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Neema’s Blog

Posts - Page 3 of 4

Nyeri was so great

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In June 2018, I attended a summer school and workshop which was organised by Data Science Africa with the aim of training participants in Machine Learning and Data Science methods and provide an avenue for researchers to present their work and demonstrate the application of these techniques to problems relevant to African context. The event was “Training of trainers” under the theme “end-to-end data science”, hosted at Didan Kimath University of Technology in Nyeri, Kenya. The speakers and instructors were from Africa and across the globe. The summer school was a great success, with informative and interesting programmes. I enjoyed both in class and field works activities. I also had a chance to present my research progress, I was very delighted with the feedbacks and comments given by the audience.

The next summer school will be held in Abuja, Nigeria in November, 2018. I encourage others to apply for this wonderful opportunity.

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Mandela Week Sensitization Event

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During Mandela week at The Nelson Mandela African Institution of Science and Technology (NM-AIST), I had an opportunity to inspire young girls to pursue science, engineering, technology and innovation. The event aimed at encouraging exploration with technology, promote self-confidence and support aspiration to technical careers. The audience of the event was girls from different secondary schools at Arusha-Tanzania. The event explored various challenges that affect girl students to achieve their dreams and how to overcome those challenges.

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Practical Approaches on using Scikit-learn for Machine Learning Project

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As part of pythontz event at The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha-Tanzania, I had an opportunity to present a talk on practical approach on using scikit-learn for Machine learning project. The talk aimed at giving an accessible introduction on how to use machine learning techniques using scikit-learn. Scikit-learn is a Python open source library designed to tackle machine learning problems from beginning to end. It provides efficient versions of a large number of common machine learning algorithms.

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