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Snowflake for Data Science: Intro to Snowpark ML for Python
  • About
  • Outcomes
  • Project details
  • Testimonials
  • Recommendations
  1. Browse
  2. Data Science
  3. Machine Learning
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Snowflake for Data Science: Intro to Snowpark ML for Python

Mohamed Touiti

Instructor: Mohamed Touiti

Included with Coursera Plus

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Guided Project
Learn, practice, and apply job-ready skills with expert guidance
Intermediate level

Recommended experience

Recommended experience

Intermediate level

Solid familiarity with Machine Learning concepts such as pandas dataframes, sklearn pipelines, UDFs.

Prior experience in Snowflake is also required.

2.5 hours
Learn at your own pace
Hands-on learning
Learn more

Guided Project
Learn, practice, and apply job-ready skills with expert guidance
Intermediate level

Recommended experience

Recommended experience

Intermediate level

Solid familiarity with Machine Learning concepts such as pandas dataframes, sklearn pipelines, UDFs.

Prior experience in Snowflake is also required.

2.5 hours
Learn at your own pace
Hands-on learning
Learn more
  • About
  • Outcomes
  • Project details
  • Testimonials
  • Recommendations

What you'll learn

  • Load data into Snowflake and setup Python workspace.

  • Transform data using Snowpark ML API and build a preprocessing pipeline.

  • Train an XGBoost model using the Snowpark ML Model API and deploy the model as a UDF for inference.

Skills you'll practice

  • Snowflake Schema
  • Python Programming
  • Data Pipelines
  • Regression Analysis
  • Data Science
  • Machine Learning
  • Applied Machine Learning
  • Feature Engineering
  • Data Processing
  • Predictive Modeling
  • Data Transformation

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English
No downloads or installation required

Only available on desktop

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Learn, practice, and apply job-ready skills in less than 2 hours

  • Receive training from industry experts
  • Gain hands-on experience solving real-world job tasks
  • Build confidence using the latest tools and technologies

About this Guided Project

Picture this: a data-driven revolution where Snowflake's data cloud and Python converge to unveil new dimensions in data science. Get ready for a transformative learning experience with our Snowpark ML for Python guided-project!

This Guided Project was crafted to help data scientists leverage Snowpark ML for Python. By the end of this project, you will be equipped to seamlessly construct an end-to-end machine learning workflow – commencing from data preprocessing, advancing through model training, and culminating in the realms of inference and deployment. Embark on this 2-hour journey where you will learn how to: Load data into Snowflake and setup Python workspace. Transform data using Snowpark ML API and build a preprocessing pipeline. Train an XGBoost model using the Snowpark ML Model API and deploy the model as a UDF for inference. To achieve the project's objectives, you will create a machine learning model (XGBoost) using Snowpark in Snowflake. The project scenario involves stepping into the role of a data scientist for a renowned Diamond retailer. The goal is to address challenges faced by salespeople in accurately estimating diamond prices. Leveraging a dataset with attributes like Carat, Weight, and Cut Quality stored in Snowflake, you will build a predictive model to recommend optimal diamond purchase prices. This project uniquely integrates Snowflake's robust data capabilities with the flexibility of Python for machine learning. Success requires an intermediate level of Python and prior experience with Snowflake. Don't miss the chance to enhance your data science skills with Snowpark ML and Python!

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. •

    Load the project's data into a Snowflake database.

  2. •

    Set-up your Snowpark for Python workspace.

  3. •

    Data Preprocessing, Transform categorical variables.

  4. •

    Build the Preprocessing Pipeline.

  5. •

    Build an XGBoost Regression Model.

  6. •

    Fine-Tune Model’s Hyperparameters using "GridSearchCV".

  7. •

    Deploy the model as a Vectorized User-Defined Function (UDF).

Recommended experience

Solid familiarity with Machine Learning concepts such as pandas dataframes, sklearn pipelines, UDFs.

Prior experience in Snowflake is also required.

8 project images

Instructor

Mohamed Touiti
Mohamed Touiti
Coursera Project Network
4 Courses•9,626 learners

Offered by

Coursera Project Network

Offered by

Coursera Project Network

The Coursera Project Network is a select group of instructors who have demonstrated expertise in specific tools or skills through their industry experience or academic backgrounds in the topics of their projects. If you're interested in becoming a project instructor and creating Guided Projects to help millions of learners around the world, please apply today at teach.coursera.org.

How you'll learn

  • Skill-based, hands-on learning

    Practice new skills by completing job-related tasks.

  • Expert guidance

    Follow along with pre-recorded videos from experts using a unique side-by-side interface.

  • No downloads or installation required

    Access the tools and resources you need in a pre-configured cloud workspace.

  • Available only on desktop

    This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.

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Frequently asked questions

By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.

Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.

You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.

Guided Projects are not eligible for refunds. See our full refund policyOpens in a new tab.

Financial aid is not available for Guided Projects.

Auditing is not available for Guided Projects.

At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.

Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.

More questions

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