When:
Monday, April 22, 2024
1:00 PM - 2:00 PM CT
Where: Online
Contact:
Leticia Vega
Group: Northwestern IT Research Computing and Data Services
Category: Training
Scikit-Learn is one of the major libraries for machine learning in Python. This series comprises four workshops designed to give you a map of Scikit-Learn’s different functionalities and place you on firm ground to start using it for your machine-learning projects.
Part 1 - The Data Science Pipeline
This workshop introduces the fundamentals of machine learning (ML): How does ML differ from statistics? Which types of problems are best suited for ML? We will explore different real-world problems and learn to distinguish between supervised and unsupervised learning. You will become familiar with the main stages of the data science pipeline: data wrangling, cleaning and pre-processing, modeling, optimization and model validation, and post-processing and visualization.
Prerequisites: Basic familiarity with Python is required. Familiarity with NumPy is highly recommended. No previous machine learning or statistics experience is necessary, but it will be helpful.
When:
Monday, April 29, 2024
1:00 PM - 2:00 PM CT
Where: Online
Contact:
Leticia Vega
Group: Northwestern IT Research Computing and Data Services
Category: Training
Scikit-Learn is one of the major libraries for machine learning in Python. This series comprises four workshops designed to give you a map of Scikit-Learn’s different functionalities and place you on firm ground to start using it for your machine-learning projects.
Part 2 - Supervised Learning – Regression
Regression analysis is a powerful tool for uncovering the associations between features of your data (known as independent variables) and dependent variables (usually denoted by Y). In this workshop, you will learn to identify ML tasks suited for regression analysis, appropriately process independent variables, train and evaluate models, and generate predictions. We will also discuss some common pitfalls and assumptions of the chosen modeling techniques.
Prerequisites: Basic familiarity with Python is required. Familiarity with NumPy is highly recommended. No previous machine learning or statistics experience is necessary, but it will be helpful.
When:
Monday, May 6, 2024
1:00 PM - 2:00 PM CT
Where: Online
Contact:
Leticia Vega
Group: Northwestern IT Research Computing and Data Services
Category: Training
Scikit-Learn is one of the major libraries for machine learning in Python. This series comprises four workshops designed to give you a map of Scikit-Learn’s different functionalities and place you on firm ground to start using it for your machine-learning projects.
Part 3 - Supervised Learning – Classification
Classification is the problem of identifying which class or category (label) an observation (features) belongs to within a pre-defined set of categories. In this workshop, you will learn to identify classification problems, prepare the features and label data for modeling, train and evaluate models, and generate predictions. We will also discuss some common pitfalls and assumptions of the chosen modeling techniques.
Prerequisites: Basic familiarity with Python is required. Familiarity with NumPy is highly recommended. No previous machine learning or statistics experience is necessary, but it will be helpful.
When:
Monday, May 13, 2024
1:00 PM - 2:00 PM CT
Where: Online
Contact:
Leticia Vega
Group: Northwestern IT Research Computing and Data Services
Category: Training
Scikit-Learn is one of the major libraries for machine learning in Python. This series comprises four workshops designed to give you a map of Scikit-Learn’s different functionalities and place you on firm ground to start using it for your machine-learning projects.
Part 4 - Unsupervised Learning and Beyond
Unsupervised learning uses machine learning to analyze unlabeled datasets without human supervision. Several real-world problems require discovering hidden patterns in data. In this workshop, you will learn about different unsupervised learning methods, such as dimensionality reduction and clustering, and how to process your data to apply these algorithms. We will also discuss other machine learning methods and future steps.
Prerequisites: Basic familiarity with Python is required. Familiarity with NumPy is highly recommended. No previous machine learning or statistics experience is necessary, but it will be helpful.