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Learn the latest data science concepts, tools, and techniques from the best. Forge a connection with these rockstars from industry and academia, who are passionate about molding the next generation of data scientists.

Taught By World-Class Data Science Experts

Chief Data ScientistNebula.io | Author of Deep Learning Illustrated

Dr. Jon Krohn

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.

Deep Learning with PyTorch and TensorFlow(Training)

Python & Data Science Corporate Trainer | ConsultantMetaSnake

Matt Harrison

Matt Harrison has been using Python since 2000. He runs MetaSnake, a Python and Data Science consultancy and corporate training shop. In the past, he has worked across the domains of search, build management and testing, business intelligence, and storage.

He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON as well as local user conferences.

Machine Learning with XGBoost(Workshop)

Idiomatic Pandas(Workshop)

Data Scientist | Bloomberg | Author of Hands-On Data Analysis with Pandas

Stefanie Molin

Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

Beyond the Basics: Data Visualization in Python(Training)

Senior Software EngineerQuansight Labs | Core Developer and Technical Committee member for scikit-learn

Thomas J. Fan

Thomas J. Fan is a Senior Software Engineer at Quansight Labs, working to sustain and evolve the PyData open-source ecosystem. He is a maintainer for scikit-learn, an open-source machine learning library written for Python. Previously, he worked at Columbia University, improving the interoperability between scikit-learn and AutoML systems. Thomas holds a Masters in Physics from Stony Brook University and a Masters in Mathematics from New York University.

Full ProfessorUniversité de Montréal | Author of Sparse Modeling: Theory, Algorithms, and Applications

Irina Rish, PhD

Irina Rish is a Full Professor in the Computer Science and Operations Research Department at the Université de Montréal (UdeM) and a core faculty member of MILA – Quebec AI Institute. She holds Canada Excellence Research Chair (CERC) in Autonomous AI and a Canadian Institute for Advanced Research (CIFAR) Canada AI Chair. She received her MSc and PhD in AI from University of California, Irvine and MSc in Applied Mathematics from Moscow Gubkin Institute. Dr. Rish’s research focus is on machine learning, neural data analysis and neuroscience-inspired AI. Before joining UdeM and MILA in 2019, Irina was a research scientist at the IBM T.J. Watson Research Center, where she worked on various projects at the intersection of neuroscience and AI, and led the Neuro-AI challenge. She received multiple IBM awards, including IBM Eminence & Excellence Award and IBM Outstanding Innovation Award in 2018, IBM Outstanding Technical Achievement Award in 2017, and IBM Research Accomplishment Award in 2009. Dr. Rish holds 64 patents, has published over 80 research papers in peer-reviewed conferences and journals, several book chapters, three edited books, and a monograph on Sparse Modeling.

VP of LabsThomson Reuters | Author of Hands-On Neural Networks)

Leonardo De Marchi

Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks. He now works in Thomson Reuters as VP of Labs, and also provides consultancy and training for small and large companies. His previous experience includes being Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through acquisition and an IPO.

Brian Lucena
PrincipalNumeristical | Creator of StructureBoost, ML-Insights, and SplineCalib

Brian Lucena, PhD

Brian Lucena is Principal at Numeristical, where he advises companies of all sizes on how to apply modern machine learning techniques to solve real-world problems with data. He is the creator of three Python packages: StructureBoost, ML-Insights, and SplineCalib. In previous roles he has served as Principal Data Scientist at Clover Health, Senior VP of Analytics at PCCI, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.

Advanced Gradient Boosting (I): Fundamentals, Interpretability, and Categorical Structure(Training)

Advanced Gradient Boosting (II): Calibration, Probabilistic Regression and Conformal Prediction(Training)

Research EngineerIBM TJ Watson

Tejaswini Pedapati

Tejaswini Pedapati works at IBM Research. Her research is focused on interpretability and automating deep learning. To that end, she was involved in developing tools and algorithms to provide these capabilities for IBM products. She has a masters’ degree from Columbia University.

