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.
Lambda and Kappa architecture. Designing IT architecture for real-time data processing.
Preparation of the microservice with the ML model for prediction use.
Structured and unstructured data. Relational databases and NoSQL databases.
Aggregations and reporting in NoSQL databases (on the example of the MongoDB or Cassandra)
Basics of object-oriented programming in Python in linear and logistic regression, neural network analysis using the sklearn, TensorFlow and Keras.
IT architecture of Big Data processing. Preparation of a virtual environment for spark. The first program PySpark.
Case study 1. Scam detection in real-time car damage reports using a prepared free environment. Part 1
Case study 1. Scam detection in real-time car damage reports using a prepared free environment. Part 2
Preparation of the Microsoft Azure environment. Case 2 Detection of anomalies and outliers in logged Ethernet network events. 1
Preparation of the Microsoft Azure environment. Case 2 Detection of anomalies and outliers in logged Ethernet network events. 2
SAS tools for Real-Time analysis.
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