TRAINING & WORKSHOPS

VIEW TRACK SPOTLIGHT
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.

Machine Learning Deep Dive

20% Last Chance | Offer ends in:

Register and Save

Taught By World-Class Data Science Experts

Director | Co-DirectorBerkeley Robot Learning Lab | Berkeley Artificial Intelligence (BAIR) Lab

Pieter Abbeel, PhD

Professor Pieter Abbeel is Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab. Abbeel’s research strives to build ever more intelligent systems, which has his lab push the frontiers of deep reinforcement learning, deep unsupervised learning, especially as it pertains to robotics. Abbeel’s Intro to AI class has been taken by over 100K students through edX, and his Deep Unsupervised Learning materials are standard references for AI researchers. Abbeel has founded several companies, including Gradescope (AI to help instructors with grading homework, projects and exams) and Covariant (AI for robotic automation of warehouses and factories). He advises many AI and robotics start-ups, and is a frequently sought after speaker worldwide for C-suite sessions on AI future and strategy. Abbeel has received many awards and honors, including ACM Prize, IEEE Fellow, PECASE, NSF-CAREER, ONR-YIP, AFOSR-YIP, Darpa-YFA, TR35, and 10+ best paper awards/finalists. His work is frequently featured in the press, including the New York Times, Wall Street Journal, BBC, Rolling Stone, Wired, and Tech Review.

Foundations of Deep Reinforcement Learning(Tutorial)

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.

 

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)

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)

Principal Machine Learning EngineerRobust Intelligence

Dr. Blaine Nelson

Dr. Blaine Nelson earned his B.S. (University of South Carolina), M.S. and Ph.D (UC Berkeley) degrees in Computer Science. He was a Humboldt Postdoctoral Research Fellow at the University of Tübingen (2011-13) and a Postdoctoral Researcher at the University of Potsdam (2013-14) in Germany. As a graduate student and post-doc, Dr. Nelson co-established the foundations of adversarial machine learning. He has twice co-chaired the ACM CCS workshop on Artificial Intelligence & Security, and co-coordinated the Dagstuhl Perspectives Workshop on Machine Learning Methods for Computer Security (2012).

Following his post-doctoral work, Dr. Nelson worked as a software engineer in Google’s fraud detection group (2014-2016) where he built models and designed infrastructure for large scale machine learning. He then became a senior software engineer at Google’s counter-abuse technology team (2016-2021) where he designed and built a large scale machine learning workflow system. Currently, Dr. Nelson is a principal machine learning engineer at Robust Intelligence where he works in a multi-faceted role to build infrastructure for testing the reliability and security of machine learned models by finding potential flaws or vulnerabilities in their behavior.

Practical Adversarial Learning: How to Evaluate, Test, and Build Better Models(Training)

CTOIhr MPE B+C GmbH

Frank Zickert, PhD

Frank Zickert is Quantum machine learning engineer and the author of Hands-On Quantum Machine Learning With Python. He teaches quantum machine learning in an accessible way to help those without a degree in math or physics to get started in the field.

In his research, Frank strives to use quantum machine learning to advance the field of knowledge graph-based natural language processing. He is also the Chief Technology Officer of Ihr MPE B+C where he supports medical physicists to provide radiation protection services for clinical customers. Previously he worked at Aperto-An IBM Company and Deutsche Bank.

Frank earned his Ph.D. in Information Systems Development from Goethe University Frankfurt am Main, Germany.

Getting Started With Quantum Bayesian Networks in Python and Qiskit(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)

 

Staff Field Data ScientistDomino Data Labs

Jennifer Dawn Davis, PhD

Jennifer Davis, Ph.D. is a Staff Field Data Scientist at Domino Data Labs, where she empowers clients on complex data science projects. She has completed two postdocs in computational and systems biology, trained at a supercomputing center at the University of Texas, Austin, and worked on hundreds of consulting projects with companies ranging from start-ups to the Fortune 100. Jennifer has previously presented topics at conferences for Association for Computing Machinery on LSTMs and Natural Language Generation and at conferences across the US and in Italy. Jennifer was part of a panel discussion for an IEEE conference on artificial intelligence in biology and medicine. She has practical experience teaching both corporate classes and at the college level. Jennifer enjoys working with clients and helping them achieve their goals.

