Databricks courses offered by P2L :
Apache Spark Programming with Databricks - This course uses a case study-driven approach to explore the fundamentals of Spark Programming with Databricks, including Spark architecture, the DataFrame API, query optimization, and Structured Streaming.
Scalable Machine Learning with Apache Spark - This course guides students through the process of building machine learning solutions using Spark. You will build and tune ML models with SparkML using transformers, estimators, and pipelines. This course highlights some of the key differences between SparkML and single-node libraries such as sci-kit-learn.
Scalable Deep Learning with TensorFlow and Apache Spark - This course starts with the basics of the tf.keras API including defining model architectures, optimizers, and saving/loading models. You then implement more advanced concepts such as callbacks, regularization, TensorBoard, and activation functions.
For more information, please check P2L's website.