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co:rise accelerates your professional growth through live, small group courses created and taught by top experts.

Live lessons created and taught by industry experts
A global network through small professional cohorts
Efficient education that fits in with your busy schedule
Real-world projects that bring your learning to life
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Learn from top industry experts
Our instructors lead Machine Learning and Data Science teams at cutting-edge companies. Each week, you’ll have live sessions with them so you can elevate your technical skillset... faster.
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Rishabh Mehrotra

Rishabh Mehrotra currently works as a Director of Machine Learning at ShareChat based in London. His current research focuses on machine learning for marketplaces, multi-objective modeling of recommenders, and the creator ecosystem. Prior to ShareChat, he was an Area Tech Lead and Staff Scientist/Engineer at Spotify where he led multiple ML projects from basic research to production across 400+ million users. Rishabh has a PhD in Machine Learning from UCL, and 50+ research papers and patents. Some of his recent work has been published at conferences including KDD, WWW, SIGIR, RecSys, and WSDM. He has co-taught a number of tutorials and summer school courses on the topics of learning from user interactions, marketplaces, and personalization.

Course
Personalized Recommendations at Scale
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Hien Luu

Hien Luu is Head of Machine Learning Platform at DoorDash, author of Beginning Apache, and has been teaching at UCSC Extension School for more than 10 years. Previously, Hien was an Engineering Manager at LinkedIn where he helped build big data applications and infrastructure. And before that, Hien was at Uber, where he led the engineering team responsible for building a streaming processing platform to extract actionable business insights.

Course
Real Time Machine Learning
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Grant Ingersoll

Grant is a CTO, independent consultant and advisor. He is the former CTO of the Wikimedia Foundation and the co-founder and ex-CTO of Lucidworks, co-author of Taming Text, co-founder of Apache Mahout and a long-standing committer on the Apache Lucene and Solr open source projects. Grant’s experience includes managing a large team of engineers, researchers and data scientists at a top ten website as well as engineering a variety of search, question answering, and natural language processing applications for a variety of domains and languages. He earned his B.S. from Amherst College in Math and Computer Science and his M.S. in Computer Science from Syracuse University.

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Pamela Fox

Pamela Fox loves to learn, teach, and create. She’s currently a UC Berkeley lecturer, teaching a Python class to 1000+ students each semester. Previously, at Khan Academy, she created all of the programming courses and also helped develop the interactive platform for teaching programming in Python/JS. In her role before that as one of Coursera’s first frontend developers, she coded much of the original student-facing experience (also in Python/JS!). Her first job was at Google, as one of the first developer advocates, showing developers how to use their APIs in JS, Python, and many other languages.

Course
Python Crash Course
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Andrew Maas

Andrew Maas is currently at Apple working on data-centric deep learning. He completed a PhD in Computer Science at Stanford in 2015 advised by Andrew Ng and Dan Jurafsky. His dissertation focused on large scale deep learning methods for spoken and written language. Andrew has worked as an engineer and scientific advisor to several startups including Wit.ai, Coursera, and Semantic Machines. Prior to Apple, he built an NLP platform for precise healthcare language as cofounder of Roam Analytics. Additionally he also teaches CS224S: Spoken Language Processing as a visiting lecturer at Stanford University.

Course
Applied Machine Learning
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Daniel Tunkelang

Daniel is an independent consultant specializing in search, machine learning / AI, and data science. He was a founding employee of Endeca, a search pioneer that Oracle acquired in 2011. He then led engineering and data science teams at Google and LinkedIn. He’s worked with a wide range of consulting clients, including Apple, eBay, Pinterest, Salesforce, Yelp, and Zoom. He wrote a book on Faceted Search, published by Morgan & Claypool, and he blogs on Medium about search-related topics — particularly query understanding. Daniel has degrees in Computer Science and Math from MIT and a PhD in computer science from CMU.

