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cohort8_structure

Introduction

For the past 7 cohorts (since January 6th 2018), we have offered free machine learning classes to more than 1000 Nigerians and we are excited to continue our long-standing tradition. This document will serve as a guideline for our students, as well as other communities looking to replicate the AI Saturdays Lagos model ❤️. Each cohort has a different flavor, so do check out our Cohort 7 guideline as well.

What should I expect?

  • 17 weeks of Main class and 1 week break 😎
  • 10 practicals
  • 1 Main project
  • Exercises to put your knowledge to test
  • 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 8 Youtube Playlist

[2] Cohort 8 Course Materials

It truly takes a village

We are extremely grateful to our selfless volunteers - class and practical instructors, lab instructors, 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 ✨.

If you wish to support our work and the initiatives we undertake, we kindly invite you to consider making a donation on our Patreon page. Your generosity will play a crucial role in furthering our mission and helping us make a positive impact in the African AI community.

Certificate Requirements

  • You are expected to have about 60% or more participation in class. Participation will be monitored by taking attendance
  • You are expected to have about 60% or more participation in Labs. Participation will be monitored by taking attendance
  • You are expected to have atleast 30% in assignments
  • You are expected to have a 100% participation in final Project

Curriculum

We will primarily be using the three fantastic courses listed below as a source of reference. 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

Lectures

Week Date Topic Resources Instructors Topics Covered
0 05-Aug Python Refresher Notebook, Youtube Steven Kolawole Hello, Python; Functions and Getting Help; Booleans and Conditionals; Lists; Loops and List Comprehensions; Sorts; Strings and Dictionaries; Working with External Libraries; Mathematics with Python
1 12-Aug Numerical Computing with Python and Numpy Notebook, Youtube Khadija Iddrisu Going from Python lists to Numpy arrays; Multi-dimensional Numpy arrays and their benefits; Arrray operations, broadcasting, indexing, and slicing; Working with CSV data files using Numpy; Working with pandas and sklearn
2 19-Aug Introduction to Data Science Slide, Youtube Emefa Duah What is data science; What is not data science; Data Science vs Machine Learning; (A few) data science examples; The skillset of data scientists; Data science pipeline
3 26-Aug Data Collection and Scraping Slide, Youtube Akintayo Jabar Data collection process; Common data formats and handling; Regular expression and parsing
4 02-Sep Relational Data Slide, Youtube Akintayo Jabar Overview of relational data; Entity relationships; Pandas and SQLite; Joins; SQLite examples; DB joins
5 09-Sep Visualization and Data Exploration Slide, Youtube Aseda Addai-Deseh Basics of visualization; Data types and visualization types; Software plotting libraries
6 16-Sep Linear Algebra Slide, Notebook, Youtube Kenechi Dukor Matrices and vectors; Basics of linear algebra; Solving linear equations; Libraries for matrices and vectors; Sparse matrices
7 23-Sep Free Text and Natural Language Processing Slide, Youtube Wuraola Oyewusi Free text in data science; Bag of words and TFIDF; Tokenization; Embedding representation; Language models and N-gramsp Example motivation: ChatGPT
8 30-Sep Introduction to Machine Learning Slide, Youtube Allen Akinkunle Least squares regression: a simple example; Machine learning notation; Linear regression revisited; Matrix / vector notation and analytic solutions; Finding good parameters; The gradient descent algorithm; Implementing linear regression
9 07-Oct Linear Classification Slide, Youtube Olumide Okubadejo Example motivation; Classification in machine learning; Example classification algorithms: Logistic regression and Support vector machines; Libraries for machine learning
10 14-Oct **No Lecture and No Lab**
11 21-Oct Nonlinear Modeling, Cross-Validation Slide, Youtube Tejumade Afonja Example motivation; Overfitting, generalization, and cross validation; Regularization; General nonlinear features; Kernels; Nonlinear classification
12 28-Oct Basics of Probability Slide, Youtube Emefa Duah Probability in data science; Basic rules of probability; Some common distributions; Example application
13 04-Nov Maximum Likelihood Estimation, Naive bayes Slide, Youtube Tejumade Afonja Maximum likelihood estimation; Naive Bayes; Spam classification with Naive Bayes
14 11-Nov Unsupervised Learning Slide, Youtube Deborah Kanubala Unsupervised learning; K-means; Principal Component Analysis
15 18-Nov Decision Trees, Interpretable Models Slide, Youtube Oluwatoyin Yetunde Sanni Decision Trees; Training (classification) decision trees; Interpretating predictions; Boosting; Examples
16 25-Nov Recommendation Systems Slide, Youtube Foutse Yuehgoh Recommendation systems; Collaborative filtering; User-user and item-item approaches; Matrix factorization; Examples
17 2-Dec Introduction to Deep Learning Slide, Youtube Femi Ogunbode Recent history in machine learning; Machine learning with neural networks; Training neural networks; Specialized neural network architectures; Deep learning in data science; Brief overview of popular deep learning-based generative models
18 9-Dec **No Lecture and No Lab**
19 16-Dec Project Presentations

