Creating data models using machine learning requires effective training data. This course will teach you how to feed your data model’s training process using data labeling for supervised training and unlabeled data for semi-supervised training.

Explore efficient fine-tuning of neural networks using LoRA (Low-Rank Adaptation) and PyTorch. Learn how to apply LoRA for parameter-efficient training of complex models in real-world scenarios.

Explore I2C communication protocols and their applications in integrating sensors with embedded systems.

Learn how to reduce dimensionality and improve model efficiency using Linear Discriminant Analysis (LDA) in Python. Discover how LDA helps with classification problems by maximizing class separability while reducing noise in high-dimensional data.

Explore the concepts of eigenvectors and eigenvalues and their applications in machine learning and data science.

This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud.

This course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including securely interconnecting networks, load balancing, autoscaling, infrastructure automation and managed services.

This is an introductory course on the Elastic Stack and the basics for how to use it for security operations. This course will teach you the fundamentals of Elasticsearch and Kibana.

Understand the fundamentals of Elasticsearch, focusing on data indexing, querying techniques, and practical applications for search functionalities.

ElasticSearch, Logstash, Kibana, Beats , Elastic search, search

ElasticSearch, Logstash, Kibana, Beats , Elastic search, search

ElasticSearch, Logstash, Kibana, Beats , Elastic search, search