Introduction to AutoML: Hyperparameter Optimization and Neural Architecture Search(Tutorial)

Associate Professor of AnalyticsInstitute for Advanced Analytics at NC State University

Aric LaBarr, PhD

A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern workforce to wisely communicate and handle a data-driven future at the nation’s first Master of Science in Analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.

Advanced Fraud Modeling & Anomaly Detection with Python & R(Training)

Assistant ProfessorMIT | NLP and Deep Learning Expert

Jacob Andreas, PhD

Jacob Andreas is the X Consortium Assistant Professor at MIT. His research aims to build intelligent systems that can communicate effectively using language and learn from human guidance. Jacob earned his Ph.D. from UC Berkeley, his M.Phil. from Cambridge (where he studied as a Churchill scholar) and his B.S. from Columbia. As a researcher at Microsoft Semantic Machines, he founded the language generation team and helped develop core pieces of the technology that powers conversational interaction in Microsoft Outlook. He has been the recipient of Samsung’s AI Researcher of the Year award, MIT’s Kolokotrones teaching award, and paper awards at NAACL and ICML.

Interpreting Features in Deep Networks(Tutorial)

CEO and CreatorIvy

Daniel Lenton, PhD

Daniel Lenton is the creator of Ivy, which is an open-source framework with an ambitious mission to unify all other ML frameworks. Prior to starting Ivy, Daniel was a PhD student at Imperial College London, where he published research in the areas of machine learning, robotics and computer vision.

Unifying ML With One Line of Code(Tutorial)

 

Apache Spark Expert, Data Science Instructor and Consultant

Adam Breindel

Adam Breindel consults and teaches widely on Apache Spark and other technologies. Adam’s experience includes work with banks on neural-net fraud detection, streaming analytics, cluster management code, and web apps, as well as development at a variety of startup and established companies in the travel, productivity, and entertainment industries. He is excited by the way that Spark and other modern big-data tech remove so many old obstacles to system design and make it possible to explore new categories of interesting, fun, hard problems.

Co-Founder | Co-author, Advanced Analytics with PySparkLooppanel | O'Reilly Media

Akash Tandon

Akash Tandon is co-founder and CTO of Looppanel where he builds software to help product teams record, store and analyze user research data. He is a co-author of Advanced Analytics with PySpark, published by O’Reilly. Previously, Akash worked as a senior data engineer at Atlan, SocialCops and RedCarpet where he built data infrastructure for enterprise, government and finance use-cases. He has also been a participant and mentor in the Google Summer of Code program with the R Project for Statistical Computing.

Introduction to Large-scale Analytics with PySpark(Workshop)

Head of Data Science for EMEADomino Data Lab | Machine Learning Instructor

Nikolay Manchev, PhD

Nikolay is an experienced Data Science professional who currently leads the EMEA Data Science team at Domino Data Lab. He holds an MSc in Software Technologies, an MSc in Data Science, and is currently undertaking postgraduate research at King’s College London. His area of expertise is Statistics, Mathematics, and Data Science in general, and his research interests are in Neural Networks with emphasis on biological plausibility. He writes articles and blogs regularly and speaks at various European conferences (ODSC, Big Data Spain, Strata, Big Data London etc.) to build awareness about data science and artificial intelligence. He is also the organizer of the London Data Science and Machine Learning meetup and recipient of several technical mastery awards like the Oracle ACE Award and the IBM Outstanding Technical Achievement Award.

Getting Started with Hyperparameter Optimisation(Training)

Inventor and Creator of PyCaret | Product Director - Artificial Intelligenceantuit.ai

Moez Ali

Innovator, Technologist, and a Data Scientist turned Product Manager with proven track record of building and scaling data products, platforms, and communities. Experienced in building and leading teams of data scientists, data engineers, and product managers. Strongly opinionated tech visionary and a thought partner to C-level leadership.

Moez Ali is an inventor and creator of PyCaret. PyCaret is an open-source, low-code, machine learning software. Ranked in top 1%, 8M+ downloads, 7K+ GitHub stars, 100+ contributors, and 1000+ citations.

