Register your interest
Register now

SAN FRANCISCO

OCTOBER 30th – NOVEMBER 2nd

UNLOCK YOUR FULL POTENTIAL

Kick-start your career in data science in 4 immersive days. Get the skills you need, taught by world-renowned experts.
Our AI 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 ANODSC AI BOOTCAMP

WHY ODSC BOOTCAMPS?  
BUILD YOUR SKILLS AND BUILD YOUR NETWORK

7 Primer Courses

With Pre-Conference training you can start learning the fundamentals before the conference. Learn Python, Programming, SQL, Data Wrangling, AI , and more. 

4-day Immersive

From October 30th to November 2nd, join our 4-day live immersive AI Mini-Bootcamp. Get hands-on experience from dozens of machine learning and deep learning sessions.

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. 

Certification  

AI Bootcamp attendees get a certificate of completion and access to orientation sessions. We also offer test-based certificates. Certificates are available for both in-person and virtual

WHAT YOU WILL LEARN

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 & LLMs and Generative AI

Transformers, Large Language Models, & GenAI

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

  • NLP & LLMs

    • Introduction to NLP and Topic Modeling
    • Finetuning LLMS
    • LLMS: LangChain and AI Agents
    • Transfer Learning in NLP
    • Training NLP Models with Deep Reinforcement Learning
    • State-of-the-Art NLP in TensorFlow and PyTorch
    • Self supervised learning
    • NLP with LSTMs (Deep learning)
    • Using HuggingFace Transformers for NLP

  • 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

  • Generative AI

    • LLMS vs traditional small-scale modelscx
    • Open-source LLM Chat Models
    • Foundational models and LLMs
    • AI Pair Programming 
    • Reinforcement Learning from Human Feedback (RLHF)
    • Challenges in Deployable Generative AI

  • 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

Past West AI 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 Live Virtual Training Primer Courses

Get started in your learning track with free access to 5 LIVE PRE-BOOTCAMP COURSES to learn core concepts from top instructors.  These are held virtually. Currently, training sessions include:

  • ODSC Bootcamp Primer: Introduction to Data Course – available on-demand;
  • ODSC Bootcamp Primer: Introduction to SQL Course – available on-demand;
  • ODSC Bootcamp Primer: Programming Primer Course with Python – available on-demand;
  • ODSC Bootcamp Primer: AI Primer – Thursday, October 5th, 2023;
  • ODSC Bootcamp Primer: Data Wrangling with Python – Thursday, October 19th, 2023 

Each course is $199 (total $796) Free to Mini-Bootcamp, VIP registrants

Additional Live training course available at 50% discount to ticket holders.

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+

Orientation sessions are held virtually and will be scheduled for October 23rd, 2023

Note: only accessible to Mini-Bootcamp registrants.

Bootcamp Day 1 : Beginner Level SEssions (October 30th)

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
  • Data Wrangling with Python
  • Introducing Machine Learning in Python with Scikit-learn
  • Introduction to Math for Data Science
  • Confident Data Science

Bootcamp Day 2 & 3 : Beginner to Intermediate Level (October 31st - November 1st) (Virtual/In-Person)

ODSC West 2023 Live sessions will be announced when our program is released on our Training Page.

Here is some of the prior session from last year’s event:

  • 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 2nd) (Virtual/In-Person)

ODSC West 2023 Live sessions will be announced when our program is released on our Training Page.

Here is some of the prior session from last year’s event:

  • 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

Certification

Showcase your new skill sets with certificate courses from ODSC and Ai+.

    • ODSC West 2023 Mini-Bootcamp Certification of Completion
    • Ai + Training LLM and Generative AI Certificate Course (Included in Bootcamp and VIP Passes)
    • Ai + Training Machine Learning Certificate Course (Included in Bootcamp and VIP Passes)


Choose Your Pass

Virtual Mini-Bootcamp Training Sessions 

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

Access to All Virtual Sessions & Events (Mon-Thur)

ODSC Keynotes & Talks (Tue-Thur)

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

On-demand Access to All Conference recordings 

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

Access to ODSC/AI Certification Tracks

Access to In-person Mini-Bootcamp Training Sessions

Access to In-person Network Reception & Networking Events

Live

Virtual Mini-Bootcamp

$699

Door price - $874 

4-Days ( Mon-Thur)

