Ai+ Training

Introduction to Scikit-learn: Machine learning in Python by Thomas Fan

Modern Machine Learning in R Part I by Jared Lander

Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values by Thomas Fan

Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection by Thomas Fan

The Life of Scikit-learn: from Tech to People by Gaël Varoquaux, PhD

Modern Machine learning in R Part II by Jared Lander

Rainforest XPRIZE: Harnessing Data for Good by Peter Houlihan

Codeless Reinforcement Learning: Building a Gaming AI by Corey Weisinger

Echo State Networks for Time-Series Data by Teal Guidici, PhD

Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression by Thomas Fan

Probabilistic Programming and Bayesian Inference with Python by Lara Kattan

Uplift Modeling Tutorial: From Predictive to Prescriptive Analytics by Victor Lo, PhD

Hands-on Reinforcement Learning with Ray RLlib by Paco Nathan

Customer2Graph: Powering Customer Analytics with Graph Representations by Srinivas Chilukuri and Kapil Jain

Intelligibility Throughout the Machine Learning Life Cycle by Jenn Wortman Vaughan, PhD

Prioritize ML Operations at Any Maturity Level by Diego Oppenheimer

Beyond OCR: Using Deep Learning to Understand Documents by Eitan Anzenberg, PhD

Bayesian Statistics Made Simple by Allen Downey, PhD

End to End Modeling & Machine Learning by Jordan Bakerman, PhD and Ari Zitin

How AI is Changing the Shopping Experience by Sveta Kostinsky and Marcelo Benedetti

Data Science for Suicide Prevention by Jennifer Redmon and Dr. Annie Ying

StructureBoost: Gradient Boosting with Categorical Structure by Brian Lucena

What Really Matters in Evaluating Machine Learning Models: Swap-Ins / Swap-Outs and How to Use Them by Seth Weidman

Advances and Frontiers in Auto AI & Machine Learning by Lisa Amini, PhD

Introduction to Generative Modeling Using Quantum Machine Learning by Luis Serrano, PhD and Kaitlin Gili and Alejandro Perdomo, PhD

Predicting Model Failures in Production by Aravind Chandramouli, PhD

GPU-accelerated Data Science with RAPIDS by John Zedlewski and Corey Nolet

Solving Problems with Both Text and Numerical Data Using Gradient Boosting by Stanislav Kirillov

Uncertainty Sampling and Diversity Sampling by Robert Munro, PhD

A Comparison of Topic Modeling Methods in Python by Russell Martin, PhD

Just Machine Learning by Tina Eliassi-Rad, PhD

Machine Learning for Biology and Medicine by Sriram Sankararaman, PhD

What if We Could Use Machine Learning Models as Database Tables? by Jorge Torres

Reinforcement Learning Research with the Dopamine Framework by Pablo Samuel Castro, PhD

Building a ML Serving Platform at Scale for Natural Language Processing by Kumaran Ponnambalam

The Bayesians are Coming! The Bayesians are Coming, to Time Series by Aric LaBarr, PhD

Interpretable Machine Learning with Python by Serg Masis

Building ML Models in a Cloud Environment by Bill Wright,Martin Isaksson and Robert Lundberg

The Fundamentals of Statistical Time Series Forecasting by Jeffrey Yau, PhD

Maximizing Dataset Potential: Challenges, Considerations & Best Practices by Soo Yang

Rapid Data Exploration and Analysis with Apache Drill by Charles Givre

MLOps in DL Model Development by Anna Petrovicheva

Framework for Model Monitoring at Scale by Josh Poduska and Dr. James Pearce

End-to-end AI Application Development with Programmatic Supervision by Alex Ratner, PhD

Build an ML pipeline for BERT models with TensorFlow Extended – An end-to-end Tutorial by Hannes Hapke

Data Science: How Do We Achieve the Most Good and Least Harm? by Megan Price, PhD

Model Governance: A Checklist for Getting AI Safely to Production by David Talby, PhD

Lessons from KPI Monitoring and Diagnosis at Scale by Peter Bailis, PhD

Unify Analytics – Combine Strengths of Data Lake and Data Warehouse by Paige Roberts

Keras from Soup to Nuts – An Example Driven Tutorial by Sujit Pal

Modern and Old Reinforcement Learning Part 1 by Leonardo De Marchi

AlphaStar: Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning by Oriol Vinyals, PhD

Natural Language Processing with PyTorch by Yashesh A. Shroff, PhD and Ravi Ilango