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Emily Hawkins

Emily Hawkins is a rising analytics star and expert in the field of dbt. She leads the analytics data platform at Drizly (acquired by Uber for $1.1B) where she designed and works on a modern data stack using tools like Fivetran, Snowflake, dbt, Dagster, Looker, and Census.

She also holds a Master of Science in Business Analytics from Bentley University, used to be the analytics lead for Wayfair, and gives powerful presentations on data modeling, the modern data stack, and analytics engineering at leading industry conferences.

Course
Analytics Engineering with dbt
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Master the skills of the future
Learn efficiently through month-long, project-based courses for only $400 each.
Search
Learn all about search, from the fundamentals to the latest developments, with these two courses.
Search Fundamentals
We’ve designed this 2-week course to introduce the fundamentals of search to engineers, data scientists, and other technical professionals who are new to the area or interested in working in it. We will cover the basics of indexing, querying, hand-tuned ranking, aggregations, and search user interfaces.
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Grant Ingersoll
Former CTO at Wikimedia
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Daniel Tunkelang
Machine Learning Consultant
Limited Enrollment
Course Full
Sold out
Search with Machine Learning
We’ve designed this course to cover the fundamentals of integrating machine learning and natural language processing techniques into search engines. We’ll dive into using machine learning for ranking, content understanding, and query understanding, along with how to use embeddings, dense vectors and deep learning to improve retrieval and ranking.
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Link to an instructor's LinkedIn profile
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Grant Ingersoll
Former CTO at Wikimedia
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Daniel Tunkelang
Machine Learning Consultant
Limited Enrollment
Course Full
Sold out

Or take the complete Search track

Two courses for a combined price of $570

Machine Learning
Foundations
Do you have a background in data science or engineering? Learn the skills you need to become an effective machine learning engineer.
Applied Machine Learning
Design, build, and debug machine learning models for classification and regression tasks using a variety of datasets with Python (Numpy, Scikit, Pyplot). Learn best practices to plan and execute ML development projects whether large or small.
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Andrew Maas
Senior Manager at Apple and Instructor at Stanford University
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Limited Enrollment
Open for registration
Start Date
September 5
Price
US$ 400
Deep Learning Essentials
Learn the foundations of deep learning and practice with training and implementing neural networks in Pytorch while covering topics including convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs).
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Kevin Wu
PhD at Stanford; formerly at Waymo, Microsoft
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Eric Wu
PhD at Stanford, formerly at Google
Limited Enrollment
Open for registration
Start Date
October 10
Price
US$ 400
Data Centric Deep Learning
A trope we often hear is that data is at the heart of deep learning. But how exactly do you manage, improve, and repair deep learning models with data? This course teaches students applied techniques to measure and improve data quality, deep learning infrastructure to do continuous testing and deployment, iterative annotation pipelines, and techniques to respond to distribution shift and adversarial examples. Projects will be grounded in practical challenges spanning computer vision and natural language processing.
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Andrew Maas
Senior Manager at Apple and Instructor at Stanford
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Mike Wu
PhD Scholar at Stanford
Limited Enrollment
Open for registration
Start Date
November 14
Price
US$ 400

