106 – Artificial Intelligence Tools and Techniques
Dmytro Peleshko; Olena Vynokurova.
Annotation
Introduction. AWS (AWS management console and how to use it; using AWS SIMPLE STORAGE SERVICE (S3); automating work with AWS resources using PYTHON; elements of ML in AWS SAGEMAKER; AWS Lambda). Scikit and Unsupervised Learning (metrics; clustering; anomaly detection; dimensionality reduction; tracking neural models; tensor board; weights and Biases; neptune). Model optimization (Auto ml; hyper opt; ray tune). Model deploying (docker; TorchServe deployment; fastAPI deployment).
Objectives
Abstract thinking, analysis and synthesis. Ability to apply knowledge in practical situations. Ability to search, process and analyse information from various sources. Ability to make a reasoned decision.
Keywords
Machine learning, clustering, neural network, hyperparameter, TensorBoard, Tensorflow, Model-optimization techniques, quantization, containerization.
Date of Creation
8.11.2024
Duration
6-8 hours
Language
English
License
ISBN
Literature
- Andreas C. Müller, Sarah Guido. Introduction to Machine Learning with Python. A Guide for Data Scientists.O’Reilly Media. 2016. 392 p.
- Andreas Wittig, Michael Wittig. Amazon Web Services in Action. An in-depth guide to AWS- Manning Publications Co. 2023. 554 p.
- Dr. Saket, S.R. Mengle, Maximo Gurmendez. Mastering Machine Learning on AWS. Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow. Packt Publishing 2019. 293 p.
- Raúl Garreta, Guillermo Moncecchi. Learning scikit-learn: Machine Learning in Python. Packt Publishing. 2013. 118 p.
- Suman Kalyan Adari, Sridhar Alla. Beginning Anomaly Detection Using Python-Based Deep Learning. Implement Anomaly Detection Applications with Keras and PyTorch. Apress. 2024. 538 p.
- Tarek Amr. Hands On Machine Learning with scikit-learn and Scientific Python Toolkits. A practical guide to implementing supervised and unsupervised machine learning algorithms in Python. Packt Publishing. 2020. 368 p.