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

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cleanFiles/bookmap_Machine_Learning_for_Regression__en-US_learning-sap-com_lsc-prod.zip (MMCP-DROPZONE) - 2024-09-27T14:54:51.000Z

Setting Up the Environment

Exploring Data with SAP HANA DataFrames

Assessment

Introducing SAP HANA Machine Learning

Training a Regression Model with SAP HANA PAL

Understanding Model Evaluation and Optimization

Building and Evaluating Regression Models

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

Desenvolvimento de modelos de regressão com o cliente de machine learning Python para SAP HANA

Développement de modèles de régression avec le client d'apprentissage automatique Python pour SAP HANA

Desarrollo de modelos de regresión con el cliente de aprendizaje automático de Python para SAP HANA

使用面向 SAP HANA 的 Python 机器学习客户端开发回归模型

Python Machine Learning Client for SAP HANA を使用した回帰モデルの開発

Entwicklung von Regressionsmodellen mit dem Python-Client für maschinelles Lernen für SAP HANA

SAP HANA용 Python 기계 학습 클라이언트를 사용한 회귀 모델 개발

<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>

1_8fbo7jvl (video-connector) - 2024-09-26T02:09:23.452Z

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!

1_4t4d3yxd (video-connector) - 2024-09-23T09:03:21.291Z

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>

1_u6x0vtsr (video-connector) - 2024-09-12T09:53:37.694Z

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!

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