Large Scale Deep Learning using the High-Performance Computing Library OpenMPI and DeepSpeed(Workshop)

Founder, Director | AuthorBerkeley Coding Academy

Corey Wade

Corey Wade, MS Mathematics, MFA Writing & Consciousness, is the director and founder of Berkeley Coding Academy, an online program with live classes where teenagers learn Python Programming, Data Analytics, and Machine Learning. Author of Hands-on Gradient Boosting with XGBoost and scikit-learn, and lead author of The Python Workshop, Corey also teaches Math, Programming, and Data Science at Berkeley Independent Study. Corey has published iPhone apps with students, designed classes to build websites, and run after-school coding programs to support girls and underserved students. A Springboard Data Science graduate and multiple grant award-winner, Corey has also worked in industry developing Data Science curricula for Pathstream and Hello World while contributing articles for Towards Data Science. When not coding or teaching, Corey reads poetry and studies the stars.

Introduction to scikit-learn: Machine Learning in Python(Training)

Climate & Agronomic Data ScientistSyngenta

Serg Masis

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he’s a Climate and Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events efficiently. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making. He wrote the bestselling book “Interpretable Machine Learning with Python” and is currently working on a new book titled “DIY AI” for Addison-Wesley for a broader audience of curious developers, makers, and hackers.

Enhance Trust with Machine Learning Model Error Analysis(Workshop)

 

CEO, Security & Forensics Analyst, Incident Responder | Senior InstructorOne eSecurity | SANS Institute

Jess Garcia

Jess Garcia is the Founder of the global Cybersecurity/DFIR firm One eSecurity and a Senior Instructor with the SANS Institute.

During his 25 years in the field, Jess has led a myriad of complex multinational investigations for Fortune 500 companies and global organizations. As a SANS Instructor, Jess stands as one of the most prolific and veteran ones, having taught 10+ different highly technical Cybersecurity/DFIR courses in hundreds of conferences world-wide over the last 19 years.

Jess is also an active Cybersecurity/DFIR Researcher. With the mission of bringing Data Science/AI to the DFIR field, Jess launched in 2020 the DS4N6 initiative (www.ds4n6.io), under which he is leading the development of multiple open source tools, standards and analysis platforms for DS/AI+DFIR interoperability.

DS/AI for Incident Response & Threat Hunting with CHRYSALIS & DAISY(Talk)

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.

Lead Data ScientistHydrostasis, Inc

Michelle Hoogenhout

Michelle Hoogenhout is the lead data scientist at Hydrostasis, Inc. Hydrostasis is pioneering hydration monitoring by collecting optical changes in blood flow and water content from wrist-worn sensors. Michelle holds a PhD in Psychology (Neuropsychology) from the University of Cape Town and a neuropsychiatric genetics training fellowship from the Harvard T.H. Chan School of Public Health. She has over 10 years of experience in machine learning and insight generation from physiological and psychological data. Her research interests include the intersection between physical states and emotional and cognitive performance, as well as developmental disorders and empathy. Michelle also loves teaching and instructional design: she’s taught data science, psychology, and statistics. In her free time Michelle loves hiking, board games and swimming.

Separating the Signal from the Noise: Signal Processing and Feature Extraction Techniques for Biological Data(Workshop)

Senior Clinical ProgrammerBioMarin

Martin Frigaard

Martin is a Senior Clinical Programmer at BioMarin, where he builds dashboards and tools for making data-informed decisions. Previously, Martin built statistical tools and dashboards for the Diabetes Technology Society, a contributing author for Data Journalism in R on the Northeastern University School of Journalism blog/website, and other volunteer and non-profit organizations. He’s a data journalism instructor for California State University, Chico. Martin holds a graduate degree in Clinical Research and is passionate about data literacy and open source technologies.

Data Visualization with ggplot2(Workshop)

Head of Artificial IntelligenceOPEN KNOWLEDGE GmbH

Oliver Zeigermann

Oliver is a software developer from Hamburg Germany and has been a practitioner for more than 3 decades. He specializes in frontend development and machine learning. He is the author of many video courses and textbooks.

Image Recognition with OpenCV and TensorFlow(Training)

 

Head of Data Science Evangelism and MarketingOuterbounds

Hugo Bowne-Anderson, PhD

Hugo Bowne-Anderson is a data scientist, writer, educator & podcaster. His interests include promoting data & AI literacy/fluency, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. He does many of these at DataCamp, a data science training company educating over 3 million learners worldwide through interactive courses on the use of Python, R, SQL, Git, Bash and Spreadsheets in a data science context. He has spearheaded the development of over 25 courses in DataCamp’s Python curriculum, impacting over 170,000 learners worldwide through my own courses. He hosts and produce the data science podcast DataFramed, in which he uses long-format interviews with working data scientists to delve into what actually happens in the space and what impact it can and does have. He earned PhD in Mathematics from the University of New South Wales, Australia and has conducted biomedical research at the Max Planck Institute in Germany and Yale University, New Haven.

