Machine learning with Azure Databricks (DP-3014)

In this course, you will learn how to:
Gain proficiency in utilizing Azure Databricks, a cloud service offering a scalable platform for data analytics using Apache Spark.
Acquire practical knowledge and hands-on experience in employing Spark to transform, analyze, and visualize data at scale.
Develop skills in training machine learning models and evaluating their performance within the Azure Databricks environment.
Learn to leverage MLflow, an open-source platform for managing the machine learning lifecycle, seamlessly integrated with Azure Databricks.
Master the art of hyperparameter tuning and optimization using Hyperopt library, enhancing the efficiency of machine learning workflows.
Explore the simplicity and effectiveness of AutoML in Azure Databricks for automating the model building process.
Dive into the realm of deep learning, understanding concepts and training models for complex AI workloads like forecasting, computer vision, and natural language processing.
Training Prerequisites
To fully benefit from this course, please ensure you possess proficiency in Python for data exploration and machine learning model training using popular open-source frameworks such as Scikit-Learn, PyTorch, and TensorFlow.