Or take the complete ML Foundations track

Three courses for a combined price of $960

All Courses
your ML & Data Skills
Ready to kick things up a notch?
Or just wish to go through the basics again?
Personalized Recommendations at Scale
Recommender systems undertake the challenging work to surface relevant content to users given millions of possible choices. In this course, we provide a holistic overview of Machine Learning modeling choices that go into developing and deploying multi-stage recommenders capable of serving recommendations from hundreds of million content choices to multiple hundred million users. The course goes into algorithmic models that power the various stages of the recommender, including the candidate generator, core ranker, user representation learning modules, and offline and online evaluation modules. The course ends with case studies, lessons, and practical considerations from deployed systems powering over 400 million users.
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Rishabh Mehrotra
Director of Machine Learning at ShareChat
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Limited Enrollment
Open for registration
Start Date
July 11
Price
US$ 400
Real Time Machine Learning
Stream processing enables companies in every industry to drive intelligence and action in real-time by processing data as it comes in from sources such as IoT devices, customer interactions and order transactions, social media, etc. Use cases for stream processing can broadly be categorized into four areas: streaming analytics, monitoring, leaderboard, and real-time predictions. This course will cover stream processing paradigm, and streaming processing system architecture. Each week, students will build stream processing applications using widely adopted stream processing technologies, such as Apache Kafka, Apache Spark and Apache Flink.
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Hien Luu
Head of Machine Learning Platform @ Doordash
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Limited Enrollment
Course Full
Sold out
Natural Language Processing
Build your own auto-compose tool and other projects while learning key NLP concepts such as BERT, entity recognition, intent classification, spacy, and Transformer models powering search engines like Google or voice assistants like Google Home or Amazon Alexa.
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Sourabh Bajaj
former Engineering Manager at Neeva
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Kaushik Rangadurai
Software Engineer at Facebook
Limited Enrollment
Open for registration
Start Date
July 11
Price
US$ 400
Web3 Applications & Filecoin/IPFS
This course provides an introduction to building simple DApps on a blockchain with storage on Filecoin/IPFS. You will finish the course with a mastery of the infrastructure tools used to build Web3 apps, knowledge of how to build a Web3 app that is accessible through the Web2 internet, and will understand how decentralized storage can be used to host files a Web3 app utilizes. We’ll start with the fundamentals of decentralized storage and then move on to programming for useful applications
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Robert Dowling
Associate Engineer at Filecoin Foundation
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Limited Enrollment
Open for registration
Start Date
July 11
Price
US$ 400
Building Computer Vision Applications
This course provides an introduction to machine learning for computer vision with a focus on practical applications relevant to industry teams. In this course, we will “reverse-engineer” a number of applications, such as traffic flow analysis, digital medicine, optical character recognition, and video analytics.
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Abubakar Abid
Machine Learning Team Lead at Hugging Face
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Limited Enrollment
Open for registration
Start Date
August 15
Price
US$ 400
Python Crash Course
Learn how to write programs in Python! By the end of our time together, you’ll have programmed decision-making algorithms, photo filters, procedural text generators, and a quiz using object-oriented programming.
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Pamela Fox
Lecturer, UC Berkeley
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Limited Enrollment
Open for registration
Start Date
August 15
Price
US$ 400
Analytics Engineering with dbt
Learn the modern analytics stack and best practices as an analytics engineer by using dbt with e-commerce data. Build data models to address real-world strategic questions.
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Emily Hawkins
Data Engineering Manager, Data Platform at Drizly
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Limited Enrollment
Open for registration
Start Date
October 3
Price
US$ 400
Search with Machine Learning
We’ve designed this course to cover the fundamentals of integrating machine learning and natural language processing techniques into search engines. We’ll dive into using machine learning for ranking, content understanding, and query understanding, along with how to use embeddings, dense vectors and deep learning to improve retrieval and ranking.
Affiliation logo
Link to an instructor's LinkedIn profile
Link to an instructor's Twitter profile
Instructor profile photo
Grant Ingersoll
Former CTO at Wikimedia
Affiliation logo
Link to an instructor's LinkedIn profile
Instructor profile photo
Daniel Tunkelang
Machine Learning Consultant
Limited Enrollment
Course Full
Sold out
Search Fundamentals
We’ve designed this 2-week course to introduce the fundamentals of search to engineers, data scientists, and other technical professionals who are new to the area or interested in working in it. We will cover the basics of indexing, querying, hand-tuned ranking, aggregations, and search user interfaces.
Affiliation logo
Link to an instructor's LinkedIn profile
Link to an instructor's Twitter profile
Instructor profile photo
Grant Ingersoll
Former CTO at Wikimedia
Affiliation logo
Link to an instructor's LinkedIn profile
Instructor profile photo
Daniel Tunkelang
Machine Learning Consultant
Limited Enrollment
Course Full
Sold out
MLOps: From Models to Production
Acquire the skills to build effective real-world ML systems (bootstrapping datasets, improving label quality, experimentation, model evaluation, deployment and observability) with hands-on projects. This course will help you bridge the gap between state-of-the-art ML modeling, and building real-world ML systems.
