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

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 2]

In part one of this blog posting series, we introduced machine learning models as a multifaceted and evolving topic. The complexity that gives extraordinary predictive abilities also makes these models challenging to understand. They generally don’t provide a clear explanation, and brands experimenting with machine learning are questioning whether they

Advanced Analytics | Machine Learning
Mark Hanson 0
Uncovering the unknowns: Why pharma is evolving from data analytics to data science

“There are known knowns … There are known unknowns … There are also unknown unknowns” This statement by Donald Rumsfeld is famous from the world of politics. But the phrase “unknown unknowns” was actually coined by a NASA data scientist to describe the huge possibilities presented to us by big data

Advanced Analytics | Analytics | Artificial Intelligence | Machine Learning
Makoto Unemi (畝見 真) 0
SAS Viya:Python API向けパッケージ:DLPyの最新版1.0拡張機能概要紹介

SASでは、従来からオープン・AIプラットフォームであるSAS Viyaの機能をPythonから効率的に活用いただくためのハイレベルなPython向けAPIパッケージであるDLPyを提供してきました。 従来のDLPyは、Viya3.3以降のディープラーニング(CNN)と画像処理(image action set)のために作成された、Python API向けハイレベルパッケージです。 DLPyではKerasに似たAPIを提供し、より簡潔なコーディングで高度な画像処理やCNNモデリングが可能でした。 そして、この度、このDLPyが大幅に機能拡張されました。 最新版DLPy1.0では、以下の機能が拡張されています。 ■ 従来からの画像データに加え、テキスト、オーディオ、そして時系列データを解析可能 ■ 新たなAPIの提供: ・ RNN に基づくタスク: テキスト分類、テキスト生成、そして 系列ラベリング(sequence labeling) ・ 一般物体検出(Object Detection) ・ 時系列処理とモデリング ・ オーディオファイルの処理と音声認識モデル生成 ■ 事前定義ネットワーク(DenseNet, DarkNet, Inception, and Yolo)の追加 ■ データビジュアライゼーションとメタデータハンドリングの拡張 今回はこれらの拡張機能の中から「一般物体検出(Object Detection)」機能を覗いてみましょう。 SAS Viyaでは従来から画像分類(資料画像1.の左から2番目:Classification)は可能でした。例えば、画像に映っている物体が「猫」なのか「犬」なのかを認識・分類するものです。 これに加えて、DLPy1.0では、一般物体検出(資料画像1.の左から3番目:Object Detection)が可能になりました。 資料画像1. (引用:Fei-Fei Li & Justin Johnson & Serena Yeung’s Lecture

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 1]

As machine learning takes its place in numerous advances within the marketing ecosystem, the interpretability of these modernized algorithmic approaches grows in importance. According to my SAS peer Ilknur Kaynar Kabul: We are surrounded with applications powered by machine learning, and we’re personally affected by the decisions made by machines

Artificial Intelligence | Fraud & Security Intelligence | Machine Learning
John Maynard 0
Unasked fraud questions answered by AI

Artificial intelligence often seems misunderstood, especially in fraud. The same is true of machine learning. One of the amazing things about them is they ask the unasked questions. This occurs as artificial intelligence (AI) and machine learning (ML) go about their daily work. So, what is the unasked question? Too

Advanced Analytics | Machine Learning
Susan Kahler 0
Four machine learning strategies for solving real-world problems

There are four widely recognized styles of machine learning: supervised, unsupervised, semi-supervised and reinforcement learning. These styles have been discussed in great depth in the literature and are included in most introductory lectures on machine learning algorithms. As a recap, the table below summarizes these styles. For a comprehensive mapping

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