Globally recognized personality for open-source work on PyCaret. Keynote speaker and top ten most-read writer in the field of artificial intelligence. Teaching AI and ML courses at Cornell, NY and Queens University, CA. Currently building world’s first hyper-focused Data and ML Platform.

Senior Data ScientistInstitute for Experiential AI at Northeastern University | NLP Expert and Instructor

Benjamin Batorsky, PhD

Ben is a Senior Data Scientist at the Institute for Experiential AI at Northeastern University. He obtained his Masters in Public Health (MPH) from Johns Hopkins and his PhD in Policy Analysis from the Pardee RAND Graduate School. Since 2014, he has been working in data science for government, academia and the private sector. His major focus has been on Natural Language Processing (NLP) technology and applications. Throughout his career, he has pursued opportunities to contribute to the larger data science community. He has presented his work at conferences, published articles, taught courses in data science and NLP, and is co-organizer of the Boston chapter of PyData. He also contributes to volunteer projects applying data science tools for public good.

Senior Software EngineerAlteryx Innovation Labs

Freddy Boulton

Freddy Boulton started his career as a data scientist for Nielsen where he built predictive models of television viewing behavior to make television ratings more accurate. This gave him a first hand-view of one of the biggest challenges faced by industry data scientists – being able to easily communicate and share machine learning models with stakeholders. He is currently solving that problem by working on Gradio, an open-source python library that lets data scientists create fully interactive demos of machine learning models with just a few lines of code.

A Practical Tutorial on Building Machine Learning Demos with Gradio(Workshop)

Head of OntologyTextkernel BV | Author of Semantic Modeling for Data

Panos Alexopoulos, PhD

Panos Alexopoulos has been working since 2006 at the intersection of data, semantics, and software, building intelligent systems that deliver value to business and society. Born and raised in Athens, Greece, he currently works as Head of Ontology at Textkernel, in Amsterdam, Netherlands, where he leads a team of Data Professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain. Panos holds a PhD in Knowledge Engineering and Management from National Technical University of Athens, and has published more than 60 papers at international conferences, journals and books. He is the author of the book “Semantic Modeling for Data – Avoiding Pitfalls and Breaking Dilemmas” (O’Reilly, 2020), and a regular speaker and trainer in both academic and industry venues.

Mastering Adversarial Evaluation for NLP: A Practical Workshop(Workshop)

Chief EvangelistHugging Face

Julien Simon

Julien is currently Chief Evangelist at Hugging Face. He’s recently spent 6 years at Amazon Web Services where he was the Global Technical Evangelist for AI & Machine Learning. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in large-scale startups.

 

Chief Scientist and Head of AIGot It AI

Chandra Khatri

Chandra Khatri is the Chief Scientist and Head of AI at Got It AI, wherein, his team is transforming AI space by leveraging state-of-the-art technologies to deliver the world’s first fully autonomous Conversational AI system. Under his leadership, Got It AI is democratizing Conversational AI and related ecosystems through automation. Prior to Got-It, Chandra was leading various AI applied and research groups at Uber, Amazon Alexa and eBay.

At Uber, he was leading Conversational AI, Multi-modal AI, and Recommendation Systems. At Amazon he was the founding member of the Alexa Prize Competition and Alexa AI, wherein he was leading the R&D and got the opportunity to significantly advance the field of Conversational AI, particularly Open-domain Dialog Systems, which is considered as the holy-grail of Conversational AI and is one of the open-ended problems in AI. And at eBay he was driving NLP, Deep Learning, and Recommendation Systems related applied research projects.

He graduated from Georgia Tech with a specialization in Deep Learning in 2015 and holds an undergraduate degree from BITS Pilani, India. His current areas of research include Artificial and General Intelligence, Democratization of AI, Reinforcement Learning, Language and Multi-modal Understanding, and Introducing Common Sense within Artificial Agents.


