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Neema’s Blog

Posts - Page 4 of 5

Mandela Week Sensitization Event

  • ~1 min read

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

  • ~1 min read

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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Python for Data Science

  • ~1 min read

Python is a powerful, flexible, open source and widely-used programming language with many applications. I had an opportunity to facilitate a workshop on python4Data science at Arusha Technical College (ATC), Arusha -Tanzania. The workshop aimed to explores Python’s place in the scientific ecosystem, and how the language, with several readily-available open-source libraries, can serve as a powerful tool for data science.

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Naives Bayes Classifier

  • 7 min read

Introduction

Naive Bayes is a family of simple probabilistic classifier based on applying Bayes theorem with strong (naive) independence assumptions between the feauture. Naive Bayes classifier follows under classification in supervised learning task for modeling and predicting categorical variables. It is a very simple algorithm based around conditional probability and counting.

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