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♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
Inclusions in Koenig's Learning Stack may vary as per policies of OEMs
The exact prerequisites for a Machine Learning for Azure Databricks training may vary depending on the specific course or program you choose. However, here are some common prerequisites that you may be expected to have before enrolling in such a training:
1. Basic programming knowledge: You should be familiar with at least one programming language, preferably Python or Scala, as these are commonly used in Azure Databricks for machine learning.
2. Fundamental understanding of machine learning: Familiarity with basic concepts such as supervised and unsupervised learning, regression, classification, and clustering is expected.
3. Knowledge of data manipulation and processing: You should know how to handle and process data using libraries such as Pandas and NumPy in Python or similar tools in other programming languages.
4. Mathematics and statistics background: Understanding of linear algebra, calculus, and probability theory is beneficial for diving deeper into machine learning algorithms and techniques.
5. Experience with Apache Spark: Azure Databricks is built on top of Apache Spark, so understanding the basics of the Spark framework, as well as concepts like transformations, actions, and RDDs, can be helpful.
6. Familiarity with cloud platforms: As Azure Databricks is a cloud-based service, having some experience with Microsoft Azure or other cloud platforms is advantageous.
7. Optional: Knowledge of big data concepts and tools such as Hadoop, Hive, or Pig can be useful, but it is not typically a strict requirement for a Machine Learning for Azure Databricks training.
Before enrolling in a specific training program or course, it's always a good idea to review the specific prerequisites and requirements listed by the course provider.
Azure Databricks Certification Training empowers learners with comprehensive knowledge of machine learning techniques using the Azure Databricks platform. This course covers essential topics such as data transformation and ingestion, machine learning model development, and training using Spark MLlib. Students gain proficiency in using Azure Machine Learning Service for model management and deployment. Further, participants learn to implement collaborative analytics, streaming analytics, and recommendation systems, significantly enhancing their skills to excel in the evolving field of machine learning.
Machine Learning for Azure Databricks equips learners with key skills for big data processing and advanced analytics. The course offers benefits such as improved productivity and deeper insights by applying machine learning techniques on scalable data. Additionally, the integration of Azure Databricks enables enhanced security, streamlined workflows, and flexible cloud deployment options.
