Introduction To Machine Learning With Python

Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars.

In this series of workshops, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries.

We will explain the fundamentals of Machine Learning and provide a comprehensive introduction to Linear Regression and Model Training, Classification, and Support Vector Machine and Unsupervised models, and will use Python to apply this knowledge to real-world datasets.

After these hands-on workshop, you will have a better understanding of these Machine Learning models and techniques and be able to make better informed decisions on how to leverage Machine Learning in your research.

Recommended Participants

All researchers interested in the application of Machine Learning techniques to the research data analysis. Participants are strongly encouraged to attend all three workshops.

Workshop 1 – Introduction & Linear Regression

Learning objectives

• Understand the difference between supervised and unsupervised Machine Learning.

• Understand the fundamentals of Machine Learning.

• Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training.

• Understand the Machine Learning modelling workflows.

• Use Python and scikit-learn to process real datasets, train and apply Machine Learning models

Prerequisites

Attendees must have a good knowledge of the basic concepts and techniques of Python, including Python syntax, basic programming concepts and familiarity with Pandas and Numpy libraries – attendance at one of QCIF’s Software Carpentry “Introduction to Programming with Python” workshops is suitable background.

Workshop 2 – Classification

Learning objectives

• Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning.

• Know the differences between various core Machine Learning models.

• Understand Machine Learning modelling workflows.

• Use Python and scikit-learn to process real datasets, train and apply Machine Learning models

Prerequisites

Attendees must have a good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training, including the learning outcomes described for the Introduction & Linear Regression workshop

Workshop 3 – SVM & Unsupervised Learning

Learning objectives

• Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.

• Know the differences between various core Machine Learning models.

• Understand Machine Learning modelling workflows.

• Use Python and scikit-learn to process real datasets, train and apply Machine Learning models

Prerequisites

Attendees must have a good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training, including the learning outcomes described for both the Introduction & Linear Regression and the Classification workshops