Modelling, learning and prediction in batch mode (offline learning) and incremental (online learning) modes. Problems of incremental machine learning.
Data processing models in Big Data. From flat files to Data Lake. Real-time data myth and facts
NRT systems (near real-time systems), data acquisition, streaming and analytics.
Algorithms for estimating model parameters in incremental mode. Stochastic Gradient Descent.
Modern streaming application architectures
Preparation of the microservice with the ML model for prediction use.
Processing structured and unstructured data in Python. Function and Object-oriented connection to RDB and NoSQL
Aggregations and reporting in NoSQL databases (MongoDB)
Basics of object-oriented programming in Python in linear and logistic regression, neural network analysis using the sklearn, TensorFlow and Keras.
IT streaming architecture. Apache Spark and Jupyter notebook environment using docker tool. Analysis of data from Twitter.
Case study 1. Fraud Detection in credit card transactions in Spark environment Part 1
Case study 1. Fraud Detection in credit card transactions in Spark and Kafka environment Part 2
Preparation of the Microsoft Azure Databricks environment. Case 2 Detection of anomalies and outliers in logged Ethernet network events. 1
SAS ESP tools for Real-Time analysis.
SAS ESP tools for Real-Time analysis.
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