Full-stack Machine Learning for Data Scientists(Tutorial)

 

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.

Artificial Intelligence and Data Analytics Expert, Educator, Author, Expert in Remote Team ManagementNeuromatch Academy

Amita Kapoor, PhD

Amita Kapoor, is the author of best-selling books in the field of Artificial Intelligence and Deep Learning. She mentors students at different online platforms such as Udacity and Coursera and is a research and tech advisor to organizations like DeepSight AI Labs and MarkTechPost. She started her academic career in the Department of Electronics, SRCASW, the University of Delhi, where she was an Associate Professor. She has over 20 years of experience in actively researching and teaching neural networks and artificial intelligence at the university level. A DAAD fellow, she has won many accolades with the most recent being Intel AI Spotlight award 2019, Europe. An active researcher, she has more than 50 publications in international journals and conferences. Extremely passionate about using AI for the betterment of society and humanity in general, she is ready to embark on her second innings as a digital nomad.

Deep Learning with Python and Keras (Tensorflow 2)(Training)

Marketing Science LeadNextdoor

Peter Spangler

Peter is a hands-on data science leader with a business focused approach to building data science solutions and telling stories with data. Experienced in translating business problems into data products using advanced statistical techniques and ML to support decision making in a variety of rapid growth environments. Scaled data science solutions for user acquisition, retention, channel optimization, revenue and fraud at Lyft, Alibaba and Citrix. Currently leading Marketing Science for Growth at Nextdoor.

Data Visualization with ggplot2(Workshop)

Data Scientist Meta

Clinton Brownley, PhD

Clinton Brownley, Ph.D., is a data scientist at Meta (formerly Facebook), where he’s responsible for a variety of analytics projects designed to empower employees to do their best work. Prior to this role, he was a data scientist at WhatsApp, working to improve messaging and VoIP calling performance and reliability. Before WhatsApp, he worked on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions at Facebook.

As an avid student and teacher of modern data analysis and visualization techniques, Clinton teaches a graduate course in interactive data visualization for UC Berkeley’s MIDS program, taught a short-term graduate course in regression analysis and machine learning workshop for NYU’s A3SR program, leads an annual machine learning in Python workshop, and is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis”.

Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Machine Learning with Python: A Hands-On Introduction(Training)

Senior AI/ML Development EngineerIntel Corporation

Tamoghna Ghosh

Tamoghna is a AI Solution Architect in Client Computing Group at Intel, working on building next generation AI solutions for edge computing. Prior to this role he has worked as a data scientist at Intel working on various domains like supply chain – inventory optimization, anomaly detection and failure prediction of various IT infrastructure across Intel, building advanced search tools for bug sightings, to name a few. After his Masters in Computer Science from Indian Statistical Institute and a Masters in Mathematics form Calcutta University, he has worked as a research assistant in Microsoft Research India for 3+ years and then moved to other product companies to start his journey in the ML and AI space. He has been teaching AI courses at Intel and trained 250+ employees. Also, he was a core member of the internal AI training academy and AI content development which is a 3-level course in AI for Intel employees. He mentors many folks for their AI projects. He has 4 US patents filed on various innovative AI applications and products and also published few papers related to the work at Intel. He published a book on hands-on transfer learning with Python in 2018 from Packt (packtpub.com) and is working on another book to be published this year from bpb publications.

A Hands-on Introduction to Transfer Learning(Workshop)

Staff Research Scientist Google Brain

Balaji Lakshminarayanan, PhD

Balaji is currently a Staff Research Scientist at Google Brain working on Machine Learning and its applications. Previously, he was a research scientist at DeepMind for 4.5+ years. Before that, he received a PhD in machine learning from Gatsby Unit, UCL supervised by Yee Whye Teh. His research interests are in scalable, probabilistic machine learning. More recently, he has focused on: – Uncertainty and out-of-distribution robustness in deep learning – Deep generative models including generative adversarial networks (GANs), normalizing flows and variational auto-encoders (VAEs) – Applying probabilistic deep learning ideas to solve challenging real-world problems.

