English | Size: 9.09 GB
Genre: eLearning
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
Who this course is for:
Developers and data scientists wanting to get certified in AWS Machine Learning
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