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Nihit Desai
CTO of Refuel.AI (ex-Facebook, Stanford)
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Limited Enrollment
Open for registration
Start Date
July 11
Price
US$ 400
People Analytics Jumpstart
Become a Qualified People Analytics Pro in 4 Weeks You will learn the techniques and tools you need to win roles in People Analytics, define the function for your organization, and stand out as a top analyst and HR specialist. This course focuses on the real-world day-to-day practice of analytics in modern, millennial-heavy workplaces.
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Max Brawer
Director of People Analytics at Twitch (ex-BuzzFeed, Google)
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Applied Machine Learning
Design, build, and debug machine learning models for classification and regression tasks using a variety of datasets with Python (Numpy, Scikit, Pyplot). Learn best practices to plan and execute ML development projects whether large or small.
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Instructor profile photo
Andrew Maas
Senior Manager at Apple and Instructor at Stanford University
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Link to an instructor's Twitter profile
Limited Enrollment
Open for registration
Start Date
September 5
Price
US$ 400
Data Science for Security and Fraud
This course provides an introduction to tackling digital security and fraud challenges using data science. Each week, we will apply data science techniques like anomaly detection and graph analysis to tackle a specific problem. At the end of this course, you will be able to build solutions to identify malicious and fraudulent behavior on the internet, and design ways to stop them.
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Yiing Chau Mak
Head of Data at MetaMap
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Limited Enrollment
Open for registration
Start Date
September 19
Price
US$ 400
Deep Learning Essentials
Learn the foundations of deep learning and practice with training and implementing neural networks in Pytorch while covering topics including convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs).
Affiliation logo
Link to an instructor's LinkedIn profile
Instructor profile photo
Kevin Wu
PhD at Stanford; formerly at Waymo, Microsoft
Affiliation logo
Link to an instructor's LinkedIn profile
Instructor profile photo
Eric Wu
PhD at Stanford, formerly at Google
Limited Enrollment
Open for registration
Start Date
October 10
Price
US$ 400
Data Centric Deep Learning
A trope we often hear is that data is at the heart of deep learning. But how exactly do you manage, improve, and repair deep learning models with data? This course teaches students applied techniques to measure and improve data quality, deep learning infrastructure to do continuous testing and deployment, iterative annotation pipelines, and techniques to respond to distribution shift and adversarial examples. Projects will be grounded in practical challenges spanning computer vision and natural language processing.
Affiliation logo
Link to an instructor's LinkedIn profile
Link to an instructor's Twitter profile
Instructor profile photo
Andrew Maas
Senior Manager at Apple and Instructor at Stanford
Affiliation logo
Link to an instructor's LinkedIn profile
Instructor profile photo
Mike Wu
PhD Scholar at Stanford
Limited Enrollment
Open for registration
Start Date
November 14
Price
US$ 400
Data Engineering with Dagster
A well built data platform allows for fast iteration and safe deployments. This course will teach you how to design, build, and maintain a data platform that supports a wide range of data tasks.You will start by taking a simple data workflow, common to most companies, and deconstructing it to its core components. By the end of the course, you will reimplement the pipeline using modern data frameworks, running in the cloud.
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Dennis Hume
Staff Data Engineer at Dutchie
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Limited Enrollment
Open for registration
Start Date
August 15
Price
US$ 400
Intro to SQL
This course provides an introduction to SQL, a programming language that will unleash your ability to explore data. We'll cover all the fundamentals of SQL, and you'll leave knowing how to issue SQL queries and interact with databases, as well as how to translate English queries to SQL correctly and quickly. We'll approach SQL in a hands-on manner with real-life examples and we'll build complexity in our SQL queries week over week.
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Srinivasan (Sesh) Seshadri
former Engineering Leader at Google, Target, Bell Labs
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Limited Enrollment
Open for registration
Start Date
July 25
Price
US$ 400
Advanced SQL
Over these four weeks, you’ll learn the advanced SQL skills necessary to dive into real datasets with confidence. We’ll clean up messy and nested data, learn about EAV schemas and pivots, and dive deep into advanced window functions. We’ll also cover performance, advanced joins, and other complex SQL patterns to optimize queries.
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Izzy Miller
Advocate at Hex, former Google and Looker
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Python for Data Science
Build projects like computing your own heart rate from an ECG, building a Shazam music identifier clone, and more while learning how to use core python libraries like Numpy, Scipy, Scikit, and Matplotlib.
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Sridevi Pudipeddi
Lecturer at UC Berkeley
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Data Science Experimental Methods
Learn how to conduct experiments and run A/B tests to effectively measure the impact of product launches and support effective decision making. We'll cover topics like multivariate testing, p-value, and how to design experiments correctly for different scenarios.
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Mona Khalil
Data Science Manager at Greenhouse Software
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Accelerate your career growth by learning alongside professional peers
Share
Share projects and get real-time, actionable feedback.
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Build relationships with your cohort that last long beyond the course.
Explore
See how your peers work and expand the way you think about complex projects.
Michael GaglianoAnalytics Engineer, Collectors Universe

