AWS Machine Learning Certification Exam|2020 Complete Guide
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Created by: Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard
500+ Slides | 200+ Questions | Become Certified in AWS ML| SageMaker|Data Engineering | Visualization | Model Deployment What you'll learn

Data Engineering
Data types, Python Libraries (pandas, Numpy, scikit Learn, MatplotLib, Seaborn), data distributions, timeseries, Feature Engineering (imputation, binning, encoding, and normalization)
AWS Services and Algorithms
Amazon SageMaker, Amazon S3 Storage services, AWS Glue
AWS Kinesis Services (Kinesis firehose, Kinesis video streams, Kinesis data streams, Kinesis analytics)
Redshift, Redshift Spectrum, DynamoDB, Athena, Amazon Quicksight, Elastic Map Reduce (EMR)
Rekognition, Lex, Polly, Comprehend, Translate, transcribe, BlazingText Word2Vec, DeepAR, Factorization Machines, Gradient Boosted Trees (XGBoost)
Image Classification (ResNet), IP Insights, K-Means Clustering, K-Nearest Neighbor (k-NN)
Latent Dirichlet Allocation (LDA), Linear Learner (Classification), Linear Learner (Regression)
Neural Topic Modelling (NTM), Object2Vec, Object Detection, Principal Component Analysis (PCA), Random Cut Forest, Semantic Segmentation, and Seqence2Sequence
Machine and Deep Learning Basics

Requirements

Basic AI/ML/AWS knowledge

Description

Update 01/02/2020: Section #13 on Machine Learning Implementation and Operations is released.

Machine and Deep Learning are the hottest tech fields to master right now! Machine/Deep Learning techniques are widely adopted in many fields such as banking, healthcare, transportation and technology. Amazon has recently introduced the AWS machine Learning Certification Speciality exam and its quite challenging! AWS Certified Machine Learning Specialty is targeted at data scientists and developers who design, train and deploy AI/ML models to solve real-world challenging problems.