Register & Save 20% 

In-Person 

Mini-Bootcamp

$1279

Door price - $1599 

4-Days

( In-Person Mon-Thur)

Register & Save 20%

NOVEMBER LIVE TRAINiNG

Free Pre-Bootcamp Warmup Workshops

Data, Coding, AI, and LLM preparation courses for our AI Mini-Bootcamps

AI 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, Large Language Models, Prompt Engineering, and Generative AI

Hosted on Ai+ Training and included FREE as part of your ODSC AI Mini-Bootcamp Pass.

learn more

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. Some of Past ODSC West instructors include:
Co-Founder and Instructor | Principal DS ManagerGenerative AI.net | Infosys

Martin Musiol

Long before the buzz surrounding generative AI, Martin Musiol was already advocating for its significance in 2015. Since then, he has been a frequent speaker at conferences, podcasts, and panel discussions, addressing the technological advancements, practical applications, and ethical considerations of generative AI. Martin Musiol is a founder of generativeAI.net, a lecturer on AI to over 3000 students, and publisher of the newsletter ‘Generative AI: Short & Sweet’. As the lead for GenAI Projects in Europe at Infosys Consulting (previously at IBM), Martin Musiol helps companies globally harness the power of generative AI to gain a competitive advantage. -> https://www.linkedin.com/in/martinmusiol1/ and his webpage: https://generativeai.net/

Generative AI, Autonomous AI Agents, and AGI – How new Advancements in AI will Improve the Products we Build(Training)

PrincipalNumeristical

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.

Uncertainty Quantification: Approaches and Methods(Training)

GDS Developer Advocate - Data ScientistNeo4j, Inc.

Alison Cossette

Alison Cossette is a dynamic Data Science Strategist, Educator, and Podcast Host. As a Developer Advocate at Neo4j specializing in Graph Data Science, she brings a wealth of expertise to the field. With her strong technical background and exceptional communication skills, Alison bridges the gap between complex data science concepts and practical applications.

Alison’s passion for responsible AI shines through in her work. She actively promotes ethical and transparent AI practices and believes in the transformative potential of responsible AI for industries and society. Through her engagements with industry professionals, policymakers, and the public, she advocates for the responsible development and deployment of AI technologies.

Alison’s academic journey includes pursuing her Master of Science in Data Science program, specializing in Artificial Intelligence, at Northwestern University and research with Stanford University Human-Computer Interaction Crowd Research Collective. Alison combines academic knowledge with real-world experience. She leverages this expertise to educate and empower individuals and organizations in the field of data science.

Overall, Alison Cossette’s multifaceted background, commitment to responsible AI, and expertise in data science make her a respected figure in the field. Through her role as a Developer Advocate at Neo4j and her podcast, she continues to drive innovation, education, and responsible practices in the exciting realm of data science and AI.

Bridging the Gap: Light Code Solutions to Uniting Social Science and Modern Knowledge Graphs(Workshop)

From Nodes to Natural Language: Grounding LLMs with Graphs & Graph Data Science(Talk)

Chief Data & A.I. OfficerFanatics Collectibles

Jeffrey Yau, PhD

Jeffrey Yau is currently Chief Data & A.I. Officer at Fanatics Collectibles. Most recently, he served as Global Head of Data Science, Analytics & Engineering at Amazon Music where he oversaw multiple teams who developed both insights-packed analytics and end-to-end statistical and machine learning systems. Prior to Amazon, Jeffrey worked at WalmartLabs as the VP of Data Science & Engineering where he led the team responsible for powering Walmart store mobile apps and the entire store finance system. Further, his team created end-to-end machine learning systems for key business initiatives and had a multi-billion dollar impact annually on Walmart U.S.

Over the years, he has held various senior level positions in quantitative finance at global investment management firm AllianceBernstein, consulting firm Data Science at Silicon Valley Data Science, multinational financial services company Charles Schwab Corporation, and the world’s leading professional services firm KPMG. He began his career as a tenure-track Assistant Professor of Economics at Virginia Tech, and he was an adjunct professor at UC Berkeley, Cornell, and NYU, teaching machine learning and advanced statistical modeling for finance and business.

Blue Collar ML ArchitectFreelancer

Oliver Zeigermann

Oliver Zeigermann has been developing software with different approaches and programming languages for more than 3 decades. In the past decade, he has been focusing on Machine Learning and its interactions with humans.

