REGISTER YOUR INTEREST FOR ODSC WEST 2023

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OCTOBER 31st – NOVEMBER 3rd

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UNLOCK YOUR FULL POTENTIAL

Kick-start your career in data science in 3 immersive days. Get the skills you need, taught by world-renowned experts.
ODSC Mini-Bootcamp is the best way to gain in-demand machine learning skills in the shortest time with minimum investment. 
A truly immersive experience, you will access a range of industry-focused data science topics on a scale not offered elsewhere. Uniquely, you will also gain invaluable insights by networking and connecting with hundreds of data science attendees, world-renowned instructors, industry experts, and dozens of top companies seeking the next wave of talent.

WHY ODSC BOOTCAMP

UNLOCK

YOUR FULL POTENTIAL

Kick-start your career in data science in 3 immersive days. Get the skills you need, taught by world-renowned experts.

ODSC Mini-Bootcamp is the best way to gain in-demand machine learning skills in the shortest time with minimum investment. 

A truly immersive experience, you will access a range of industry-focused data science topics on a scale not offered elsewhere. Uniquely, you will also gain invaluable insights by networking and connecting with hundreds of data science attendees, world-renowned instructors, industry experts, and dozens of top companies seeking the next wave of talent.

WHY ODSC BOOTCAMPS?  
BUILD YOUR SKILLS AND BUILD YOUR NETWORK

Get Prepped

With Pre-Conference training, you can start learning the fundamentals. Learn programming, SQL, and basic math skills.

4-day Immersive

From October 31st to November 3rd, join our 4-day live immersive Mini-Bootcamp. Get hands-on experience in machine learning and deep learning.

Continue Learning

After the conference, you will be perfectly positioned to continue learning. With Ai+ on-demand training and discounted live training, you can learn at your own pace. 

Get Help 

Each attendee gets a certificate of completion and access to office hours. Jobseekers will be featured in our Ai+ Career job portal featuring 40+ hiring companies

WHAT YOU WILL LEARN

What You’ll Learn
ACCELERATE FROM ZERO TO MACHINE LEARNING

PROGRAMMING

Python,  R, Jupyter Notebooks, Julia

DATA WRANGLING

SQL, Spark, Pandas, Tableau

MACHINE LEARNING

Supervised, Unsupervised, & Reinforcement ML

DEEP LEARNING

Neural Networks, GANS, CNN, LSTM

NLP

Transformers, BERT, Sentiment Analysis

FRAMEWORKS

PyTorch and TensorFlow Keras, scikit-learn

TOOLS

Airflow, MLFlow, MLOPs, Spark, Kubeflow

DATA VISUALIZATION

D3.JS, Plotly, MatPlotLib, Tableau

 SPECIALIZATION TRACKS

  • Machine Learning with Python

    • Mathematics for Data Science
    • Programming with Data: Python and Pandas
    • Hands-on Machine Learning with scikit-learn
    • Machine Learning at Scale using Apache Spark
    • Supervised Learning for Missing Data
    • Causal Inference for Data Science
    • Meta-learning for Machine Learning
    • Cybersecurity in ML
    • Reinforcement Learning with TF-Agents
    • Reinforcement Learning for Human Language

  • Deep Learning

    • Intro to Neural Networks for Deep Learning
    • PyTorch Hands-on Training
    • Deep Reinforcement Learning
    • Deep Learning with TensorFlow
    • Recommendation Systems with Deep Learning
    • Deep Learning with Keras
    • TFF (TensorFlow Federated Learning)
    • How to Identify Deepfakes
    • Deep Learning CNNs and GANs

  • Data Engineering / MLOps

    • Mathematics for Data Science
    • SQL for Data Science
    • Deploying ML Pipelines with Open-data
    • Kubeflow & Kubernetes
    • Auto Machine Learning
    • Debugging Machine Learning
    • Introducing Flyte
    • Reproducible Data Science in Pachyderm
    • Data Science Best Practices: Continuous Delivery for Machine Learning

  • NLP

    • Introduction to NLP and Topic Modeling
    • Transfer Learning in NLP
    • Training NLP Models with Deep Reinforcement Learning
    • State-of-the-Art NLP in TensorFlow and PyTorch
    • Active Learning for Data Labeling
    • NLP with LSTMs (Deep learning)
    • Using HuggingFace Transformers for NLP

  • Data Analytics

    • Statistics and Probability Distribution
    • Big Data using Spark
    • Streaming Analytics
    • Augmented Analytics
    • Decision Analytics
    • Graph Analytics
    • No-Sql
    • In-memory computing

Live sessions include, but are not limited to, the ones listed below.

