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Why do students
love AI DEEP DIVE?
It's a 3-month course built to elevate your AI skills and land you a job doing your magnus opus project.
Learn in person through real conversation and interaction with your teachers.
Implement deep skills into a real proof-of-concept project.
Get career advice from your mentor, get support from our in-house placement expert.
The AI Deep Dive curriculum works in two blocks that are designed to get you job-ready
Hard skills are critical, but they also need to be deployable in order to be relevant.
Classes by experienced professionals that will get you up to speed as an AI practitioner. You'll gain a deep understanding of the essential concepts while working on exciting, real-life tasks. .
In the second block, the training wheels falls off, and you'll be working on your own project. We'll help you decide on a new application that showcases your expertise.
We'll guide you through the process to finish off with an impressive final product to show the world: your personal portfolio.
Frequently Asked Questions
What is the program cost?
The tuition fee is CAD 15,000
Do you offer to fund for fellows?
Yes, please see our Income Share agreement (ISA)
Do all graduates receive a job offer?
For DSR Berlin, 100 % of our graduates find a Job within three months of graduating. Connect to our graduates to know more; you can see the alumni (and their LinkedIn profiles) https://datascienceretreat.com/graduates/
How long will I need to stay after the program ends
It depends. You can certainly land a job by doing most interviews remotely, but the consensus seems to be that being around helps your search, because you can attend meetups, have lunch/coffee meetings with potential employers, etc. In an ideal case you should be able to stay for three months after the program ends. But plenty of people who didn’t stay got about the same results
What visa do I need to attend the program?
We suggest visiting visa as it provides 6 months staying option in Canada which can be converted to work visa upon getting a job offer letter.
How can I best prepare myself to transition to data science?
(1) Study python/engineering. We expect you to have about 1000 hrs of experience programming so that your programming skill is not a bottleneck. If during the interview we realize you don’t have enough background, we will send you back to improve yourself and interview again in a month. During that time we ask you to write something that shows you can program. It doesn’t have to be ML or data science related. It’s much better if it’s not a jupyter notebook. We want to see how you organize your code, write comments, commit messages, etc. (2) You can prepare some basic ML questions. The first few chapters of a book would be good. One that has pretty good explanations is Kuhn’s ‘advanced predictive modeling.’ Unfortunately, it’s in R, so please skip the code examples and exercises.
You will graduate the program with projects just like these.
What if you could walk into a job interview and hand them a robot that picks up cigarettes using computer vision instead of a resume?
Costs only 60 USD to produce (plus an used phone), could save 600k lives per year if deployed
Cigarette Butt Cleaner
One Cigarette butt contaminates 40 liters of water and it's physically hard to pick with a broom
Collecting roughness data with the accelerometer and camera of a phone attached to a wheelchair as ground truth.
The AI Deep Dive curriculum works in two blocks
Hard skills are critical, but they also need to be deployable in order to be relevant. chapter point The first block consists of classes by experienced professionals that will get you up to speed as an AI practitioner. You'll gain a deep understanding of the essential concepts while working on exciting, real-life tasks.
In the second block, the training wheels falls off, and you'll be working on your own project. We'll help you decide on a new application that showcases your expertise. We'll guide you through the process to finish off with an impressive final product to show the world: your personal portfolio.
Our Curriculum in Detail
Foundations of Classical Machine Learning
Estimated time: 15 hours
While deep learning has essentially become the benchmark for unstructured data, businesses still have plenty of use cases for classical machine learning. Particularly with regards to tabular data. During the AI Deep Dive interview, you demonstrated that you have a solid knowledge base, therefore we will cover classical machine learning beyond the introductory level.
Bias & variance trade-off
Norms & distances
Scaling & outliers
Support vector machines (SVMs)
Data Wrangling at Scale and Command Line
Estimated time: 40 hours
Large ever-changing datasets needed a new paradigm: we are not dealing with tables, but with streams. This is the realm of Kafka, Spark Streaming, Flink, etc. Additionally, we look at best-practive SQL, Scikit-learn pipelines, and contemprorary Geodata handling to give participants a robust foundation for data wrangling
SQL for the 21st century
Kafka, Spark Streaming
Computer vision with deep learning
Estimated time: 50 hours
We will cover a few convolutional neural network (CNN) architectures that have made a big splash in computer vision, such as Inception, VGG16, and ResNet. Most portfolio projects at AI Deep Dive use CNNs because the results are impressive, and computer vision makes for vibrant demos. The architecture we include will depend on the state of the art, which will have inevitably transformed from the time of this writing!
