A Visit to a Machine Learning Workshop

Today, I had a great opportunity to attend a workshop on machine learning organized by the Intel Student Partner Program of the University of Nairobi. The speaker at the workshop was my class mate, Robert Kigen. It was a very informative workshop that introduced us to the world of machine learning via real world examples. The following is a brief summary of what I learnt from the workshop:

  • Machine learning involves the use of learning algorithms to train on data in order to give meaningful outcomes.

  • Applications of machine learning include: Fraud detection, Face detection, IoT Analysis, Natural Language Processing, Movie recommendation, Spam Filtering/Virus detection.

  • Machine learning problems are solved using classification, regression, clustering and recommendations. Classification is used when grouping of data is required. Regression involves predicting real valued output for an entity given a set of features. Clustering is an example of unsupervised learning used to group entities with similar features.

  • Main types of machine learning are supervised learning and unsupervised learning.

  • In supervised learning, right answers are used for training the model in order to make predictions.

  • Unsupervised learning identifies patterns and common trends in a set of data. Right answers are not provided.

I also got to learn about Boston Dynamics; a company that engineers and designs robots and, the Google Cloud Vision API; a REST API that employs machine learning models to analyze the content of images.

It was worth attending the workshop since it gave me a better insight on concepts of machine learning and its applications.