More instructors added weekly

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Confirmed Workshop/Training Sessions

(more sessions added weekly)

Aric LaBarr, PhD | Associate Professor of Analytics | Institute for Advanced Analytics at NC State University

Advanced Fraud Modeling & Anomaly Detection with Python & R

Dr. Jon Krohn | Chief Data Scientist | Nebula.io

Intro to Deep Learning with PyTorch and TensorFlow

Leonardo De Marchi | VP of Labs | Thomson Reuters

NLP Fundamentals

Chandra Khatri | Chief Scientist and Head of AI | Got It AI

Anomaly Detection with Python and R

Daniel Lenton, PhD | CEO | Ivy

Unifying ML With One Line of Code

Benjamin Batorsky, PhD | Senior Data Scientist | Institute for Experiential AI at Northeastern University

Bagging to BERT – A Tour of Applied NLP

Brian Lucena | Principal | Numeristical

Advanced Gradient Boosting: Probabilistic Regression and Categorical Structure

Julien Simon | Chief Evangelist | Hugging Face

Hyper-productive NLP with Hugging Face Transformers

Matt Harrison | Python & Data Science Corporate Trainer | Consultant | MetaSnake

Machine Learning with XGBoost

Akash Tandon | Co-Founder | Co-author, Advanced Analytics with PySpark | Looppanel | O'Reilly Media

Introduction to Large-scale Analytics with PySpark

Stefanie Molin | Data Scientist, Software Engineer | Bloomberg

Beyond the Basics: Data Visualization in Python

Panos Alexopoulos, PhD | Head of Ontology | Textkernel BV

Mastering Adversarial Evaluation for NLP: A Practical Workshop

Freddy Boulton | Senior Software Engineer | Alteryx Innovation Labs

A Practical Tutorial on Building Machine Learning Demos with Gradio

Nikolay Manchev, PhD | Head of Data Science for EMEA | Domino Data Lab

Getting Started with Hyperparameter Optimisation

Matt Harrison | Python & Data Science Corporate Trainer | Consultant | MetaSnake

Idiomatic Pandas

Free Pre-Bootcamp Primer Courses

Data, Coding, and AI preparation courses for ODSC Mini-Bootcamps

ODSC Bootcamp Primer Courses

These primer courses can be taken stand alone or as part of our Mini-Bootcamp series. This foundations series is built from the ground up to boost your understanding of data-centric AI

Hosted on Ai+ Training and included FREE as part of your ODSC Mini-Bootcamp Pass. Courses hosted in March and April 2023  – LIVE and On-Demand

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Beginner to Advanced Level Training

From the Leading Instructors in the Industry

Machine Learning

  • Meta-learning for Machine Learning

  • Self Supervised learning; new techniques

  • Federated Learning for Data Privacy

  • Explainable AI and Bias in machine learning

  • Machine Learning at Scale using Apache Spark

  • Safety & Robustness in Machine Learning Modeling

  • Semi-supervised learning

  • Causal Inference with Machine Learning

Deep Learning

  • Deep Reinforcement learning

  • Deep Learning with PyTorch & Tensorflow

  • Deep Learning Deep Dive

  • Computer Vision 1/2 Day Training

  • Deep Learning with Keras

  • Introduction to Deep learning

  • Deepfakes Tutorial

  • Graph Representation Learning

NLP

  • Self Supervised learning; new techniques

  • Transfer Learning in NLP

  • Introduction to NLP and Topic Modeling

  • NLP Pre-trained Transformer Models with Bert, Ernie,, and GPT-2

  • State-of-the-Art NLP with PyTorch and Tensorflow

  • Semi-supervised learning

  • Hugging Face Transformer Library Workshop

  • Applications of NLP; Sentiment Analysis, Dialog Systems, and Semantic Search

ADDITIONAL TUTORIALS & WORKSHOPS

  • Machine Learning for Cyber Security

  • Real-time Streaming Analytics

  • MLOps and Machine Learning Pipelines

  • Introduction to Machine Learning Using scikit-learn

  • Auto Machine Learning (AutoML)

  • Distributed Machine Learning

  • Introduction to Data Analysis with Python Pandas

  • Machine Learning Workflow with Kubeflow & Kubernetes

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