Fundamentals of computer vision and image processing
Object detection and image segmentation
Applications and trends in computer vision
Soft Skills, case studies, and technical communication
Estimated time: 33 hours
The world of deep learning and AI is changing rapidly and it is not enough to just have the skills and competence: you also need to know how, when, and where to use them. To maximize your career trajectory, you need to identify areas where you can leverage your unique skills to maximum effect. Part of our goal with the program is to give you hard skills, but also the soft skills necessary to navigate this climate.
Business case studies
Soft skills, interview skills, and negotiation
A Technical communication & demo day rehearsal
Natural Language Processing
Estimated time: 71 hours
This course will provide an introduction to Natural Language Processing (NLP). The focus of this course will be on the development of machine learning and deep learning models for a variety of use cases like sentiment analysis, named entity recognition (NER), clustering, recommendation, classification, translation, generation, and summarization. The participants will gain hands-on experience with professional tools and techniques, and Python libraries such as TensorFlow, NLTK, and spaCy.
How to work with text and natural language data
NLP in Python, using common libraries such as NLTK and spaCy
Assess, transform, and select the appropriate features for building machine learning and deep learning models.
Explain machine learning and deep learning concepts of an NLP ecosystem.
Creative Portfolio Project
Estimated time: 250 hours
This is where it all comes together. You will deploy all of your hard skills on a creative portfolio project under the watchful eye and helpful hand of your mentor. The goal here is to solidify your command of AI hard skills in a way that no online program can.
We will assist you in finding a project that helps you brand yourself as an AI specialist and data scientist
You will apply many of the skills that you have developed during the previous parts of the course to ensure your project is top-notch
You will work closely with your mentor over the course of the project
You will present your project to the public on "demo day"
Meet a few of our alumni
These are just a few of the hundreds of students who have graduated our program in the past six years.
Deep Learning Scientist
Before: Posdoctoral research scientist
After: Deep learning engineer
Senior Data Scientist
Before: finance professional @ Marlborough Partners and @ Houlihan Lokey
After: Senior Data Scientist
Roberto Bruno Martins
Machine Learning Specialist
Roberto Bruno Martins
Before: Business intelligence manager
After: Machine Learning Specialist
Meet Our Instructors
Our instructors are leaders in their field, comprised of principle data scientists, researchers and Kaggle champions.
Krista Bennatti-Roberts, CPA, CA
Krista Bennatti-Roberts, CPA, CA
Krista is a data scientist at Hansell McLaughlin Advisory Group, a boutique law and advisory firm specialising in corporate governance. Her role encompasses natural language processing, statistical modelling and process automation. Krista's work has been featured by the Harvard Law School Forum on Corporate Governance and Financial Regulation, The Conference Board and others.
Data Science Tooling Lead at TD Securities
Lucas got his start in finance 6 years ago, when he broke from years of study in theoretical physics to take a seat on the trading floor of a major Canadian bank. He has taken on a number of roles in the fintech space as part of the TD Technology Solutions Associate Program, with the majority of his time spent developing computational financial models with Quant teams in retail and wholesale banking, as well as implementing Big Data and Machine Learning solutions with a focus on leveraging Python to unlock analytic power for the user.
Data Scientist at Healthchain
Subash Gandyer is a seasoned AI/ML practitioner with 5+ years of experience. Currently working as a Data Scientist in a Healthcare StartUp. He taught Computer Science, Machine Learning, Deep Learning to University students in India for 9+ years. When he is not building deep models, you can see him dancing in jive and salsa parties.
Technical Consultant at Advanced Utility Systems
At present working as a Technical Consultant in Toronto based IT company where I am involved in managing multiple projects, beginning from requirement gathering to implementation. I hold a Masters degree in Computer Application. Has spent 8+ years teaching to the Master and Bachelor degree Computer Programs at University level and also worked as a corporate Trainer for Microsoft Certification courses. Coordinated more than 50 dissertation projects of final year students. I have significant experience in the field of Database (Design, Normalization, Reporting, Transact-SQL, ETL Tools, etc...). Well versed with the Software Development Life Cycle model for each project to enhance and improve clients' Customer Information System.
