Machine Learning

Get the latest machine learning algorithms and techniques

Analytics | Artificial Intelligence | Data Visualization | Machine Learning | Programming Tips
Melanie Carey 0
How SAS Visual Analytics' automated analysis takes customer care to the next level - Part 3

In the second of three posts on using automated analysis with SAS Visual Analytics, we used the automated analysis object to get a better understanding of our variable of interest, X-Sell and Up-sell Flag, and how it is influenced by other variables in our dataset. In this third and final

Analytics | Artificial Intelligence | Data Visualization | Machine Learning | Programming Tips
Melanie Carey 0
How SAS Visual Analytics' automated analysis takes customer care to the next level - Part 2

In the first of three posts on using automated analysis with SAS Visual Analytics, we explored a typical visualization designed to give telco customer care workers guidance on customers most receptive to upgrade their plans. While the analysis provided some insight, it lacked analytical depth -- and that increases the risk of  wasting time, energy and

Analytics | Artificial Intelligence | Data Visualization | Machine Learning | Programming Tips
Melanie Carey 0
How SAS Visual Analytics' automated analysis takes customer care to the next level - Part 1

You're the operations director for a major telco's contact center. Your customer-care workers enjoy solving problems. Turning irate callers into fans makes their day. They also hate flying blind. They've been begging you for deeper insight into customer data to better serve their callers. They want to know which customers

Analytics | Machine Learning
Alejandro Bolaños 0
Explicate! Entendiendo los modelos de Machine Learning (Parte 3: Individual Conditional Expectation)

Parte I: Introducción Parte II: Partial Dependence Plots Repasemos como llegamos hasta acá. Desde hace varios años los algoritmos de machine learning nos ofrecen una mejora sustancial en sus capacidades, son cada vez más precisos. Además, gracias a la optimización hiperparamétrica, el analista puede utilizar el tiempo de prueba y

Analytics | Artificial Intelligence | Internet of Things | Machine Learning
Javier Alexander Rengifo 0
El análisis predictivo: impactando los negocios y sus procesos de transformación digital

La tecnología y la sociedad están evolucionando en un entorno digital que exige cambios en el modelo de negocio, la infraestructura y la cultura de una organización. Sin embargo, uno de los mayores retos a los que se están enfrentando las empresas en este momento se basa en el desconocimiento

Internet of Things | Machine Learning
Christian Goßler 0
Lenin jagt Dr. Noh ohne Daten im Internet of Tuna (IoT7)

Lenin hebt sein Glas: „Auf unsere digitalisierte Service-Flotte und das zehnte angebundene Werk!“ Ich proste zurück: „Auf Ihren neuen Job!“ – „Ach, ich mache das Gleiche wie vorher: Machine Learning – insbesondere für das Internet of Things …“ Der Kellner unterbricht: „Wer wollte noch Thunfisch-Nigiri?“ – „Internet of Thunfisch“, lacht

Analytics | Data Management | Learn SAS | Machine Learning
Michael Herrmann 0
DevOps & SAS: Entwicklung und Betrieb aus einer Hand?

K(o)ennen Sie schon „DevOps“? Machen Sie SAS? Dann lohnt sich eventuell ein frischer Blick auf die Kombination! Denn immer mehr Unternehmen probieren, ihren produktiven Betrieb auch in die Hände der Software-Entwickler zu legen (2 von 3 laut Jenkins) – speziell in der Analyse, insbesondere beim agilen Modellieren und dem Veredeln

Advanced Analytics | Analytics | Customer Intelligence | Data Visualization | Machine Learning
Suneel Grover 0
SAS Customer Intelligence 360: Decision management, machine learning, and digital marketing

A typical day brings countless business decisions that affect everything from profitability to customer experience. What is a reasonable price point? Which audience segments should I personalize offers for? When should I recommend specific content earlier in a customer journey? Daily decisions like these can alter the trajectory of a

Advanced Analytics | Analytics | Customer Intelligence | Data Visualization | Machine Learning
Suneel Grover 0
SAS Customer Intelligence 360: Model management for competitive differentiation [Part 1]

The universe of customer experiences, digital analytics, personalization and decisioning is massive. At times, it can seem as complicated and vast as the galaxy itself. With intricate subjects underneath this umbrella, you can lose direction, wander aimlessly, or feel a misleading sense of success or failure. When you lose vision,

Analytics | Machine Learning
Chris Hartmann 0
Machine Learning: Mehr Effizienz im Planungsprozess

Sie glauben, dass Machine Learning die Rolle von Nachfrageplanern komplett ersetzen kann? Dann lesen Sie diesen Beitrag besser nicht. Wenn Sie jedoch der Ansicht sind, dass maschinelles Lernen den Planungsprozess automatisieren kann, so dass Nachfrageplaner effizienter arbeiten können, dann stimme ich Ihnen voll und ganz zu! Intelligente Automatisierungstechniken sind quasi

Analytics | Artificial Intelligence | Machine Learning
Makoto Unemi (畝見 真) 0
SAS Viya:一般物体検出(Object Detection)を試してみた

PythonからSAS Viyaの機能を利用するための基本パッケージであるSWATと、よりハイレベルなPython向けAPIパッケージであるDLPyを使用して、Jupyter NotebookからPythonでSAS Viyaの機能を使用して一般物体検出(Object Detection)を試してみました。  今回は、弊社で用意した数枚の画像データを使用して、処理の流れを確認するだけなので、精度に関しては度外視です。  大まかな処理の流れは以下の通りです。 1.必要なパッケージ(ライブラリ)のインポートとセッションの作成 2.一般物体検出向け学習用データの作成 3.モデル構造の定義 4.モデル生成(学習) 5.物体検出(スコアリング)  1.必要なパッケージ(ライブラリ)のインポートとセッションの作成 swatやdlpyなど、必要なパッケージをインポートします。 from swat import * import sys sys.path.append(dlpy_path) from dlpy.model import * from dlpy.layers import * from dlpy.applications import * from dlpy.utils import * from dlpy.images import ImageTable   from dlpy.splitting import two_way_split from dlpy.blocks import *

Advanced Analytics | Analytics | Artificial Intelligence | Customer Intelligence | Data Visualization | Machine Learning
Suneel Grover 0
SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 3]

In parts one and two of this blog posting series, we introduced machine learning models and the complexity that comes along with their extraordinary predictive abilities. Following this, we defined interpretability within machine learning, made the case for why we need it, and where it applies. In part three of

1 2 3 4 5 13

Back to Top