WorkShop: An Introduction to Data Science and Machine Learning using Python

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, and is largely synonymous to data mining and big data. Machine learning (ML), a sub-field of Artificial Intelligence, is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. This workshop aims to walk you through the basics of machine learning and data science, taking you to the point where you can start building and applying your own machine learning models using Python’s scikit-learn library. We will try to limit any mathematics to high-school level.
Who is this presentation for?
Anyone interested in learning the basics of machine learning.
Prerequisite knowledge:
This workshop assumes a basic knowledge of the Python programming language. If you don't have experience with Python you are still welcome to attend. We will try to keep a balance between theory, code and application.
What you'll learn?
Overview of machine learning Introduction to the Python toolset Machine learning process and best practice Basic machine learning models (regression, classification, non-supervised) Further explorations in ML

Paul has over 20 years experience leading businesses, managing teams and consulting on enterprise systems to medium and large firms. After being exposed to the promise of data science through Andrew Ng's machine learning course, he set out to learn all he could about the fields of artificial intelligence, machine learning and data science. The fields he has been working with include robotics, machine vision, recommender systems, predictive analytics, natural language processing and Bayesian data analysis. Paul is currently working on an investment management system for value-based asset managers in the finance sector.