Ai+ Training

R for Python Programmers by Dr. Colin Gillespie

Can Your Model Survive the Crisis: Monitoring, Diagnosis and Mitigation by Jiahang Zhong, PhD

Practical, Rigorous Explainability in AI by Tsvi Lev

PyTorch 101: Building A Model Step-by-Step by Daniel Voigt Godoy

Training a Machine to See What’s Beautiful (esp. for Hotel Photos) by Dat Tran

Variational Auto-Encoders for Customer Insight by Yaniv Ben-Ami, PhD

Deep Learning Building Blocks by Nathaniel Tucker

Deep Learning on Mobile by Anirudh Koul

Deep Learning for Anomaly Detection by Nisha Muktewar

Forecasting the Economy with Fifty Shades of Emotions by Sonja Tilly, CFA

Image Detection as a Service: How we Use APIs and Deep Learning to Support our Products by Laura Mitchell

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

Your Future, Today. Using NLP to Advance Your Career by Gabrielle Fournet, PhD

Machine Learning Operations: Latent Conditions and Active Failures by Flavio Clesio

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

Modern and Old Reinforcement Learning Part 1 by Leonardo De Marchi

Active Learning with a Sprinkle of PyTorch by Olga Petrova, PhD

A Deep Dive into Convolutional Neural Networks by Susana Zoghbi, PhD

Predicting Future Decisions with Deep learning for Financial Trading by Ning Wang, PhD and Yuting Fu

AI Assisting in Traffic Relief by Piotr Kaczyński and Wioletta Stobieniecka

Modern and Old Reinforcement Learning Part 2 by Leonardo De Marchi

Explainable ML: Application of Different Approaches by Violeta Misheva, PhD

Provenance: a Fundamental Data Governance Tool ⎯ a Case Study for Data Science Pipelines and Their Explanations by Luc Moreau, PhD

Rule Induction and Reasoning in Knowledge Graphs by Daria Stepanova, PhD

Practical Methods to Optimise Model Stability: A Case Study Using Customer-Lifetime Value at Farfetch by Davide Sarra and Kishan Manani, PhD

Sprinting Pandas by Ian Ozsvald

Knowledge Graphs for the Greater Good by Bojan Božić, PhD

Missing Data in Supervised Machine Learning by Andras Zsom, PhD

Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples by Dr. Kirk Borne

Atypical Applications of Typical Machine Learning Algorithms by Dr. Kirk Borne

Machine Learning in R Part I: Featuring Penalized Regression and Boosted Trees by Jared Lander

Good, Fast, Cheap: How to do Data Science with Missing Data by Matt Brems

Introduction To Face Processing With Computer Vision by Gabriel Bianconi

Interpreting and Explaining XGBoost Models by Brian Lucena, PhD

Making Happy Modelers: Build and Maintain Your Data Warehouse with AWS Redshift and Airflow by Stephanie Kirmer

Tracking Coal and Solar Power with Machine Learning and Satellites by Laurence Watson

Ensuring Ethical Practice in AI by Sray Agarwal

Sustainable Retail Through Open Source, Scraping and NLP by Joanneke Meijer

Building Personalized Scores for Customers: How to Combine Different Data Types and Learn in the Process by Svetlana Vinogradova, PhD

Have I Got (Financial) News for You by Alun Biffin, PhD

Multivariate (Flight) Anomalies Detection by Marta Markiewicz

CRESST: Complete Rare Event Specification Using Stochastic Treatment by Debanjana Banerjee

Which is the Tallest Building in Europe? — Representing and Reasoning About Knowledge by Ian Horrocks, PhD

Automated Insights in Finance Using Machine Learning & AI by Dr. Arun Verma

Hands-on Machine Learning Engineer with scikit-learn by Olivier Grisel

Hands-on Reinforcement Learning with Ray RLlib by Dean Wampler, PhD

Federated Learning: AI for the Privacism Movement by Michael Huth, PhD

Removing Unfair Bias in Machine Learning by Margriet Groenendijk, PhD

Explain Machine Learning Models by Margriet Groenendijk, PhD

VerticaPy: Demystifying Machine Learning Complexity with Python at Scale by Badr Ouali

Responsible Data Science Using Bias-Dashboards by Ramon van den Akker, PhD and Daan Knoope and Joris Krijger

Data Annotation at Scale: Active and Semi-Supervised Learning in Python by Gokhan Ciflikli, PhD

Virtual Data Science Learnathon by Paolo Tamagnini

Introduction to Time Series Analysis with KNIME by Maarit Widmann and Corey Weisinger