Practical Tutorial on Uncertainty and Out-of-distribution Robustness in Deep Learning(Tutorial)

Chief Data & A.I. OfficerFanatics Collectibles | Statistics and Machine Learning Instructor

Jeffrey Yau, PhD

Jeffrey is currently the Global Head of Data Science and Analytics at Amazon Music. Prior to Amazon, Jeffrey worked at WalmartLabs as the VP of Data Science, Data Engineering, and Platform Engineering. Before joining WalmartLabs, he pretty much spent my entire career in quantitative finance. His last role in the investment management industry was the Chief Data Scientist and Global Head of Data Science at AllianceBernstein (AB), a global investment management firm that managed almost $800B. Before AB, he was the VP and Head of Data Science at Silicon Valley Data Science, a startup acquired by Apple in 2017. Earlier in his career, he held various quantitative leadership positions, including the Corporate VP and Head of Risk Analytics and Quantitative Research at Charles Schwab Corporation, Director of Financial Risk Consulting at KPMG, and Assistant Director at Moody’s Analytics. Jeffrey enjoys academic research and teaching. He has taught finance, economics, machine learning, and statistics at University of Pennsylvania, Virginia Tech, Cornell, NYU, and UC Berkeley. He is a frequent speaker at national and international A.I., data science, and technology conferences, such as Spark&AI Summit, Strata, ODSC, PyCon, and many others. He holds a Ph.D. and an M.A. in Economics from the University of Pennsylvania and a B.S. in Mathematics and Economics from UCLA.

Data Science InstructorMetis

Julia Lintern

Julia Lintern currently works as an instructor for the Metis Data Science Flex Program. Previously, she worked as a Data Scientist for the New York Times. Julia began her career as a structures engineer designing repairs for damaged aircraft. Julia holds an MA in applied math from Hunter College, where she focused on visualizations of various numerical methods and discovered a deep appreciation for the combination of mathematics and visualizations. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.

Introduction to Machine Learning(Bootcamp)

ODSC Speaker - Utkarsh Contractor
Vice President of Artificial IntelligenceAisera Inc.

Utkarsh Contractor

Utkarsh Contractor is the VP of AI and Machine Learning at Aisera, where he leads the data science team working on machine learning and artificial intelligence applications in the fields of Natural Language Processing and Computer Vision. As a graduate student at Stanford University, his research focussed on experiments in computer vision, using Deep Neural Networks to analyze surveillance scene imagery and footages. Utkarsh has a decade of industry experience in Computer Vision, NLP and other Machine Learning domains working at companies such as Aisera, LinkedIn and AT&T Labs.

Deep Multimodal Learning for Surveillance(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)

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)

Senior Data ScientistContinental AG

David Koll

David Koll is a Senior Data Scientist at Continental Tires, Germany. He holds a PhD in Computer Science from the University of Göttingen with research visits to the University of Oregon (USA), Uppsala University (Sweden), and Fudan University (China). Most of his academic work was involving analyses of social media. Since joining Continental in 2018 he has developed different analytical solutions that are now running in production, with a focus on both forecasting and Industry 4.0.

Any Way You Want It: Integrating Complex Business Requirements into ML Forecasting Systems(Workshop)


More instructors added weekly

More Instructors Coming Soon

Track Spotlight

Your consent is required to display this content from youtube - Privacy Settings

The Topic:

Practical Adversarial Learning: How to Evaluate, Test, and Build Better Models

In this training, Dr. Blaine Nelson will introduce you to the techniques that have been developed for constructing adversarial examples for a model, tools that can find potential vulnerabilities in them, and training procedures that can produce better models.

The Instructor:

Dr. Blaine Nelson earned his B.S. (University of South Carolina), M.S. and Ph.D (UC Berkeley) degrees in Computer Science. He was a Humboldt Postdoctoral Research Fellow at the University of Tübingen (2011-13) and a Postdoctoral Researcher at the University of Potsdam (2013-14) in Germany. As a graduate student and post-doc, Dr. Nelson co-established the foundations of adversarial machine learning. He has twice co-chaired the ACM CCS workshop on Artificial Intelligence & Security, and co-coordinated the Dagstuhl Perspectives Workshop on Machine Learning Methods for Computer Security (2012).

Check out the blog here

See 2022 Training Sessions

Previuos Workshops/Training Sessions

Pieter Abbeel, PhD | Director | Co-Director | Berkeley Robot Learning Lab | Berkeley Artificial Intelligence (BAIR) Lab

Foundations of Deep Reinforcement Learning

Dr. Blaine Nelson | Principal Machine Learning Engineer | Robust Intelligence

Practical Adversarial Learning: How to Evaluate, Test, and Build Better Models

Leonardo De Marchi | VP of Labs | Thomson Reuters | Laura Skylaki, PhD | Manager of Applied Research | Thomson Reuters Labs

