ML 4 PMs
Learn how to make an impact as a Product Manager in the domain of ML in 10 week's course
A program built for experienced Product Mangers who are thrilled to level up as ML Product Managers
About the course
A 10 weeks course built for experienced Product Managers who are thrilled to level up as ML Product Managers
Who is it for?
Experienced product managers who have at least 4 years of practical experience as product managers, or equivalent experience.
What will you learn?
- Basic ML fundamentals to have a basic understanding of the ML process.
- The theory needed to be understood in order to work along with data analysts and researchers.
- The theory will be followed by weekly hands-on practices with our amazing teaching assistants.
- The soft skills that will help you to be involved in the ML domain, work with your allies, setting business goals and lead your teams to make an impact.
Things you should know
Starting date 18.11.2022, every Friday between 9:00-12:00.
The course will be held in Hebrew.
This is an online program using zoom.
90% of the proceeds are donated to nonprofit organizations
The other 10% helps us expand our activity
Got Questions? We've Got Answers
The course begins on 18.11.22
Every Friday between 9:00-12:00, the students will learn ML
Students will also practice what they learned with mentors in small groups and work on a final project to experience product work and feedback.
Overall, the course is about 70 hours total.
- Experienced product managers who have at least 4 years of practical experience as product managers, or equivalent experience.
- Initial application – Once registration opens, you will have 48 hours to submit your application.
Please note this doesn’t guarantee your place in the course, as we need to make sure applicants meet our minimum requirements for the course (you can see them under ‘Course Requirements’).
- Application Review – We go over all applications and make sure they meet our course requirements.
- Donate and secure your place – Once you receive an email from us, you will have 48 hours to complete the registration and secure your spot in the course. The ‘cost’ of the course is a donation with a set range of ₪2500 – ₪5000. You will be able to choose how much you’d like to donate.
Should you not complete your registration within 48 hours, we will automatically move to the next applicant on the list to secure their place, regardless of when they applied to the course.
- Your donation – You can see the various charities we donate to in the ‘Who we donate to’ section of this page.
90% of your donation will be used to help these charities exist and support those in need, and 10% will go to Give and Tech’s operational costs.
- Participate in at least 80% of the lectures and practice sessions
- Be able to work in a group and present your work.
- 10 Lectures by industry leading product managers
- 10 Practice sessions in small groups led by a dedicated senior product manager
- LinkedIn graduation certificate to include in your CV
The association’s approach is that the donation is not a payment for the course (it is a donation and the course is a by-product), so there is no direct relationship between the number of sessions the student has had and one refund or another.
If we think the course is not for you after one or two meetings – we will return the full amount and check if it is possible to put someone else in your place.
We wish to give a fair chance to everyone, therefore the participation will be chosen randomly from the applications pool.
All course lectures, practice sessions and panels will be held in Hebrew only.
The sessions will be recorded and available for you to watch offline.
Our Lecturers
Lior Kornblum
Tech Entrepreneur | Specializing in AI | Turning ideas into E2E leading products ⭐ | Innovation leader | Startups strategic advisor and mentor | Building
Ofer Egozi
VP Product at Aspectiva – a Walmart company
Inbal Ben Yehuda
AI Product Manager at Gong
Eilat Lev-Ari
Product Director
Avi Luski
Head of Data Products /Process Mining/ Fintech/Life long Learner/Mentor/Data Science/SAP GURU/Fraud Detection/Audit-Tech
Paz Aviv
Product Lead at Riskified
Nir Solomon
Product @ Meta
Ran Romano
Co-Founder, VP of Engineering at Qwak
Shira Navot
Computer Vision & Deep Learning Algorithm Engineer at UVeye | Community Leader | Mentor
Nitzan Gado
Senior Data Scientist at Intuit
Adam Fox 
NLP & Algorithms Team Leader at Aspectiva – a Walmart company
Victoriya Kalmanovich
Backend & Data Engineering Manager at Walmart-Aspectiva | Podcaster @Barvazgumi
Tal Shahar
Founder & CEO at Stealth Startup + Principal at Deep Insight
Inbal Gilead
Data and AI Product Leader Certificate
Erez Shilon
Group Product Manager at Gong
Anna Feldman
Senior Product Manager, Data & ML
Shelly Shmurack
Data PM | Product Management Podcaster, Speaker & Mentor
ML Course Mentors
Shelly Shmurack
Data PM | Product Management Podcaster, Speaker & Mentor
Tomer Simon
Head of Product, Algo at Via
Shir Yarkoni
Product Manager at Taboola
Michal Kaufmann
Product Management AI Centric Products
Gal Orian
Senior Product Manager at Windward
Jonathan Bhonker
Product Dolphin | Incurable Scientist | Career Switcher
Or Hay
Senior Product Manager at Microsoft Industry AI
Hadas Sheinfeld
Product Strategist
Course Staff
Keren Katz
Product lead @ Sygnia
Erez Shilon
Group Product Manager at Gong
Tom Dekel
ML Product Manager & ML Team Lead @ Imubit
Assaf Katz
Customer Success Manager at Colleen.AI
Tomer Bashiri
Product Manager | Computer Science (Hebrew University) and Photography (Bezalel)
Course Syllabus
In the course of 10 sessions, we’ll review the basics concepts of machine learning, and how they interact with the world of product management. We will discuss product management processes and methodologies and how we can use them to build ML products.
a. What is Give & Tech?
b. Get to know the non profits
c. Intro to ML for PMs – an overview of this course.
a. Structured Vs. Unstructured, Supervised Learning (Regression, Classification, Time Series) Vs. Unsupervised, Active learn method, reinforcement learning
b. DIfferent Model types (Trees, xgboost etc.)
c. Unstructured Intro to NLP
a. CRISP DM process
b. Model Evaluation Metrics (MEA, RMSE.. Vs. Recall, Precision..)
c. Test train validation: split, cross validation, optimization cycles, sampling (and sampling methods), overfitting
a. How to define the problem space for ML products?
b. Why do DS projects fail? (Start from a rule based engine → when to evolve to ML)
c. When should & shouldn’t we use ML?
a. Collection, Preparation.. Working with Data Analysts, Maintain Model (Retrain) + use cases (PM, Data Analyst, Data Scientists),
b. ML & Data infra & platform i. (ETLs, Big Data, ML Ops)
a. Explainability: How can product managers harness this as a cool user facing feature to increase prediction adoptions and trust
b. Bias & Ethics: Our role in preventing real life biases
a. ML Product Manager organization role (ML product as a platform)