DIVE DEEPER

Get hands-on experience in job-ready skills

West 2022 Mini-Bootcamp Overview

Begin your data science training with pre-conference, live, and on-demand training to build confidence in the fundamentals. Steadily progress through the Bootcamp week with our hands-on training and expert-led sessions and workshops. Post-conference, take advantage of our continuing on-demand training sessions on the Ai+ Training platform. Refer to the timeline below:

Pre-Bootcamp On-demand Training

Get started in your learning track with free access to introductory concepts with short, to-the-point and industry driven on-demand courses.

Schedule: Self-paced

Note: only accessible to Mini-Bootcamp registrants.

Pre-Bootcamp and Live Training Warm up

Why wait? Get started early with free access to summer and fall Live Training courses to learn concepts with short, to-the-point, and industry-driven training.

Data Wrangling with SQL – July 26th, 2022
PyTorch 101 – August 24th, 2022
Data Literacy for Data Science and Machine Learning – October 20th, 2022
Probabilistic programming bayesian inference with Python – November 15th, 2022

The full schedule and more details coming soon! Note: only accessible to Mini-Bootcamp registrants.

Bootcamp Orientation

Bootcamp Orientations are offered to address:

  • All questions on the ODSC West Bootcamp program
  • Understand program content
  • Find out what courses are available on Ai+
  • Get help with assignments on Ai+

Use the following link to access Virtual Orientation. In-Person Mini-Bootcamp Orientation is available on this page.

Note: only accessible to Mini-Bootcamp registrants.

Bootcamp Day 1 : Beginner Level SEssions (October 31st) (Virtual Only)

Live sessions include but are not limited to ones below. Check full half-day and full-day training program at the Training Page.

  • Introduction to Statistics for Data Science
  • An Introduction to Data Literacy with SQL
  • Introduction to Python for Data Analysis
  • Introduction to Machine Learning

Bootcamp Day 2 & 3 : Beginner to Intermediate Level (November 1st - 2nd) (Virtual/In-Person)

Live sessions include but are not limited to ones below. Check full half-day and full-day training program at the Training Page.

  • Deep Learning with Python and Keras (Tensorflow 2)
  • NLP Fundamentals
  • Machine Learning with XGBoost
  • Self-Supervised and Unsupervised Learning for Conversational AI and NLP
  • Building a GPT-3 Powered Knowledge Base Bot for Discord
  • Machine Learning with Python: A Hands-On Introduction
  • A Practical Tutorial on Building Machine Learning Demos with Gradio
  • A Hands-on Introduction to Transfer Learning
  • Foundations of Deep Reinforcement Learning

Bootcamp Day 4: Beginner-Intermediate-Advanced Level ( NOVEMBER 3rd) (Virtual/In-Person)

Virtual Live sessions include but are not limited to the ones below. Check the full half-day and full-day training program at the Training Page.

  • Advanced Deep Learning with Tensorflow
  • NLP Word Embeddings
  • Deep Learning for Detecting DeepFakes
  • Machine Learning at Scale using Apache Spark
  • Practical Tutorial on Uncertainty and Out-of-distribution Robustness in Deep Learning
  • StructureBoost: Gradient Boosting with Categorical Structure
  • Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

subscription to the Ai+ Training Platform

What is included

  • Foundations for Machine Learning Bootcamp: 14 On-Demand Sessions
  • Machine Learning Certification
  • Certification Assessments and Badges
  • Entire On-Demand Training Library (50 + courses) and All Previous ODSC Conference recordings (500+ sessions)
  • Monthly Webinars
  • All Assessments (Skills, Feedback, Coding Proficiency and Coding Proficiency Exam)
  • Certificate of Completion for Every course 

