The Machine Learning (ML) Flipped Cohort is a structured, community-driven Data Science and Machine Learning learning 15 weeks cohort designed for beginners. The goal is to equip individuals with foundational to intermediate ML knowledge using a flipped classroom model where learners independently consume pre-recorded content during the week, then attend a weekly community to discuss, explore and ask questions about what they’ve learned.
We follow a flipped classroom model where:
- Participants watch curated pre-recorded lectures and complete labs during the week.
- Every Saturday, attend a community call to engage with one of the organizers.
Each week you will be:
- Assigned selected videos (from a curated playlist of lectures and labs)
- Receive supporting materials like Jupyter notebooks, slides, and assessments
- Join a live Zoom session on weekends to engage with instructors and peers
- Interact daily on Discord for Q&A, collaboration, and accountability
By the end of the cohort, you will:
- Participate in capstone projects and present your solution to demonstrate real-world understanding
- Earn a certificate if all conditions are met (see below)
This cohort is ideal for:
- Students and recent graduates exploring data science or ML
- Career switchers with programming experience aiming to enter ML roles
- Self-learners seeking structure, mentorship, and a community
- You!
Prerequisite: Basic Python knowledge is expected
We’ll provide beginner-friendly Python resources during Week 1 for anyone needing a refresher.
The cohort will run for 15 weeks, broken down into:
- 10 weeks of structured learning
- 5 weeks of working on capstone projects
Important Dates
- Cohort Start Date: July 26, 2025
- Cohort End Date: November 1, 2025
| Tool | Purpose | Link |
|---|---|---|
| GitHub | All materials, assignments, and resources | Cohort Repository |
| Gmail Group | Announcements & Notifications | AI6 Lagos Group |
| Zoom | Weekly community sessions & project demos | Link shared weekly |
| Discord | Daily interaction, Q&A, accountability & support | Join Discord |
| YouTube | Pre-recorded lectures & community session recordings | Pre-recorded Lectures & Lab, C9 - Weekly Community Sessions |
Each week will follow this schedule:
- Sundays: Email regarding the videos, labs, notebooks and slides for the week will be sent to participants
- Saturdays: Complete and Submit Assessments 9:00 AM WAT on Saturdays
- Saturdays: Attend a 2-hour community discussion via Zoom (10-12 PM WAT)
There will be an onboarding session on July 26th, at 10:00 AM WAT.
| Week | Dates | Topics | Lectures | Labs | Assessment | Suggested Weekly Schedule |
|---|---|---|---|---|---|---|
| 0 | Jul 26 | Onboarding & Kickoff | - | - | - | - |
| 1 | Jul 27 – Aug 2 | Python & Numerical Computing | ☘️Python Refresher: Lecture Video , Lecture Notebook ☘️Numerical Computing with Python and Numpy: Lecture Video, Lecture Notebook |
- | Link | Mon: Python Refresher Lecture Wed: NumPy Lecture |
| 2 | Aug 3 – Aug 9 | Data Science Foundations | ☘️Introduction to Data Science: Lecture Video, Lecture Slides ☘️Data Collection and Scraping: Lecture Video, Lecture Slides |
🍒Introduction to Git and Github: Lab Video, Lab Slides 🍒Data Collection and Scraping: Lab Video, Lab Notebook |
link | Mon: Intro to DS Lecture Tue: Intro + Git/GitHub Lab Wed: Data Collection Lecture Thur: Data Collection Lab |
| 3 | Aug 10 – Aug 16 | Databases, SQL & Exploratory Data Analysis | ☘️Relational Data: Lecture Video, Lecture Slides ☘️ Visualization and Data Exploration: Lecture Video, Lecture Slides |
🍒Relational Data and SQL: Lab Video, Lab Notebook 🍒Data Exploration and Visualization: Lab Video, Lab Notebook |
Link | Mon: Relational data Lecture Tue: Relational data Lab Wed: Data Exploration Lecture Thur: Data Exploration Lab |
| 4 | Aug 17 – Aug 23 | Math for ML | ☘️Linear Algebra: Lecture Video, Lecture Notebook, Lecture Slides | - | TBD | Mon: Linear Algebra Lecture Wed: Linear Algebra Notebook |
| 5 | Aug 24 – Aug 30 | Text Processing | ☘️ Free Text and Natural Language Processing: Lecture Video, Lecture Slides | 🍒Text Processing: Lab Video, Lab Notebook | Mon: Free Text & NLP Lecture Wed: Text Processing Lab |
|
| Capstone Project Proposal Submission | ||||||
| 6 | Aug 31 – Sep 6 | Linear Regression & Classification Models | ☘️Introduction to Machine Learning & Linear Regression: Lecture Video, Lecture Slides ☘️Linear Classification: Lecture Video, Lecture Slides |
🍒Linear Regression and Classification: Lab Video, Lab Notebook | TBD | Mon: Introduction to ML Lecture Wed: Linear Classification Lecture Thur Linear Regression & Classification Lab |
| 7 | Sep 7 – Sep 13 | Non-Linear Modeling & Interpretable ML | ☘️Nonlinear Modeling, Cross-Validation: Lecture Video, Lecture Slides ☘️Decision Trees, Interpretable Models: Lecture Video, Lecture Slides |
