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Introducing SAP HANA Machine Learning

Building and Evaluating Regression Models

Developing Regression Models with the Python Machine Learning Client for SAP HANA

<p>In this enablement you will apply the machine learning workflow using the Python machine learning Client for SAP HANA, also known as hana-ml. You will look at the steps from feature extraction to model evaluation, using the California housing dataset as scenario.</p>

<div>Learn to develop regression models with Python Machine Learning Client for SAP HANA. Explore feature extraction, model building with PAL, and HGBT algorithm implementation. Master model evaluation and optimization. Let's get started!<br><br># This description was auto-generated #</div>

Python ML for SAP HANA_V2_The latest version

Welcome to the training on developing regression models with the Python Machine Learning Client for SAP HANA. This learning journey is for developers and data scientists who want to understand how to apply the machine learning workflow using the Python Machine Learning Client for SAP HANA. The steps from feature extraction to model evaluation using the California housing data set scenario will be illustrated. In this learning journey, you will learn to set up the connection to the SAP HANA database, build a regression model with SAP HANA Predictive Analysis Library, PAL. You will be able to explore the capabilities of the hybrid gradient boosting tree, HGBT, algorithm implementation. It is an SAP HANA optimized gradient boosting tree implementation supporting mixed feature types continuous and categorical as input. By the end of this learning journey you'll be able to perform model evaluation and optimization. Let's get started!

Python ML for SAP HANA_V2 (1)

<div><div><div><div><p><a href="https://sap.sharepoint.com/:v:/r/teams/HAML01/Shared%20Documents/General/HAML02_coll10/Teaser%20video/HAML02_V2%20(3).mp4?csf=1&amp;web=1&amp;e=4RMcnmhttp://" rel="nofollow noopener noreferrer">https://sap.sharepoint.com/:v:/r/teams/HAML01/Shared%20Documents/General/HAML02_coll10/Teaser%20video/HAML02_V2%20(3).mp4?csf=1&amp;web=1&amp;e=4RMcnmhttp://</a> <br><br>Welcome to the training on “Developing Regression models with the Python Machine Learning client for SAP HANA”&nbsp;</p></div><div><p>&nbsp;</p></div><div><p>This learning journey is for developers and data scientists who want to understand how to apply the machine learning workflow using the Python Machine Learning Client for SAP HANA (hana-ml).&nbsp;&nbsp;</p></div><div><p>The steps from feature extraction to model evaluation, using the California Housing dataset scenario will be illustrated.&nbsp;</p></div><div><p>During this learning journey, you will learn how to set up the connection to the SAP HANA database, and to build a regression model with SAP HANA Predictive Analysis Library (PAL).&nbsp;&nbsp;</p></div><div><p>You will be able to explore the capabilities of the HybridGradientBoostingTree (HGBT) algorithm. It is an SAP HANA optimized gradient boosting tree implementation supporting mixed feature types (continuous and categorical) as input.&nbsp;&nbsp;</p></div><div><p>By the end of this learning journey, you'll be able to perform Model Evaluation and Optimization.&nbsp;</p></div><div><p>Let's get started!&nbsp;</p></div></div></div></div>

Developing Regression Models with the Python Machine Learning Client for SAP HANA _Teaser Video

Welcome to the training on developing regression models with the Python Machine Learning Client for SAP HANA. This learning journey is for developers and data scientists who want to understand how to apply the machine learning workflow using the Python Machine Learning Client for SAP HANA, HANA Machine Learning. The steps from feature extraction to model evaluation using the California Housing Dataset scenario will be illustrated. In this learning journey, you will learn to set up the connection to the SAP HANA database. Build a regression model with SAP HANA Predictive Analysis Library, PAL. You will be able to explore the capabilities of the SAP HANA PAL Hybrid Gradient Boosting Tree, HGBT algorithm. It is an SAP HANA-optimized gradient boosting tree implementation, supporting mixed feature types, continuous and categorical as input. By the end of this learning journey, you'll be able to perform model evaluation and optimization. Let's get started!

<p>This live session will introduce you to the new Generative AI &amp; Machine Learning capabilities in SAP Sales Cloud Version 2</p>

SAP Sales Cloud Version 2 - Generative AI & Machine Learning

Learn to develop regression models with Python Machine Learning Client for SAP HANA. Explore feature extraction, model building with PAL, and HGBT algorithm implementation. Master model evaluation and optimization. Let's get started!<br><br># This description was auto-generated #

Developing Regression Models with the Python Machine Learning Client for SAP HANA

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