Labs

Week Date Topic Resources Tutor
0 05-Aug **No Lab**
1 12-Aug **No Lab**
2 19-Aug Introduction to Git and Github Slide, Youtube Sandra Oriji
3 26-Aug Data Collection and Scraping Notebook, Youtube Ejiro Onose
4 02-Sept Relational Data and SQL Notebook, Youtube Afolabi Animashaun
5 09-Sept Lab Postponed to Next Week
6 16-Sept Data exploration and visualization Notebook, Youtube Oluwaseun Ajayi
7 23-Sept Text Processing Notebook, Youtube Fortune Adekogbe
8 30-Sept **No Lab**
9 7-Oct Linear Regression and Classification Notebook, Youtube Lawrence Francis
10 14-Oct **No Lecture and No Lab**
11 21-Oct Non-linear Modeling Notebook, Youtube Tejumade Afonja
12 28-Oct **No Lab**
13 04-Nov **No Lab**
14 11-Nov Unsupervised Learning Notebook, Youtube Joscha Cüppers
15 18-Nov **No Lab**
16 25-Nov Recommendation Systems Notebook, **Lab Cancelled due to technical difficulties** Ejiro Onose
17 2-Dec Neural Networks Notebook, Youtube Funmito Adeyemi
18 9-Dec **No Lecture and No Lab**
19 16-Dec Project Presentations

Assignments

N/A Release Date Week-Topic Links Deadline
1 03-Sept 03-Data Collection and Scraping Assignment, Submission 9th September 2023
2 11-Sept 04-Relational Data and SQL Assignment, Submission 17th September 2023
3 27-Sept 05-Data Exploration and Visualization Assignment, Submission 8th October 2023
4 15-Oct 07-Free Text and NLP Assignment, Submission 29th October 2023
5 25-Nov 11-Nonlinear Modeling Assignment, Submission 10th December 2023

Project and Mentorship

As a prerequisite for successfully concluding this cohort, participants are presented with two structural options based on prefrence. They may choose to engage in a collaborative group project or undertake an individual project. The primary objective of this initiative is to afford participants the opportunity to apply their theoretical knowledge gained throughout the cohort in a practical setting. Throughout the project engagement, participants will receive guidance from industry experts in Artificial Intelligence. These mentors will play a pivotal role in providing support and direction, ensuring the successful completion of the projects. Notably, there are currently 15 groups, each named after prominent African world leaders, symbolizing their significant contributions and advancements. The Solo-Ransome-Kuti group members are embarking on individually-led projects.

Project Timelime

Project Proposal Deadline: October 15, 2023

Project Submission Deadline: December 10, 2023

Presentation Day: December 16, 2023

Team Name Project Members Resources Mentor
Solo-Ransome-Kuti-Ladipo NSL-2-Audio: Nigerian Sign Language to Audio Ipadeola Ladipo Github Tejumade Afonja
Solo-Ransome-Kuti-Oyeneye Adaptive Melodies: A User-Shift Preference Music Recommendation System Samuel Oyeneye Github Tejumade Afonja
Solo-Ransome-Kuti-Adai Predicting Credit Card Approvals Christopher Adai Github Afolabi Animashaun
Sankara MovieSense: Movie Recommender System Olugbade Ifeoluwa, Ogbobe Charles, Ehimwenman Edemakhiota, -Moshood Sanusi, -Muhammad Yahya Github David Onyeali
Johnson-Sirleaf Machine Learning Approach to Predicting Diabetes Risk Buraimoh Glory, Dolamu Oludare, Chinedu Oguazu, Usman Daudu, -Oluwafemi Akinode Github Olawale Abimbola
Maathai Customer Churn Prediction Jack Oraro, Oyelayo Seye, Adenike Ayodeji, Ayooluwa Jesuniyi, -Elijah Mesagan, -Lucky Nkwocha Github Foutse Yuehgoh
Nkrumah Sentiment Analysis on Social Media Samuel Ekuma,Rapheal Alemoh, Chinelo Okafor, -Chisom Nenna, -Oluwafunto Daramola Github Olumide Okubadejo
Mandela Air Quality Monitoring and Anomaly Detection System Peter Agida, Damilola Akin-Adamu, Oluwapolore Oyeniji, Abdul-lateef Asafa, Yetunde Afolabi, -Folashade Akintola Github Joscha Cüppers
Ahmed-Ibrahim Building Prices Prediction Model Peter Oni, Daniel Eze, Oluwapelumi Olaniyi, Joanna Yadeka, Elisha Babalola Github Emefa Duah
Kapwepwe Analysing Reviews of Hotels and Restaurants in Nigeria to Determine Customer Sentiment Daniel Otulagun, Olatunde Ogunboyejo, Sarah Akinkunmi, -Iyanu Gbiri, -Olushola Yusuf Github Orevaoghene Ahia
Anomah-Ngu Handwritten Digits Recognition Model Mofiyinfoluwa Aladesuyi, Jedaiah Akimsah, Taiwo Olorunnishola, Victor Bassey, Mukhtar Abdulquadir Github Akintayo Jabar
Bourguiba Implementation of Medical FAQ Chatbot Monsurat Ariyo, Ayodeji Akande, Caleb Balogun, Martins Joseph, Bala Mairiga Abduljalil, -Waris Akorede Github Fortune Adekogbe
Elnadi Flood Chat Etietop Udofia, -Basil Makama, -Okosa Uche, -Akorede Salaam, -Joshua Michael, -Okosa Uche Github Sandra Onyinyechi
Selassie Diabetic Risk Prediction using Random Forest and Logistic Regression Ebunoluwa Amoo, Ayodeji Adesegun, Funbi Bolarinwa, Abraham Ugwa, Oyindamola Olatunji Github Oluwaseun Ajayi
Lumumba - Ayorinde Alase, Gamaliel Okudo, Nurudeen Alase, Precious Ita, Chitom Uzokwe Github Oluwafemi Azeez
Nyerere - Temitope Ajibade, Kelvin Obi, Oluwabukola Ogunbunmi, Muhammad Gimba, Eniola Adetunji Github Femi Ogunbode
Machel - Olorundara Akojede, Tope Rufai, Anuoluwapo Ogunrinde, Jamiu Ahmed, Bilikis Olanrewaju Github Kenechi Dukor

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