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cohort7_structure

Introduction

For the past 6 cohorts (since January 6th 2018), we have offered free machine learning classes to more than 600 Nigerians and we are excited to continue our long-standing tradition. However, in this cohort, we will be introducing our students to Machine Learning operations (MLOPS) tools in the hope of better preparing them for their future work in the field Machine Learning. This document will serve as a guideline for our students, as well as other communities looking to replicate the AI Saturdays Lagos model ❤️

What should I expect?

  • 15 weeks of Main class and 1 week break 😎
  • 10 practicals with MLOPS
  • 1 Main project
  • Certificate of participation if all the requirements are fulfiled (more details below)

How can I access the classes?

Our classes are streamed weekly and can be accessed online, at anytime - for free!

[1] Cohort 7 Classes - Youtube Playlist

[2] Cohort 7 Practicals - Youtube Playlist

Where are the class materials?

We draw on a wealth of knowledge available for free online and have linked to these excellent resources in the curriculum units below. We also provide a companion Github repository of slides and notebooks. Our practical classes are hands-on, introducing students to various MLOPs tools.

[1] Cohort 7 Classes - Github Repository

[2] Cohort 7 Practicals - Github Repository

[3] Cohort 7 Projects - Github Repository

It truly takes a village

We are extremely grateful to our selfless volunteers - class and practical instructors, teaching assistants, mentors, and many more. Our community is truly fortunate to have such an amazing, talented, kind, and incredible group of people❤️.

Are you interested in joining our next cohort? Please follow us on our various socials media platforms to keep in touch ✨.

Cohort Structure

Subgroups

Team Lead: Teams leads are software engineers or who have some degree of software engineering skills that can be leveraged across all members of their team. The function of the team lead would be to teach the students peripheral software skills like using Github, command line, debugging, etc. The team leads will report to the team mentor.

Team Scrum Master: The function of the scrum master is to lead team meetings and help keep meeting times.

Team PM: The team project manager helps monitor and assign team's task on notion.

Team Ninjas The team ninjas (preferably a backend and frontend developers) would help the team with any frontend and backend development tasks for the team's project.

Team Secretary: The secretary helps the team to monitor some key metrics such as attendance turnout rate, and help prepare team monthly presentations.

Team Responsibilities

Weekly Meetings: Each team is encouraged to have a weekly meeting for the purpose of working through the task or learning peripheral software skills like how to use Github, command line, etc, together.

Practical Tasks: Each team is expected to work through practical tasks together. Tasks would be shared through a notion template documents which can be duplicated by different teams.

Montly Meetings with Team Leads: Each team mentor will facilitate and meet monthly with the team leads to discuss progress and get update on the team statistics.

Team Github Repository: Each team is expected to create a Github repository for their team under AI Saturdays Lagos Github organization, where each member would complete and contribute to the tasks.

Certificate Requirements

  • You are expected to have about 60% or more participation in class. Participation will be monitored by taking attendance (team secretary's duty)
  • You are expected to have about 80% participation in Practicals. Participation is monitored by inspecting commits to the tasks repo and team weekly meeting attendance.
  • You are expected to have a 100% participation in final Project. Participation is monitored by inspecting commits to the project repository.

Curriculum

We will primarily be using the three fantastic courses listed below. However, each volunteer course instructor has full autonomy in choosing which materials to use to teach their classes.

  1. CMU Data Science Course
  2. Machine Learning @ VU University Amsterdam
  3. Stanford Machine Learning

You can access all our classes and materials via these links

Week Date Topic Resources Instructors
1 4-Sep Introduction to Data Science Slide, Note Tejumade Afonja
2 11-Sep Data Collection and Scraping Slide, Note Akintayo Jabar
3 18-Sep Relational Data Slide, Note Akintayo Jabar
4 25-Sep Visualization and Data Exploration Slide, Note Ahmed Olanrewaju
5 2-Oct Matrices, Vectors, and Linear Algebra Slide, Note Lawrence Francis
6 9-Oct Data Preprocessing Slide, Videos Esemeje Omole
7 16-Oct Introduction to Machine Learning Slide, Note Olumide Okubadejo
8 23-Oct Linear Models Slide, Note, Playlist Stanley Dukor
9 30-Oct Break
10 6-Nov Model Evaluation Slide, Playlist Femi Ogunbode
11 13-Nov Nonlinear Models Slide, Note Kenechi Dukor
12 20-Nov Basic Probability Slide, Note Tejumade Afonja
13 27-Nov Probabilistic Models Slide, Note, Playlist George Igwegbe
14 4-Dec Tree Models Slide, Playlist Azeez Oluwafemi
15 11-Dec Unsupervised Learning Slide, Note Olumide Okubadejo
16 8-Jan Recommenders Systems Slide, Note Farouq Oyebiyi
17 29-Jan Project Presentation

Practicals

The practicals will touch on different MLOPS and will be held alongside classes each week.

Week Date Topic TAs
1 4-Sep Data Science Notebook Frameworks Ojeifo Ozeigbe
2 11-Sep Data Science Notebook Frameworks: Recap Azeez Oluwafemi, Akintunde Oladipo and Tejumade Afonja
3 18-Sep A web scraping task with basic intro first Akintunde Oladipo
4 25-Sep Break (IndabaXNigeria Conference)
5 2-Oct Data Labelling Tools and Frameworks Tejumade Afonja
6 9-Oct Intro to Pandas Gideon Onyewuenyi, Sandra Oriji
7 16-Oct Industrial Strength Visualization libraries Sharon Ibejih
9 6-Nov Intro to Sklearn Chizurum Olorondu
10 30-Oct Outlier and Anomaly Detection Kawthar Babatunde
11 13-Nov Model and Data Versioning Akintunde Oladipo
12 20-Nov Feature Engineering Automation Oluwafemi Azeez
13 27-Nov Model Serving and Monitoring Tejumade Afonja