Data Analysis Review - GRE® LT 对比 AWS Machine Learning Exam Prep 的使用情况和统计数据

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 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.