And much more. See everything that is included  here


Choose Your Pass

Monday | Virtual Mini-Bootcamp Training Sessions 

Premium 1-Year Subscription to Ai+ Training (value =  $700)

Access to All Virtual Sessions & Events (Tue-Thu)

ODSC Keynotes & Talks (Wed-Thu)

Prep Training:  live and On-demand (value = $499)

On-demand Access to All Conference recordings 

Access to AI Solution Showcase Expo Area (Wed-Thu) 

Access to In-person Mini-Bootcamp Training Sessions

Access to In-person Network Reception & Networking Events

Live

Virtual Mini-Bootcamp

$599

Door price - $749 - SAVE $150

4-Days

 

Register | Last50

In-Person 

Mini-Bootcamp

$1499

 Door price - $1874 - SAVE $375 

4-Days

( In-Person Tue-Thu)

Register | Last50

NOVEMBER LIVE TRAINiNG

November 15th

If you can write a model in sklearn, you can make the leap to Bayesian inference with PyMC3, a user-friendly intro to probabilistic programming (PP) in Python. PP just means building models where the building blocks are probability distributions! And we can use PP to do Bayesian inference easily. Bayesian inference allows us to solve problems that aren’t otherwise tractable with classical methods.

The intention is to get hands-on experience building PyMC3 models to demystify probabilistic programming / Bayesian inference for those more well versed in traditional ML, and, most importantly, to understand how these models can be relevant in our daily work as data scientists in business.

Let’s build up our knowledge of probabilistic programming and Bayesian inference! All you need to start is basic knowledge of linear regression; familiarity with running a model of any type in Python is helpful.

LEARN MORE
BEGINNER

PAST LIVE TRAINING

October 20th

Data literacy is fundamental to mastering many areas of AI including data science, machine learning, deep learning and data engineering. This fast paced but comprehensive tutorial will introduce you to fundamental data concept that will help you grasp underlying concepts and serve as a foundation course and prerequisite to more advanced machine learning and data science topics. Over 5 lessons you will learn the language of data and better understand the data life cycle and its role in building machine learning models.
LEARN MORE
BEGINNER

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.

LEARN MORE
BEGINNER

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.

As this is an interactive training session, in addition to covering these data 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.

LEARN MORE
BEGINNER

Includes a Pre-Conference Live Training Session

Get a free AI+ live training session with your ODSC Mini-Bootcamp pass and start training now!

The Bootcamp Experience

Virtual Experience

In-Person Experience

LEARN FROM THE LEADING EXPERTS

Our mission is to provide you with applied training from some of the top instructors in the field. Previous ODSC West instructors include:
Analytics EngineerGreenhouse Software

Andrew Zirm, PhD

Andrew is a Ph.D. Astrophysicist who made the switch from academia to data science (via the Insight Data Science program) in 2014. He was the first data scientist hired at Greenhouse Software where he has worked on many internal data science projects and a few customer-facing data-powered product features. Andrew lives in New Jersey with his wife and son.

Statistics for Data Science(Bootcamp)

 

Senior Data ScientistAstraZeneca

Leonidas Souliotis, PhD

Leonidas (Leo) is a Senior Data Scientist at Astrazeneca. His work is focused around machine learning in oncology, including clinical and non clinical applications. He is also enthusiastic about NLP applications in oncology and how this can be used to leverage patient treatment. He is also a workshop facilitator in the European Leadership University (ELU), NL and has also been a data science educator at DataCamp. He holds a PhD from the University of Warwick, UK. in bioinformatics and ML, an MSc in statistics from Imperial College London, UK and a BSc in Statistics and Insurance Science from the University of Piraeus, GR.

Introduction to Python for Data Analysis(Bootcamp)

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.

Founder and CEOODSC

Sheamus McGovern

Sheamus McGovern is the founder of ODSC (The Open Data Science Conference). He is also a software architect, data engineer, and AI expert. He started his career in finance by building stock and bond trading systems and risk assessment platforms and has worked for numerous financial institutions and quant hedge funds. Over the last decade, Sheamus has consulted with dozens of companies and startups to build leading-edge data-driven applications in finance, healthcare, eCommerce, and venture capital. He holds degrees from Northeastern University, Boston University, Harvard University, and a CQF in Quantitative Finance.