Modern and Old Reinforcement Learning Part 2 by Leonardo De Marchi

Deep Learning (with TensorFlow 2) by Dr. Jon Krohn

Conversational AI with DeepPavlov by Mikhail Burtsev, PhD and Daniel Kornev

Learning with Limited Labels by Shanghang Zhang, PhD

Interacting with Deep Generative Models for Content Creation by Bolei Zhou, PhD

State of the art AI Methods with TensorFlow: Transfer Learning, RL and GANs by Daniel Whitenack, PhD

Ludwig, a Code-Free Deep Learning Toolbox by Piero Molino, PhD

Building Content Embedding with Self Supervised Learning by Sijun He and Kenny Leung

Continuous-time Deep Models for Forecasting Sparse Time Series by David Duvenaud, PhD

A Hands-On Tutorial for Training Interpretable Variational Autoencoders Using siVAE by Gerald Quon, PhD and Yongin Choi

Testing Production Machine Learning Systems by Josh Tobin, PhD

Applied Deep Learning: Building a Chess Object Detection Model with TensorFlow by Joseph Nelson

Learning Intended Reward Functions: Extracting all the Right Information from All the Right Places by Anca Dragan, PhD

The Era of Brain Observatories: Open-Source Tools for Data-Driven Neuroscience by Ariel Rokem, PhD

Making Deep Learning Efficient by Kurt Keutzer, PhD

Introduction to Shiny Application Development by Bethany Poulin

Fast Data Access in R and Python with Apache Arrow by Neal Richardson,PhD

Taking Unique Advantage of High Missing Data Scenarios by Anne Lifton

How to Stop Worrying and Start Tackling AI Bias by Jett Oristaglio

Some Failures and Lessons Learned Using AI in our AI Company by Dustin Burke and Borys Drozhak

The Rise of MLOps by Seph Mard

Realizing Value through DataRobot’s AI-Powered Apps by Ina Ko

Lessons Learned with Data & Storytelling by Danny Ma, Kate Strachnyi, Jen Underwood, Susan Walsh, Ben Taylor, PhD

Experimentation, Metrics and Analytics: An Ecosystem for Data Informed Decisions by Eric Weber

Fireside Chat with Jacqueline Ros Amable - AI in Climate Tech by Ryan Sevey and Jacqueline Amable

Components of AI Infrastructure & MLOps by Michael Balint

Building an Analytics COE: One Leader's Story by Edward M. Young

Solving Practical Computer Vision Problems in 10 Minutes by Anton Kasyanov and Ivan Pyzow

Hands-on Data Science for Software Developers -- A Live Coding Session with Data Robot Self-Service by David Gonzalez

A Tutorial on Robust Machine Learning Deployment by Tim Whittaker and Rajiv Shah, PhD

The AI Practitioner Series - Data Prep Walkthrough (A Reusable Framework!) by Sean Smith and Shyam Ayyar

Communicating COVID: Visualization, Models, and Uncertainty during a Pandemic by Jonathan Industries

Semantic Scholar and the Fight Against COVID-19 by Oren Etzioni, PhD

Bayesian Workflow as Demonstrated with a Coronavirus Example by Andrew Gelman, PhD

What to Expect When You Are Expecting Robots - The Future of Human-Robot Collaboration by Julie A. Shah, PhD

Creating Equality and Inclusivity with Feature Engineering by Vida Williams

Using Artificial Intelligence to Save Lives at Birth by Charles Onu, PhD

The State of Serverless and Applications to AI by Joe Hellerstein, PhD

Diversity in Data Science: Challenges and Possibilities  by Marie desJardins, PhD

Getting Started with Pandas for Data Analysis by Boris Paskhaver

ML Easel – Tredence’s Data Science and ML Engineering Workbench by Changa Reddy

Painting with Data: Introduction to d3.js by Ian Johnson

Data Visualization: From Jupyter to Dashboards by David Yerrington

Exploring the Interconnected World: Network/Graph Analysis in Python by Noemi Derzsy, PhD

Best Practices for Optimizing Migration to the Cloud by Ernie Ostic

State-of-the-Art Natural Language Processing with Spark NLP by David Talby, PhD

Evaluating and Testing Natural Language Processing Models by Sameer Singh, PhD

Topic-Adjusted Visibility Metric for Scientific Articles by Tian Zheng, PhD

Language Complexity and Volatility in Financial Markets: Using NLP to Further our Understanding of Information Processing by Ahmet K. Karagozoglu, Ph.D.

