DP-100 Azure Machine Learning in Python-Basic to Advance2023

🚀 Course Title: DP-100 Azure Machine Learning in Python - Basic to Advanced 🎓

Headline: Master Machine Learning Workflow Orchestration with Azure ML & Python!

🔑 Course Description:

Embark on a comprehensive journey into the realm of machine learning with our "DP-100 Azure Machine Learning in Python" course. This meticulously crafted curriculum is tailored for entry-level Data Scientists, aspiring Python developers, and experts alike who wish to delve into the vast capabilities of AzureML. Whether you're starting from scratch or looking to expand your skill set, this course will guide you through the latest advancements in the DP-100 Certification.

With a focus on creating, deploying, and managing machine learning models, this course is designed to cater to a wide range of proficiency levels. You'll learn to orchestrate your machine learning workflows to automate training, deployment, and management processes using Python. This includes hands-on experience with cloud and on-premises environments.

Why Take This Course? 🤔

• No Programming Background Required: Designed for those new to programming, this course will help you get started with AzureML.
• Up-to-Date Content: Based on the latest changes in the DP-100 Certification, ensuring you learn the most current practices.
• For Experts Too: Ideal for professionals looking to create and deploy a machine learning environment in production, whether it's in the cloud or on-premises.

Course Highlights:

• 🎫 Azure Account & ML Studio Access: Learn to set up a free Azure account and navigate the AzureML studio interface.
• Complete AzureML Architecture: Understand the comprehensive architecture of Azure Machine Learning.
• Python Virtual Environment Setup: Create a virtual environment for Python using an yml file on your local machine.
• Local vs. Azure ML Training: Train machine learning and deep learning algorithms on your local machine and then execute the same code in Azure ML.
• Storage Integration: Attach various storage options like Azure SQL DB to Azure ML and understand their roles.
• ML Deployment & Prediction: Deploy ML models for real-time prediction in Azure.
• Pipeline Creation & Hyperparameter Tuning: Build machine learning pipelines and fine-tune hyperparameters efficiently.
• Azure Auto ML Implementation: Explore the power of Azure Auto ML to automate model selection and training.
• No-Code ML Lifecycle Management: Discover how to create a complete machine learning lifecycle without writing code using Azure Machine Learning Studio.
• Understanding Components & Workflow: Gain insights into the components involved in building, deploying, and maintaining machine learning models and how they interplay.
• Azure Machine Learning Workspace Provisioning: Learn to set up an Azure Machine Learning workspace.
• Tools & Interfaces Utilization: Effectively use tools and interfaces provided by Azure Machine Learning.
• Code-Based Experiments: Run experiments in an Azure Machine Learning workspace using code.

📚 Learning Outcomes:

• Provision an Azure Machine Learning workspace.
• Navigate and utilize tools and interfaces within Azure Machine Learning.
• Run code-based experiments in an Azure Machine Learning workspace.
• Understand the architecture, components, and workflow of Azure Machine Learning.
• Train machine learning models both locally and in Azure ML environments.
• Deploy models for real-time prediction and manage them efficiently.
• Build, automate, and tune machine learning pipelines with ease.
• Implement Azure Auto ML to streamline the model selection and training process.
• Manage the complete machine learning lifecycle with or without coding.

🚀 Join Us and Elevate Your Data Science Skills with Azure ML & Python Today! 🚀

Whether you're a budding data scientist or an experienced developer looking to expand your expertise, this course offers a robust foundation in Azure Machine Learning with Python. Enroll now to harness the full potential of machine learning and automate your workflows for unparalleled efficiency and scalability.