An Introduction to Data Wrangling with SQL(Bootcamp)

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)

VP of LabsThomson Reuters

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.

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)

CEOIvy

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)

 

Climate & Agronomic Data ScientistSyngenta

Serg Masis

Serg MasĂ­s 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)

 

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)

 

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)

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.

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)

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)

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 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)

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)


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Choose Your Pass

Monday | Virtual Mini-Bootcamp Training Sessions 

Premium 1-Year Subscription to Ai+ Training (value =  $700)

Access to All Virtual Sessions & Events (Tue-Thu)

ODSC Keynotes & Talks (Wed-Thu)

Prep Training:  live and On-demand (value = $499)

On-demand Access to All Conference recordings 

Access to AI Solution Showcase Expo Area (Wed-Thu) 

Access to In-person Mini-Bootcamp Training Sessions

Access to In-person Network Reception & Networking Events

Live

Virtual Mini-Bootcamp

$599

Door price - $749 - SAVE $150

4-Days

 

Register | Last50

In-Person 

Mini-Bootcamp

$1499

 Door price - $1874 - SAVE $375 

4-Days

( In-Person Tue-Thu)

Register | Last50

DISCOVER YOUR BOOTCAMP PATH

Free Warmup Tutorials

Get Prepped with AI+ Live training

Paid training: one free included in your pas

REGISTER YOUR INTEREST FOR ODSC WEST 2023

CONTINUOUS LEARNING ON-DEMAND

With Each ODSC Bootcamp pass, you get a 1 year Ai+ on-demand hands-on training subscription (Value $700)

Ai+ is the only hands-on training platform solely developed for AI practitioners. Keep training with the top names in the industry. Free for 1 year with an ODSC West Mini-Bootcamp Pass.

Learn More

WHO SHOULD ATTEND

Who Should Attend

Beginners

  • Beginners pursuing data science careers

  • Those trying to decide on the right data science path

  • Business Analysts and Data Analysts seeking new skills

Intermediates

  • Data Scientists

  • Data Engineers

  • ML Engineers

  • Software Engineers

Citizens

  • Citizen Data Scientists

  • Data Wranglers

  • Data Science 4 Good

  • Data Journalists 

Companies Represented at ODSC Bootcamps

We’re Proud to Have the Best and Brightest in Attendance

NEED MORE REASONS TO SIGN-UP?

 Learn Fundamentals

Prepare for your immersive training by learning core fundamentals with our Pre-Bootcamp sessions including:

  • Fundamentals of Python Programming

  • Data Analysis in Python

  • Introduction to Statistics for Data Science

  • Fundamental Analytics for Data Engineering

Learn Frameworks & Tools

Get hands-on experience with in-demand data science tools and frameworks, including:

  • Python, Jupyter Notebooks

  • R programming, Julia, Scala, Stan

  • Apache Spark, MLlib, Streaming

  • Tensorflow, MXNet, Caffe, CNTK

  • Scikit-learn, Theano, Shogun, Pylearn2

  • See schedule for more tools and frameworks…

Learn Core Topics

Get hands-on experience in the most important data science topics with over 100+ beginner to intermediate sessions to choose from, including

  • Deep Learning

  • Reinforcement Learning and Deep Reinforcement Learning

  • Machine Learning

  • Transfer Learning

  • Natural Language Processing

  • Text Analytics

  • Data Visualization

  • Data Modeling and Data Wrangling

  Build your Network

A career in data science opens up the possibility of rewarding and exciting work. Build your network and help grow your career

  • Learn from, and connect with, world-leading data science experts

  • Stop by our Career Expo and meet top hiring companies like Google, DataRobot, and dozens of others

  • Join our various networking events, such as Meet the Speakers, Meet the Experts, Dinner with Data Scientists; plus other networking opportunities

  • Network with hundreds of data science attendees to learn what it’s like to be a data scientist

Learn More About Our Program



Participate at ODSC West 2023

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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.

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Cambridge, MA 02142
info@odsc.com

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