NLP Fundamentals

Daniel Lenton, PhD | CEO | Ivy

Unifying ML With One Line of Code

Stefanie Molin | Data Scientist, Software Engineer | Bloomberg

Beyond the Basics: Data Visualization in Python

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

Self-Supervised and Unsupervised Learning for Conversational AI and NLP

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

Anomaly Detection with Python and R

Frank Zickert, PhD | CTO | Ihr MPE B+C GmbH

Getting Started With Quantum Bayesian Networks in Python and Qiskit

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

Introduction to Large-scale Analytics with PySpark

James Demmel, PhD | Professor of Mathematics and Computer Science | UC Berkeley | Yang You, PhD | Presidential Young Professor | National University of Singapore

Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

Julien Simon | Chief Evangelist | Hugging Face

Hyper-productive NLP with Hugging Face Transformers

Corey Wade | Founder, Director | Author | Berkeley Coding Academy

Introduction to scikit-learn: Machine Learning in Python

Oliver Zeigermann | Head of Artificial Intelligence | OPEN KNOWLEDGE GmbH

Image Recognition with OpenCV and TensorFlow

Clinton Brownley, PhD | Data Scientist | Meta

Machine Learning with Python: A Hands-On Introduction

Serg Masis | Climate & Agronomic Data Scientist | Syngenta

Enhance Trust with Machine Learning Model Error Analysis

Amita Kapoor, PhD | Artificial Intelligence and Data Analytics Expert | Neuromatch Academy

Deep Learning with Python and Keras (Tensorflow 2)

Guglielmo Iozzia | Associate Director – Business Tech Analysis, ML/AI | MSD

Not Just Deep Fakes: Applications of Visual Generative Models in Pharma Manufacturing

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

Machine Learning with XGBoost

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

Training & Workshop Sessions

– Taught by World-Class Data Scientists –

Learn the latest data science concepts, tools, and techniques from the best. Forge a connection with these rock stars from industry and academia, who are passionate about molding the next generation of data scientists.
Register now | 20% ends soon

Pre-Conference LIVE Trainings

Join ONE of our Ai+ Live Training Sessions for free when you purchase an ODSC West In-Person, or Virtual Training Pass ( VIP, Platinum Gold, or Bootcamp).

LIVE TRAINING: JULY 26TH

Data wrangling is an essential foundational topic for anyone considering projects or a role in data engineering, data science, or machine learning. This session will help one understand core data-wrangling concepts including what is data, data generation and collecting, data profiling, data transformation, data manipulation, and other essential workflow topics.  We will layer on SQL and an introduction to relational databases. With this foundational understanding, you will not only have job-ready data skills but you will be better position to proceed to other introductory-level courses in data analysis, programming, data science, and machine learning.

LIVE TRAINING: AUGUST 24ST

 Learn the basics of building a PyTorch model using a structured, incremental, and from first principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and how to make use of its capabilities: autograd, dynamic computation graph, model classes, data loaders and more. The main goal of this training is to show you how PyTorch works: we will start with a simple and familiar example in Numpy and “torch” it! At the end of it, you should be able to understand PyTorch’s key components and how to assemble them together into a working model.

LIVE TRAINING CONTINUES:

 

We will be hosting additional live training session during the months of September, October, and November

Ai+ is the only hands-on training platform solely developed for AI practitioners. Keep training with the top names in the industry. Free for 6 months with an ODSC West Platinum pass.

Learn More

Need More Reason To Sign Up?

ODSC Training Includes

Opportunities to form working relationships with some of the world’s top data scientists.

Access to 40+ training sessions and 70+ workshops.

Hands-on experience with the latest frameworks and breakthroughs in data science.

Affordable training–equivalent training at other conferences costs much more.

Professionally prepared learning materials, custom- tailored to each course.

Opportunities to connect with other ambitious, like-minded data scientists.

Highly Experienced Instructors

Our instructors are highly regarded in in data science and come from both industry and academia.

Real World Applications

Get the data science skills and knowledge that will help you in your career or business.

Cutting Edge Subject Matter

Find training sessions offered on a wide variety of data science topics, from machine learning to data visualization.

I'm Ready to register for ODSC WEST 2023

Participate at ODSC West 2023

Register
More Info
Submit a Session
GET IN TOUCH

As part of the global data science community we value inclusivity, diversity,  and fairness in the pursuit of knowledge and learning. We seek to deliver a conference agenda, speaker program, and attendee participation that moves the global data science community forward with these shared goals. Learn more on our code of conduct, speaker submissions, or speaker committee pages.

ODSC Newsletter

Stay current with the latest news and updates in open source data science.

In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London.

And keep a lookout for special discount codes, only available to our newsletter subscribers!

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

Menu
Conferences
Extras
Copyright ODSC 2022. All Rights Reserved
Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from Youtube
Vimeo
Consent to display content from Vimeo
Google Maps
Consent to display content from Google
Save