Category Archives: Machine Learning
Machine Learning in Oracle Database | Oracle
Oracle Machine Learning AutoML User Interface
A no-code user interface supporting AutoML on Oracle Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression.
Accelerate machine learning modeling using Oracle Autonomous Database as a high performance computing platform with an R interface. Use Oracle Machine Learning Notebooks with R, Python, and SQL interpreters to develop machine learningbased solutions. Easily deploy user-defined R functions from SQL and REST APIs with data-parallel and task-parallel options.
Data scientists and other Python users accelerate machine learning modeling and solution deployment by using Oracle Autonomous Database as a high-performance computing platform with a Python interface. Built-in automated machine learning (AutoML) recommends relevant algorithms and features for each model, and performs automated model tuning. Together, these capabilities enhance user productivity, model accuracy, and scalability.
Data scientists and data analysts can use this drag-and-drop user interface to quickly build analytical workflows. Rapid model development and refinement allows users to discover hidden patterns, relationships, and insights in their data.
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Machine Learning in Oracle Database | Oracle
RBI Shortlists 7 Global Consultancy Firms To Use Artificial Intelligence, Machine Learning To Improve Regulatory Supervision – Outlook India
RBI Shortlists 7 Global Consultancy Firms To Use Artificial Intelligence, Machine Learning To Improve Regulatory Supervision Outlook India
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RBI Shortlists 7 Global Consultancy Firms To Use Artificial Intelligence, Machine Learning To Improve Regulatory Supervision - Outlook India
Interpretable Machine Learning – GitHub Pages
Machine learning has great potential for improving products, processes and research.But computers usually do not explain their predictions which is a barrier to the adoption of machine learning.This book is about making machine learning models and their decisions interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression.The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local effects, and explaining individual predictions with Shapley values and LIME.In addition, the book presents methods specific to deep neural networks.
All interpretation methods are explained in depth and discussed critically.How do they work under the hood?What are their strengths and weaknesses?How can their outputs be interpreted?This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.
You can buy the PDF and e-book version (epub, mobi) on leanpub.com.
You can buy the print version on amazon.
About me: My name is Christoph Molnar, Im a statistician and a machine learner.My goal is to make machine learning interpretable.
Follow me on Twitter! @ChristophMolnar
Cover by @YvonneDoinel
This book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Interpretable Machine Learning - GitHub Pages