Shoutout to the course team for helping me (and others) keep pushing along to make sure we get through the finish line, even when things are hard. I can't tell you how much I appreciate the accountability and extra help!

Mike WilliamsProgram Manager, Salesforce

I love it - just enough content and guidance that can be covered between work, kids, and life but still learn and be challenged

Brad SilfanFinance/Data Systems, Wonder

Completing this course has given me the foundation and confidence needed to make the full jump from Finance to Analytics Engineering. I've always felt like I needed a "credential" to make the full leap. Having gone through the course and learning with this community has made me feel like less of an "imposter" and like I have the skills/knowledge to do this as my profession (or at least know where to go to find the answers!). Really appreciative of the entire staff, TAs, cohort, pod, etc.

Sara GasparSenior BI Analyst, Lyst

This course has been an amazing learning experience - no only because of the content of the course but also the way in which is taught! It has the perfect mix of online - individual work - community time and resources. The teaching materials are very high quality and the whole team are super attentive and responsive.

It's a great way to learn hands-on experience with dbt, get exposed to real life problems, industry experts and also networking! I highly recommend it!

Mer ManahanData Analyst

I would recommend this to anyone looking to get their feet wet in dbt that needs a structured course and accountability to learn a new tool/skill

Erik JakubowskiSoftware Engineer, Financial Tech

Absolutely stoked to share this! Over the last month I had the pleasure of learning from some of the great minds coming out of Facebook, Google, and other enterprises doing bleeding edge work in Machine Learning and Natural Language Processing. Being part of a select cohort spanning three continents I was able to hear about initiatives and research from AI/ML professionals around the globe. NLP is a fast emerging sector that is changing the landscape of software everywhere you look. I'm so honored to be part of this experience and am taking away so many great connections and insights in a field that I really love.

Jason EllicotData Scientist III, J.B. Hunt

The co:rise NLP course has been the most efficient way I've ever learned new skills. Having access to talented instructors and peers made the learning process both quicker and richer.

Yudhiesh RavindranathData Scientist, MoneyLion

I got to learn and directly interact with world class practitioners of NLP which no other course in the market offers.

Julie KalliniSoftware Engineer, Facebook

I really enjoyed the course and the community around it. I think it was a great way to not only learn about NLP, but also connect with other engineers in the field.

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