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--- en:iot-reloaded:hints_for_further_readings_on_ai [2023/10/31 08:45] – created margus
+++ en:iot-reloaded:hints_for_further_readings_on_ai [2024/12/03 18:07] (current) – ktokarz
@@ Line 1: / Line 1: @@
 ====== Hints for Further Readings on AI ======
+This chapter has covered some of the most widely used data analysis methods applicable in sensor data analysis, which might be typical for IoT systems. However, it is only the surface of the exciting world of data analytics and AI. The authors suggest the following online resources besides the well-known online learning platforms to dive into this world.
+=== Useful Python libraries ===
+  * **SciKit learn** library for general data analysis and fundamental AI algorithms ((https://scikit-learn.org/stable/|SciKit learn)): a handy Python library with complemented detailed documentation and example code snippets.
+  * Time series library **TSlearn** ((https://tslearn.readthedocs.io/en/stable/index.html|TSlearn)): provides very insightful comments and documentation on different algorithms and approaches widely used in time series analysis.
+  * **Pytorch** ((https://pytorch.org/|Pytorch)) and **Keras** ((https://keras.io/|Keras)): community pages for those who seek deep learning resources and more complex models in comparison to those that were covered in this chapter.
+  * **Scipy** ((https://scipy.org/|Scipy)): a vibrant library for statistical models in Python.
+=== Useful tools ===
+  * **Orange** ((https://orangedatamining.com/|Orange)): visual programming tool for data analysis and visualisation.
+  * **Weka** ((https://waikato.github.io/weka-wiki/documentation/|Weka)): a ready-to-use data analysis and visualisation tool.

en/iot-reloaded/hints_for_further_readings_on_ai.1698741901.txt.gz · Last modified: 2023/10/31 08:45 by margus