🍒Nonlinear Modeling: Lab Video, Lab Notebook | TBD | Mon: Nonlinear Modeling Lecture Tue: Nonlinear Modeling Lab Wed: Decision Trees Lecture |
| 8 | Sep 14 – Sep 20 | Probabilistic Models | ☘️Basics of Probability: Lecture Video, Lecture Slides ☘️Maximum Likelihood Estimation, Naive bayes: Lecture Video, Lecture Slides |
- | TBD | Mon: Basics of Probability Lecture Wed: MLE, Naive Bayes Lecture |
| 9 | Sep 21 – Sep 27 | Unsupervised Learning & Recommendation Systems | ☘️Unsupervised Learning: Lecture Video, Lecture Slides ☘️Recommendation Systems: Lecture Video, Lecture Slides |
🍒Unsupervised Learning: Lab Video, Lab Notebook 🍒Recommendation Systems: Lab Notebook |
TBD | Mon: Unsupervised Learning Lecture Tue: Unsupervised Learning Lab Wed: Recommendation Systems Lecture Thur: Recommendation Systems Lab |
| 10 | Sep 28 – Oct 4 | Deep Learning Basics | ☘️Introduction to Deep Learning: Lecture Video, Lecture Slides | 🍒Neural Networks: Lab Video, Lab Notebook | TBD | Mon: Deep Learning Lecture Wed: Neural Network Lab |
| Capstone Project Begins | ||||||
| 10 | Sep 29 – Oct 3 | Capstone Project Team Check-in & Project Proposal Feedback | ||||
| 11 | Oct 11 | Review of past lectures | - | - | - | - |
| 12 | Oct 13 - 17 | Capstone Project Team Check-in | - | - | - | - |
| 12 | Oct 18 | Break. Enjoy DataFestAfrica2025 | - | - | - | - |
| 13 | Oct 20 - 24 | Capstone Project Team Check-in | - | - | - | - |
| 13 | Oct 25 | Mock Project Presentations | - | - | - | - |
| 14 | Nov 1 | Project Presentations | - | - | - | - |
- Submitted via Google Forms
- Deadline: 1 hour before the community call on Saturdays
- Reviewed live during the discussion
To receive a Certificate of Completion:
- 60% minimum attendance at community calls (tracked via Google Forms)
- 40% average assessment score
- 100% participation in the final project (submission required)
Each cohort participant is required to form a team of 2–5 members to collaboratively work on a capstone project. Teams must submit a project proposal for review and approval by the cohort coordinators.
Projects should align with topics covered in the cohort, and each proposal must include the following sections:
- Problem statement or motivation
- Existing solutions
- Project objectives
- Proposed dataset and justification
- Proposed methodology
- Modeling, Evaluation & Deployment plan
- Expected outcomes
- Community impact
- Team members
- Acknowledgement & References
Below is the list of approved Cohort 9 (C9) Capstone Projects:
| Team name | Project title | Project repository | Project proposal |
|---|---|---|---|
| BenOkri | Predicting Customer Churn in Nigeria's Telecom Industry: A machine learning approach with MTN data | Link | Link |
| Armah | Research Paper Clustering System for Topic Discovery and Literature Organization | Link | Link |
| SeedGuard | SeedGuard AI: Mapping the Genetically Modified Organism (GMO) Narrative Landscape for True Agricultural Empowerment Sovereignty | Link | Link |
| TrulyFit | Building a Rich Fitness Dataset and Recommendation System using Machine Learning | Link | Link |
| Nwapa | Predicting Solar Energy Efficiency of Buildings in Lagos | Link | Link |
| SDML | Spam Detection in Emails using NLP | Link | Link |
| Adichie | Flood Area Extent Prediction in Ibadan Metropolis using GIS and Machine Learning Models | Link | Link |
| TwinPillars | Predicting Cholera Risk in Kenya and Nigeria: A data-driven approach for preventive public health | Link | Link |
| Gordimer | Nutritional Value Estimator for Nigerian Foods | Link | Link |
| Solo-Alie | Customer Segmentation with RFM & Unsupervised Learning | Link | Link |
- Kenechi Dukor
- Oluwafemi Azeez
- Tejumade Afonja
You are encouraged to explore the following:
- ML Zoomcamp – DataTalksClub
- CMU Data Science Course
- Stanford ML Course – Andrew Ng
- Machine Learning @ VU Amsterdam
This cohort is built on the foundation laid by the incredible work from Cohort 8 (C8) — its lectures, labs, and community contributions. We are deeply grateful to the selfless volunteers who made it all possible: class instructors, lab facilitators, mentors, and countless others who gave their time and expertise.
Our community is fortunate to be supported by such a generous, talented, and inspiring group of individuals. Thank you for your continued impact.
- Afolabi Animashaun
- Akintayo Jabar
- Allen Akinkunle
- Aseda Addai-Deseh
- Deborah Kanubala
- Ejiro Onose
- Emefa Duah
- Femi Ogunbode
- Fortune Adekogbe
- Foutse Yuehgoh
- Funmito Adeyemi
- Joscha Cüppers
- Khadija Iddrisu
- Kenechi Dukor
- Lawrence Francis
- Olumide Okubadejo
- Oluwaseun Ajayi
- Oluwatoyin Yetunde Sanni
- Sandra Oriji
- Steven Kolawole
- Tejumade Afonja
- Wuraola Oyewusi
This effort is brought to you by our amazing team of volunteers — thank you for your time, dedication, and leadership.
- Jesuyanmife Egbewale (lead)
- Tejumade Afonja (co-lead)
Supports:
- Adetola Adetunji
- Ibrahim Gana
- Sharon Alawode
- Simon Ubi