Deep Learning-Driven Text Summarization & Explainability by Nadja Herger, PhD and Nina Hristozova Viktoriia Samatova

Natural Language Processing: Feature Engineering in the Context of Stock Investing by Frank Zhao

Remote HPCC Systems/ECL Training by Bob Foreman and Hugo Watanuki

Training Conversational Agents on Noisy Data by Phoebe Liu

Transfer Learning in NLP by Joan Xiao, PhD

Accelerating NLP Model Training and Deployment with PyTorch by Prasanth Pulavarthi

Are We Ready for the Era of Analytics Heterogeneity? Maybe… but the Data Says No by Marinela Profi

Health AI: What's Possible Now and What's Hard by Suchi Saria, PhD

A Secure Collaborative Learning Platform by Raluca Ada Popa, PhD

Data for Good: Ensuring the Responsible Use of Data to Benefit Society by Jeannette M. Wing, PhD

Our Applied AI Future by Ben Taylor, PhD

Applying AI to Real World Use Cases by John Montgomery

Generalized Deep Reinforcement Learning for Solving Combinatorial Optimization Problems by Azalia Mirhoseini, PhD

Frontiers of Probabilistic Machine Learning by Zoubin Ghahramani, PhD

The Future of Computing is Distributed by Ion Stoica, PhD

Overcoming Obstacles to AI Execution: Trust, Scale, and Reasoning by Mark Weber

Going Beyond FAIR to Create a Connected Data Ecosystem by Susan Gregurick, PhD

A Human-Machine Collaboration Built on Trust and Accountability by Dr. Biplav Srivastava

Business Skills for Data Scientists by Liz Sander, PhD

How Google Uses AI and Machine Learning in the Enterprise by Rich Dutton

Strategies for Building AI-ready Data Sources and (Semi)autonomous Reasoning Agents Operating on Top of Them by Marcin von Grotthuss, PhD

Inverse Reinforcement Learning for Financial Applications by Igor Halperin, PhD

Solving Real-life Challenges in Detecting Cognitive Diseases from Speech using ML by Jekaterina Novikova, PhD

Jupyter as an Enterprise "Do It Yourself" (DIY) Analytic Platform by Dave Stuart

Tackling Ethical Risk and Bias in Machine Learning Applications by Javed Ahmed, PhD

What if AI Could Craft the Next Generation of your AI? by Yonatan Geifman, PhD

A Quick, Practical Overview of KNIME Analytics Platform by Paolo Tamagnini

Personalize.AI: Transforming Businesses Through Personalization by Gopi Vikranth and Dr. Prakash

Leverage Data Lineage to Maximize the Benefits of AI and Big Data by Ernie Ostic

Integrating Open Source Modeling with SAS Model Manager by Scott Lindauer, PhD and Diana Shaw

Improving Your Data Visualization Flow with Altair and Vega-Lite by Rachel House

An Overview of Algorithmia: How to Deploy, Manage, and Scale Your Machine Learning Model Portfolio by Kristopher Overholt

DataRobot Enterprise AI Platform: End-to-End Demonstration by Andy Lofgreen

Responsible AI with Azure Machine Learning by Mehrnoosh Sameki, PhD

Accelerate Time-to-Model by Simplifying the Complexity of Feature Engineering by Daniel B Gray and John Lynch

[Deep Learning] Fresh Data in Days Instead of Months by Anthony Sarkis

Supercharge your Training Data Quality with Samasource by Abha Laddha

Budgeting, Building & Scaling Data Labeling Operations by Soo Yang

Implementing an Automated X-Ray Images Data Pipelines, the Cloud-native Way! by Guillaume Moutier

DataOps: The Secret Advantage for ML and AI Success by Cody Rich

Automated Model Management with ML Works by Pavan Nanjundaiah

Next-Generation Big Data Pipelines with Prefect and Dask by Aaron Richter, PhD

How to Increase ML Server Utilization With MLOps Visualization Dashboards by Yochay Ettun

HPCC Systems – The Kit and Kaboodle for Big Data and Data Science by Bob Foreman and Hugo Watanuki

Jumpstart Your Data Science Career with The Data Incubator by Sierra King

Meet the New Hot Analytics Stack - Apache Kafka, Spark and Druid by Danny Leybzon

Centralizing Data Science Work and Infrastructure Access Across the Enterprise by Ross Sharp

Mental Models for Building Your Career in Data Science by Chirasmita Mallick

A Data Scientist from Academia to Industry: things you should know! by Wjdan Alharthi

The Data Engineering Path by Daniela Petruzalek

How Data Scientists Can Support Their Organization's DEI Efforts by Timi Dayo-Kayode

Am I Ready for a Data Science Job? by Aadil Hussaini

Coding Challenges: What Are hiring Companies Looking For? by Arwen Griffioen, PhD

Data Science Success Stories by Jeff Anderson

ODSC Ignite: Women in Data Science

AI Investors Reverse Pitch

Learning from Failure - Incredible Stories from Successful Business Leaders