Designing and Implementing a Data Science Solution on Azure (DP-100T01)
DP-100 is an outstanding data science course that our students have found of great value.
It is important to reinforce however that this course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.
Before attending this course, students MUST have:
- Deep experience in training, building, and evaluating machine learning models
- A solid command of Python and its data science libraries including: pandas, scikit-learn, matplotlib, and seaborn.
- Experience with statistical learning concepts such as: regression, correlation, normalization, feature importance, and AUC - ROC
- Azure Fundamentals
Who should attend
This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.
Before attending this course, students must have:
- A fundamental knowledge of Microsoft Azure
- Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
- Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.
- Introduction to Azure Machine Learning
- No-Code Machine Learning with Designer
- Running Experiments and Training Models
- Working with Data
- Compute Contexts
- Orchestrating Operations with Pipelines
- Deploying and Consuming Models
- Training Optimal Models
- Interpreting Models
- Monitoring Models
Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.
Currently no local training dates available. Оставить заявку
Time zone: Eastern European Summer Time (EEST)