For a more detailed inforation on the specifics of this training cohort, see this post on our blog
Week | Date | Week Subject | Deliverabe(s) / Assignment | Guest Speakers/Facilitator |
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1 | March 7th | Get Started Thinking Like an Engineer and Approaches to Problem-Solving in Artificial Intelligence. |
Leaners will be teamed in groups to use case-studies to design products. Learners will be expected to write a community-helpful blog post on “Problem-Solving and Design Thinking in Machine Learning and Artificial Intelligence.” |
Tamuntotonye, Richard. UX Design Mentor, Udacity. |
2 | March 14th | Data Vectorization and Visualization with NumPy and Matplotlib (Series 1) | ||
3 | March 21st | Data Vectorization and Visualization with NumPy and Matplotlib (Series 2). | ||
4 | March 28th | Exploratory Data Analysis and Basic Data Preprocessing with Pandas. |
Akinsande Olalekan Technical Delivery Lead/Data Scientist at Data Science Nigeria (DSN). |
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5 | April 3rd & 4th | Statistics and Probability for Data Science and Machine Learning. | Learners with their peers will collaboratively attempt to solve a fictitious business problem, based on a case-study, with concepts from Statistics, Probability and Exploratory Data Analysis using associated Python libraries. |
Jennifer Oghenetejiri Ebe Data Scientist at Interswitch |
Week | Date | Week Subject | Deliverabe(s) / Assignment | Guest Speakers/Facilitator |
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1 | April 24th | Hands-on Introduction to Machine Learning. | Groups will make a presentation on problem analysis in Rivers State and how ML can contribute to the solutions. |
Dr Sebastian Raschka Dr Sebastian Raschka
Assistant Professor of Statistics at the University of Wisconsin-Madison.
Former YouTube Product Manager. |
2 | April 25th | Full Machine Learning Project Lifecycle. | Groups will simulate conducting a Machine Learning project analysis for one of the problems stated in the previous project. |
Google Developer Expert, Machine Learning. |
3 | May 2nd | Regression: Learning and Practicing Regression in Machine Learning (Series 1) | Regression project will be tailored to problems analyzed in our local society and will be announced to students. |
Data Scientist at OneFi |
4 | May 8th & 9th | Regression: Learning and Practicing Regression in Machine Learning (Series 2) | ||
5 | May 16th | Learn and Use Software Version Control (Git) for Your Machine Learning Projects | Students will apply their knowledge of SVC to their previous project, building their portfolio, as well as create a GitHub page for their works. |
Technical Lead, Data Science at MOBicure |
6 | May 23rd | Classification: Use Machine Learning Classifiers to Solve Problems (Series 1) | ||
7 | May 29th & 30th | Classification: Use Machine Learning Classifiers to Solve Problems (Series 2) | Students will work on a classification project tailored to problems analyzed in their local society. |
Grad student, African Masters in Machine Intelligence. |
8 | June 6th | Improve Your Models with Data Preprocessing and Feature Engineering (Full-dive) | Project on data preprocessing and feature engineering will be disclosed to students |
Data/Research Scientist, Data Science Nigeria. |
9 | June 13th | Model Deployment Techniques I (Series 1). |
Chief Data Scientist at Versus |
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10 | June 19th & 20th | Model Deployment Techniques I (Series 2) | Groups will deploy one of their existing projects to a public server that can be accessed by anyone anywhere. | |
11 | June 27th | Running ML Services in Containers | Groups will package an existing Machine Learning application into a container and push to Docker Hub for another group to pull down to their machine and use. |
DevOps Engineer at Peak Capacity |
12 | July 4th | Explore Other Machine Learning Techniques and Algorithms (Series 1). |
Machine Learning Engineer, NIIT. |
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13 | July 11th | Explore Other Machine Learning Techniques and Algorithms (Series 2). | Groups will attempt to apply each of these algorithms to a problem and dataset of their choice and present their findings to the stakeholders and as a written blog post as well. |
Machine Learning Engineer, NIIT. |
14 | July 18th | Hands-on Workshop on AI Accountability I (Series 1). | Groups will create a presentation on how customer data can be managed and kept private in a typical machine learning project, using their designed ethical framework. | |
15 | July 25th | Hands-on Workshop on AI Accountability I (Series 2). | Groups will use probing and analysis tools to interpret model decisions. |
Assistant Professor at New York University. |
16 | Aug 1st | Learn and Practice Dimensionality Reduction and Unsupervised Learning (Series 1) |
Author of the best-selling unsupervised learning book on Amazon. |
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17 | Aug 7th & 8th | Learn and Practice Dimensionality Reduction and Unsupervised Learning (Series 2) | Groups will simulate solving one of the most disturbing problems (chosen by instructors) in the local society with supervised learning. | |
18 | Aug 15th | Workshop on Cloud Machine Learning and Data Science I | Groups will serve an existing Machine Learning model to an application with an online prediction on Google Cloud Platform. |
Developer Advocate, Google Cloud Platform (GCP).
Cloud Architect, Lynn Langit Consulting Google Cloud Developers Co-Lead Port Harcourt |
19 | Aug 17th - 22nd | Project Week!!! | Capstone Projects and Proposed Projects Week: Deliver Group-Led Working ML Software fast. |
Graduate Student at New York University (NYU) Center for Data Science. |
20 | Aug 28th & 29th | Machine Learning Project Leadership: Apply Agile Methods in Machine Learning Project Management and Tips for Leading Machine Learning Teams. | Groups will work temporarily with a startup or business solving the problem(s) in Port Harcourt to initiate Machine Learning projects in a division or incorporate Machine Learning and Data technologies into their products or services. |
Machine learning & AI analyst, advisor & podcaster www.twimlai.com Managing Partner, Doug Enterprises, LLC CEO, EventPady |