Data Scientist at Uber
Alex works at the cross-section of GIS, data science, and urban planning at JUMP, a fast-growing bike and scooter-sharing startup acquired by Uber one year ago. A recent graduate of DSR, Alex took part in a group project that applied computer vision to detect whether bikes are locked to street furniture. He holds a Master of Science in Geoinformatics from the University of Münster in Germany and a Master of Arts in Regional Studies of Russia, Eastern Europe, and Central Asia from Harvard University. Before delving into the field of shared mobility, Alex interned at one of Russia's leading urban planning firms - KB Strelka - where he helped develop resources for spatial information management and designed maps for MyStreet, a large scale, ongoing urban renovation project in Moscow. In addition to being an urbanist, Alex has a passion for history and has volunteered as a cartographer with Memorial, a human rights NGO that illuminates human rights violations in the past and present.
Lemurian labs, CEO
Jay is founder and CEO of Lemurian labs (and he has leadership roles in two other companies). He has done consulting for diverse companies, including SpaceX. Jay got into AI at the age of 14; received world distinctions in mathematics and physics from Cambridge in A levels. Jay is currently authoring a book - "mathematics for deep learning." Jay is a Forbes 30 Under 30 Fellow.
Jose Quesada, PhD
AI Deep Dive, CEO
Jose Quesada, PhD
Jose is the founder and CEO of AI Deep Dive (Toronto) and Data Science Retreat (Berlin). Jose Mentored and directed > 165 machine learning portfolio projects. Some resulted in startups; others ended up being non-profits with significant social impact. His goal is to demonstrate that a single person or small team can have an enormous impact thanks to open source and pre-trained models. One doesn't need to be a big corporation to solve the world's worst problems with technology.
Data scientist at Scotiabank
Pooja Bhojwani is a data scientist working with Fraud Detection group of Scotiabank, a leading commercial bank with subsidiaries in Canada and South America. In her daily job, she employs state of the art machine learning techniques and statistical analysis to protect Scotiabank and its customers against various financial threats. She completed her Msc. Computer science from university of Victoria in 2018 and has more than three years of Industrial experience.
Consultant, BI Developer at CGI
Devang Swami is a data engineering and deep learning expert. He works as a BI Developer - Consultant and helps his client build and optimize data platforms for Machine learning/AI tasks. He is proficient in a multitude of Big data platforms like Hadoop Ecosystem, Spark, Kafka, and many NoSQL databases. In his free time, he works on developing deep learning models Self-driving cars using camera and LIDAR sensors.
Senior Data Scientist at Loblaw Companies
Sabya works as a Senior Data Scientist with expertise in developing data driven solutions to drive business value. Graduated with a Masters from IIT Bombay & Ph.D Univ. of Cologne, Germany trained as a theoretical physicist remains passionate towards exploring quantitative features of large-scale data using iterative model building and data visualization via. scalable technologies . I love to help others learn, build synergistic teams to facilitate streamlined exchange of knowledge, leveraging my solid 10+ years experience across 3 continents in leading applied research teams from India/Singapore/France/Germany/Toronto within analytics domain to gain both (a) technical insights for building classifiers / decision engines towards delivering industry specific data driven solutions as well (b) develop soft-skills keeping strong focus towards inclusion/diversity values within tech teams with single goal towards facilitating brainstorming platforms to build solutions in teams.
QINGCHEN WANG, PhD.
Assistant Professor / Data Scientist
QINGCHEN WANG, PhD.
Qingchen is an award-winning data scientist with rigorous training in machine learning, artificial intelligence, statistics, and econometrics. He is one of only 87 grandmasters (as of mid-2017) on the Kaggle data science competition platform (top rank of 14th out of 52,000+ active competitors on Kaggle), and he also have significant experience in software engineering (C++, Java, Python). Currently he is working on research and development of data-driven solutions to problems in digital marketing.
After spending nearly a decade at UC Berkeley, Kelvin decided to repay his debt to the public education system by helping build UC Merced. He spent seven years teaching 4,500 students across 55 classes, while redesigning the undergraduate Computer Science curriculum. He is currently designing curricula at NVIDIA’s Deep Learning Institute (DLI) to democratize access to the latest technologies across many disciplines, industries and geographies. Kelvin helped DLI reach over 100K developers worldwide directly and in collaboration with Udacity and Coursera/Deeplearning.ai. He continues to search for ways to leverage AI to solve the Paradox of Progress.