MLOps: Monitoring and Managing Drift(Training)

 

Founder & CEOTDengine

Jeff Tao

Jeff Tao is the founder and CEO of TDengine. He has a background as a technologist and serial entrepreneur, having previously conducted research and development on mobile Internet at Motorola and 3Com and established two successful tech startups. Foreseeing the explosive growth of time-series data generated by machines and sensors now taking place, he founded TDengine in May 2017 to develop a high-performance time-series database purpose-built for modern IoT and IIoT businesses.

What is a Time-series Database and Why do I Need One?(Workshop)

Board Trustee at University of California, Berkeley, Former co-founder and Engineer at Oculus VR

Jack McCauley

Jack McCauley an Innovator in Residence at Jacobs Institute for Design Innovation at UC Berkeley, Professor at UC Berkeley, Co-Founder of Oculus, an American engineer, hardware designer, inventor, video game developer and philanthropist. Jack is best known for designing the guitars and drums for the Guitar Hero video game series, and as a co-founder and former chief engineer at Oculus VR. At Oculus, Jack designed and built the Oculus DK1 and DK2 virtual reality headsets. Oculus was acquired by Facebook for $2 Billion. McCauley holds numerous U.S. patents for inventions in software, audio effects, virtual reality, motion control, computer peripherals, and video game hardware and controllers. Jack was awarded a full scholarship to attend University of California, Berkeley where he earned a BSc., EECS in Electrical Engineering and Computer Science in 1986. Jack has authored numerous research papers in the field of artificial intelligence (AI) and mathematical modeling of AI-based systems and is currently pursuing new projects at his private R&D facility and hardware incubator in Pleasanton, California.

AI and Video Games : The Evolution(Talk)

Data Scientist, Software Engineer, Author of Hands-On Data Analysis with PandasBloomberg

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.

CEO and Software Architect, Data Engineer and AI expertODSC

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.

Data Wrangling with Python(Bootcamp)

Founder/CEOSematic

Emmanuel Turlay

Emmanuel Turlay is the founder and CEO of Sematic, an open-source ML infrastructure company. Emmanuel started his career in academia researching particle physics at CERN, before branching out into tech and moving to the US in 2014. He led engineering teams at Instacart, and then Cruise, where he led the ML Infrastructure team for four years. In 2022, Emmanuel founded Sematic to bring learnings from building ML infrastructure for robotaxis to the rest of the industry in an open-source manner.

Battle Scars from the MLOps Trenches of the Robotaxi Industry(Talk)

Machine Learning EngineerHugging Face

Rajiv Shah, PhD

Rajiv Shah is a machine learning engineer at Hugging Face who focuses on enabling enterprise teams to succeed with AI. Rajiv is a leading expert in the practical application of AI. Previously, he led data science enablement efforts across hundreds of data scientists at DataRobot. He was also a part of data science teams at Snorkel AI, Caterpillar, and State Farm. Rajiv is a widely recognized speaker on AI, published over 20 research papers, and received over 20 patents, including sports analytics, deep learning, and interpretability. Rajiv holds a PhD in Communications and a Juris Doctor from the University of Illinois at Urbana Champaign. While earning his degrees, he received a fellowship in Digital Government from the John F. Kennedy School of Government at Harvard University. He also has a large following on AI-related short videos on Tik Tok and Instagram at @rajistics.

Evaluation Techniques for Large Language Models(Tutorial)

 

Chief Scientist and Co-FounderCleanlab

Jonas Mueller

Jonas Mueller is Chief Scientist and Co-Founder at Cleanlab, a software company providing data-centric AI tools to efficiently improve ML datasets. Previously, he was a senior scientist at Amazon Web Services developing AutoML and Deep Learning algorithms which now power ML applications at hundreds of the world’s largest companies. In 2018, he completed his PhD in Machine Learning at MIT, also doing research in NLP, Statistics, and Computational Biology.

Jonas has published over 30 papers in top ML and Data Science venues (NeurIPS, ICML, ICLR, AAAI, JASA, Annals of Statistics, etc). This research has been featured in Wired, VentureBeat, Technology Review, World Economic Forum, and other media. He has also contributed open-source software, including the fastest-growing open-source libraries for AutoML (https://github.com/awslabs/autogluon) and Data-Centric AI (https://github.com/cleanlab/cleanlab).

How to Practice Data-Centric AI and Have AI improve its Own Dataset(Tutorial)

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.

Instructor University of Southern California

Thomas Nield

Thomas Nield is the founder of Nield Consulting Group and Yawman Flight, as well as an instructor at University of Southern California. He enjoys making technical content relatable and relevant to those unfamiliar or intimidated by it. Thomas regularly teaches classes on data analysis, machine learning, mathematical optimization, and practical artificial intelligence. At USC he teaches AI System Safety, developing systematic approaches for identifying AI-related hazards in aviation and ground vehicles. He’s authored three books, including Essential Math for Data Science (O’Reilly) and Getting Started with SQL (O’Reilly)

He is also the founder and inventor of Yawman Flight, a company developing universal handheld flight controls for flight simulation and unmanned aerial vehicles.

Introduction to Math for Data Science(Bootcamp)

Lead Data Scientist, Best Selling Author of AI/ML booksSyngenta

Serg Masis

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. He’s an 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 concerning 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” with do-it-yourself projects for AI hobbyists and practitioners alike.

 

Azure Specialist - Data and Artificial IntelligenceMicrosoft

Valentina Alto

Valentina is a Data Science MSc graduate and Cloud Specialist at Microsoft, focusing on Analytics and AI workloads within the manufacturing and pharmaceutical industry since 2022. She has been working on customers’ digital transformations, designing cloud architecture and modern data platforms, including IoT, real-time analytics, Machine Learning, and Generative AI. She is also a tech author, contributing articles on machine learning, AI, and statistics, and recently published a book on Generative AI and Large Language Models.

In her free time, she loves hiking and climbing around the beautiful Italian mountains, running, and enjoying a good book with a cup of coffee.

The AI Paradigm Shift: Under the Hood of a Large Language Models(Workshop)

Customer Success Manager and Value EngineerTangent Works

Philip Wauters

Philip Wauters is Customer Success Manager and Value engineer at Tangent Works working on practical applications of time series machine learning at customers from various industries such as Siemens, BASF, Borealis and Volkswagen. With a commercial background and experience with data engineering, analysis and data science his goal is to find and extract the business value in the enormous amounts of time-series data that exists at companies today.

Learn how to Efficiently Build and Operationalize Time Series Models in 2023(Workshop)

The Tangent Information Modeler, time series modeling reinvented(Solution Showcase)

Abstract:

Modeling time series data is difficult due to its large quantities and constantly evolving nature. Existing techniques have limitations in scalability, agility, explainability, and accuracy. Despite 50 years of research, current techniques often fall short when applied to time series data. The Tangent Information Modeler (TIM) offers a game-changing approach with efficient and effective feature engineering based on Information Geometry. This multivariate modeling co-pilot can handle a wider range of time series use cases with award-winning results and incredible performance.

During this demo session we will showcase how best-in-class and very transparent time series models can be built with just one iteration through the data. We will cover several concrete use cases for advanced time series forecasting, anomaly detection and root cause analysis.

Chief AI Officer & Chief ScientistFiddler AI

Krishnaram Kenthapadi

Krishnaram Kenthapadi is the Chief AI Officer & Chief Scientist of Fiddler AI, an enterprise startup building a responsible AI and ML monitoring platform. Previously, he was a Principal Scientist at Amazon AWS AI, where he led the fairness, explainability, privacy, and model understanding initiatives in the Amazon AI platform. Prior to joining Amazon, he led similar efforts at the LinkedIn AI team, and served as LinkedIn’s representative in Microsoft’s AI and Ethics in Engineering and Research (AETHER) Advisory Board. Previously, he was a Researcher at Microsoft Research Silicon Valley Lab. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the senior program committees of FAccT, KDD, WWW, WSDM, and related conferences, and co-chaired the 2014 ACM Symposium on Computing for Development. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 50+ papers, with 7000+ citations and filed 150+ patents (70 granted). He has presented tutorials on privacy, fairness, explainable AI, model monitoring, responsible AI, and generative AI at forums such as ICML, KDD, WSDM, WWW, FAccT, and AAAI, given several invited industry talks, and instructed a course on responsible AI at Stanford.

Deploying Trustworthy Generative AI(Tutorial)

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.

Introducing Machine Learning in Python with Scikit-learn(Training)


PAST PRE-BOOTCAMP PRIMER COURSES

ODSC Instructor

Introduction to Data Course

ODSC Instructor

Introduction to Programming with Python Course

ODSC Instructor

Data Wrangling with Python Course

ODSC Instructor

Introduction to SQL Course

ODSC Instructor

Introduction to AI Course

ODSC Instructors

LLMs, Prompt Engineering, & Gen AI

Past Training & Workshops

Oliver Zeigermann - Machine Learning Architect | Freelancer

Training: MLOps: Monitoring and Managing Drift

Thomas Nield | Instructor | Founder | University of Southern California | Nield Consulting Group and Yawman Flight

Training: Introduction to Math for Data Science

Alison Cossette - Developer Advocate - Data Science | Neo4j

Workshop: Bridging the Gap: Light Code Solutions to Uniting Social Science and Modern Knowledge Graphs

Rajiv Shah, PhD - Machine Learning Engineer | Hugging Face

Tutorial: Evaluation Techniques for Large Language Models

Gwendolyn D. Stripling, PhD - Lead AI & ML Content Developer | Google Cloud

Tutorial: No-Code and Low-Code AI: A Practical Project Driven Approach to ML

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

Half-Day Training: Introducing Machine Learning in Python with Scikit-learn

Jeffrey Yau, PhD - Chief Data & A.I. Officer | Fanatics Collectibles

Half-Day Training: An Introduction to Time Series Forecasting: A Tutorial

Martin Musiol - Instructor | Principal Data Science Manager | Generative AI.net | Infosys Consulting

Training: Generative AI, Autonomous AI Agents, and AGI – How new Advancements in AI will Improve the Products we Build

Jeff Tao - Founder & CEO | TDengine

Workshop: What is a Time-series Database and Why do I Need One?

Serg Masis - Lead Data Scientist, Best Selling Author of AI/ML books | Syngenta

Workshop: Facial Recognition from Scratch with Python and JS

Serg Masis - Lead Data Scientist, Best Selling Author of AI/ML books | Syngenta

Workshop: Facial Recognition from Scratch with Python and JS

Adam Ross Nelson - Data Scientist + Career Coach | Up Level Data, LLC

Half-Day Training:: Confident Data Science

Sheamus McGovern - CEO and Software Architect, Data Engineer, and AI expert | ODSC

Half-Day Training:Programming Course with Python

Past Talks & Panels

Dominic Bohan - Co-Founder | StoryIQ

Talk: Building a Data-Driven Workforce

Stefanie Molin - Software Engineer, Data Scientist, Chief Information Security Office, Author of Hands-On Data Analysis with Pandas | Bloombergv

Talk: Data Morph: A Cautionary Tale of Summary Statistics

Emmanuel Turlay - Founder/CEO | Sematic

Talk: Battle Scars from the MLOps Trenches of the Robotaxi Industry

Michelle Yi - Board Member | Women in Data

Talk: Building Generative AI Applications: An LLM Case Study

Jack McCauley - Board Trustee at University of California, Berkeley, Former co-founder and Engineer, Oculus VR.

Talk: AI and Video Games : The Evolution

Doris Lee - CEO and Cofounder | Ponder

Talk: Scaling your Data Science Workflows by Changing a Single Line of Code

Patrick Hall - assistant professor | Principal Scientist | George Washington University School of Business| BNH.AI

Talk: De-risking Language Models for Adoption

Nils Reimers - Director of Machine Learning | Cohere.ai

Talk: Connecting Large Language Models – Common pitfalls & challenges

Back To School  50% Off  Ends In

CERTIFICATION

Stand Out: Earn a Job Ready Credential

Not just another generic certificate, ODSC is recognized as one of the foremost open-source communities in the field of data science.

Get access to Ai+ Training and job and career recommendation resources upon successful completion.

Achievement Badges

Earn a badge for each core BootCamp course you successfully complete.

Certification Recognition

Earn all 8 badges and receive a Machine Learning Certificate.

ODSC will publicize your accomplishment (opt-in) in our quarterly newsletter, which reaches over 110,000 subscribers.

Share your earned badges and certificates on LinkedIn and on your resume. 

Free Warmup Tutorials

Get Prepped with AI+ Live training

Paid training: one free included in your pas

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

Learn More About Our AI Bootcamp Program

Learn more about ODSC West’s Team Training options, including our dedicated team AI Mini-Bootcamp and exclusive team discounts. Complete the form to be contacted by one our of knowledgeable team members.



Open Data Science

 

 

 

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

Menu
Conferences
Extras
Copyright ODSC 2023. 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