Algorithmic Confounding in Recommendation Systems by Allison Chaney, PhD

From Longitudinal Patient Observational Data to Individualized Treatments Effects Using Causal Inference by Ioana Bica

Building Fair and Explainable AI Pipelines by Margriet Groenendijk, PhD

Knowledge Graph Extraction for the Enterprise by Dr. Paul Buitelaar and Dr. John McCrae

Machine Learning in R Part II: Featuring Penalized Regression and Boosted Trees

The Evolution of Data Labeling by Soo Yang

Algorithms with Predictions by Michael Mitzenmacher, PhD

On the Automation of Data Science by Luc De Raedt, PhD

Building a Better Data Visualization Culture by Alan Rutter

From Numbers to Narrative: Turning Raw Data into Compelling Visual Stories with Impact by Bill Shander

Animating Data: From matplotlib plots to GIFs by Max Humber

Dare to Start Simple by Dr. Katharina Glass

What Do I See in This Data? Visual Tools to Enhance Data Understanding by Max Novelli

Mapping the Waters of The United States by Alfredo Kalaitzis, PhD

Industrial Artificial Intelligence – From automated Process to Cognitive Analytics by Diego Galar, PhD

Democratizing Data for the Enterprise by Sherard Griffin

Integrating Small Data, Synthetic Data in AI and Data Strategy for Fashion Retail by Andrey Golub, PhD

Building an Industry Classifier With The Latest Scraping, NLP and Deployment Tools by Ido Shlomo

A Gentle Intro to Transformer Neural Networks by Jay Alammar

Spark NLP for Healthcare: Lessons Learned Building Real-World Healthcare AI Systems by Veysel Kocaman, PhD

State-of-the-art NLP Made Easy with AdaptNLP by Brian Sacash and Andrew Chang

Advanced NLP: From Essentials to Deep Transfer Learning by Dipanjan (DJ) Sarkar and Anuj Gupta

Transformer Knows More than Meets the Eye by Michał Chromiak, PhD

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

Programming with Data: Python and Pandas by Daniel Gerlanc

SQL for Data Science by Mona Khalil

Introduction to Linear Algebra for Data Science and Machine Learning With Python by Hadrien Jean, PhD

Bayesian Data Science: Probabilistic Programming by Hugo Bowne-Anderson, PhD

Pomegranate: Fast and Flexible Probabilistic Modeling in Python by Jacob Schreiber

How to Build and Test a Trading Strategy Using ML by Stefan Jansen

Learning and Mining Large-Scale Spatiotemporal Data by Rose Yu, PhD

Data Excellence: Better Data for Better AI by Dr. Lora Aroyo

Data Science Change Is Inevitable, Growth Is Optional by Dr. Iain Brown

Machine Learning for Exoplanet Discovery by Dr. David Armstrong

First Aid Kit for Data Science: Keeping Machine Learning Alive by Véronique Van Vlasselaer, PhD

eXplainable Predictive c: Combine ML and Decision Management to Promote Trust on Automated Decision Making by Matteo Mortari and Daniele Zonca

Sports Analytics - Leveraging Open Source Technology to Improve Athlete Performance by Christopher Connelly

Is Infrastructure Holding Back Adoption of AI at Scale? by Nick Patience

Learn How to Seamlessly Use Julia for Your Machine Learning Tasks by Dr. Matt Bauman

Build and Deploy Custom AI Predictive Models by Yamini Rao

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

An Overview of Algorithmia: the Industry Leading Machine Learning Operations and Management Platform by Kristopher Overholt

Best Practices: Partnerships between ML/AI and Data Labeling Companies by Soo Yang

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

Revision Control for Structured Data by Gavin Mendel-Gleason

Creating Efficiency and Trust with MLOps by Jan van der Vegt

Build Your Own Cloud Native Covid-19 Data Analytics with Kubernetes and OpenShift by Dr. Mo Haghighi

VerticaPy Demo : Building a Prediction Churn Model Using Random Forest & Logistic Regression by Badr Ouali

Annotating Data with AI-assisted Labelling by Eric Landau

Demystifying data science roles and responsibilities by Eva-Marie Muller-Stuler, PhD

The Data Engineering Path by Daniela Petruzalek

Who is a Data Scientist? by Behrooz Afghahi

Changing career paths: be a Data Scientist! by Bea Hernández

Navigating Data Science Interviews by Shrilata Murthy

Women in Data Science Ignite

Learning from Failure