East 2019Featured Postposted by ODSC Team Mar 13, 2019
6 Reasons Your Business is Falling Behind the AI Race
BusinessAccelerate AIposted by Elizabeth Wallace Mar 22, 2019
7 Steps to Go From Data Science to Data Ops
ConferencesDataOpsAccelerate AIposted by Elizabeth Wallace Mar 19, 2019
How Will AI Nudging Affect Our Privacy?
Artificial IntelligenceAccelerate AIposted by Elizabeth Wallace Mar 18, 2019
Stream Data Processing with Apache Kafka and TensorFlow
Deep LearningKafkaTensorFlowConferencesposted by Yong Tang Mar 18, 2019
The Promise of Retrofitting: Building Better Models for Natural Language Processing
Machine LearningNLPConferencesposted by Catherine Havasi Mar 15, 2019
4 of the Best Data Science YouTube Channels
Featured Postposted by Elizabeth Wallace Mar 15, 2019
ConferencesMachine LearningMachine Learningposted by ODSC Team Mar 22, 2019
What Will the Next Generation of Data Scientists Look Like?
OpinionCareer Insightsposted by Daniel Gutierrez, ODSC Mar 21, 2019
Watch: Standardized Data Science – The Team Data Science Process with Python
Modelingposted by ODSC Team Mar 20, 2019
Stream Data Processing with Apache Kafka and TensorFlow
Deep LearningKafkaTensorFlowConferencesposted by Yong Tang Mar 18, 2019
The Promise of Retrofitting: Building Better Models for Natural Language Processing
Machine LearningNLPConferencesposted by Catherine Havasi Mar 15, 2019
4 of the Best Data Science YouTube Channels
Featured Postposted by Elizabeth Wallace Mar 15, 2019
From the New York Times to NASA: How Text Analysis Saves Lives
Deep LearningText AnalysisDeep Learningposted by Alexander Spangher Mar 14, 2019
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The Importance of P-Values in Data Science
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Reviewing Amazon’s Machine Learning University – Is it Worth All of the Hype?
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Learn Interpretability for Data Science
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The Anatomy of K-Means Clustering
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An Introduction to Natural Language Processing (NLP)
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The 2019 Data Science Dictionary – Key Terms You Need to Know
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Challenges and Opportunities in Applying Machine Learning – Alex Jaimes | ODSC East 2018
There are many opportunities in applying machine learning, whether as an individual developer, or in a business. How do you get started? In this talk, Alex gives an overview that separates fact from fiction and proposes processes to find opportunities for applying ML.
This includes understanding where ML can have the biggest impact while avoiding common pitfalls. He emphasizes how sometimes, improvements in processes can significantly outweigh algorithmic improvements. He examines data collection and quality, definitions used (e.g, for labeling), metrics, objective functions, overfitting, and the cost of different types of errors, among others.
BusinessAccelerate AIposted by Elizabeth Wallace Mar 22, 2019
7 Steps to Go From Data Science to Data Ops
ConferencesDataOpsAccelerate AIposted by Elizabeth Wallace Mar 19, 2019
How Will AI Nudging Affect Our Privacy?
Artificial IntelligenceAccelerate AIposted by Elizabeth Wallace Mar 18, 2019
What are Some of the Best Practices for Hiring Data Scientists?
Accelerate AIConferencesAccelerate AIposted by Luke Coughlin Mar 14, 2019
Artificial Intelligence and Machine Learning in Practice: Anomaly Detection in Army ERP Data
Accelerate AIMachine LearningAccelerate AIposted by ODSC Team Mar 12, 2019
Deep LearningKafkaTensorFlowConferencesposted by Yong Tang Mar 18, 2019
The Promise of Retrofitting: Building Better Models for Natural Language Processing
Machine LearningNLPConferencesposted by Catherine Havasi Mar 15, 2019
Business Applications of AI-Powered NLP
Accelerate AINLPAccelerate AIposted by Elizabeth Wallace Mar 8, 2019
10 Trending Data Science Topics at ODSC East 2019
Conferencesposted by ODSC Team Mar 7, 2019
Learn Interpretability for Data Science
explainable AIMachine LearningConferencesposted by Rajiv Shah Mar 5, 2019
Linguistics In NLP: Why So Complex?
ConferencesNatural Language ProcessingNLPConferencesposted by Elizabeth Wallace Mar 1, 2019
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- ODSC East 2019 Career Expo May 3-4
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ConferencesDataOpsAccelerate AIposted by Elizabeth Wallace Mar 19, 2019
Introduction to R Shiny
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Data Ops: Running ML Models in Production the Right Way
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7 Tips for Visual Search at Scale
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How to Balance Work and Learn More About Data Science
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