Advanced topics including: NLP, Computer Vision,  Python,  linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc. 

Domain 1: Data Engineering
Create data repositories for machine learning.
Identify data sources (e.g., content and location, primary sources such as user data)
Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)
Identify and implement a data ingestion solution.
Data job styles/types (batch load, streaming)
Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads), etc.


Domain 2: Exploratory Data Analysis
Sanitize and prepare data for modeling.
Perform feature engineering.
Analyze and visualize data for machine learning.

Domain 3: Modeling
Frame business problems as machine learning problems.
Select the appropriate model(s) for a given machine learning problem.
Train machine learning models.
Perform hyperparameter optimization.
Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations
Build machine learning solutions for performance, availability, scalability, resiliency, and fault
tolerance.
Recommend and implement the appropriate machine learning services and features for a given
problem.
Apply basic AWS security practices to machine learning solutions.
Deploy and operationalize machine learning solutions.

Machine Learning Services covered:
Amazon Comprehend
AWS Deep Learning AMIs (DLAMI)
AWS DeepLens
Amazon Forecast
Amazon Fraud Detector
Amazon Lex
Amazon Polly
Amazon Rekognition
Amazon SageMaker
Amazon Textract
Amazon Transcribe
Amazon Translate

Other Services and topics covered are:
Ingestion/Collection
Processing/ETL
Data analysis/visualization
Model training
Model deployment/inference
Operational
AWS ML application services
Language relevant to ML (for example, Python, Java, Scala, R, SQL)
Notebooks and integrated development environments (IDEs),
S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, SageMaker, CSV, JSON, IMG, parquet or databases, Amazon Athena
Amazon EC2, Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Container Service, Amazon Elastic Kubernetes Service ,  Amazon Redshift


Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.


Note and disclaimer: We are not affiliated with  Microsoft or Azure or Google or Amazon.  The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any  exam you did not pass.

Release notes: