Training

Wild Tree Tech offer in-house training for teams. These courses will bring your staff up to speed with modern machine-learning, AI and data engineering techniques. Courses are custom made for each audience, below an example agenda.

Machine-learning Starterkit

A two day course for data scientists, data analysts, and business intelligence experts interested in using Python for their day-to-day machine-learning work. The primary focus is on learning to use supervised machine-learning techniques efficiently and effectively.

1
Duration: 60 minutes

1 — Getting Started

Let’s start at the beginning. We examine different types of machine-learning problems and what the available tools are. If you can not measure how well your algorithm works you can’t improve it. We will look at cross-validation, performance metrics and common mistakes. If you can not measure how well your algorithm works you can’t improve it. We will look at cross-validation, performance metrics and common mistakes.

2
Duration: 120 minutes

2 — Measuring Performance

If you can not measure how well your algorithm works you can’t improve it. We will look at cross-validation, performance metrics and common mistakes. If you can not measure how well your algorithm works you can’t improve it. We will look at cross-validation, performance metrics and common mistakes. If you can not measure how well your algorithm works you can’t improve it. We will look at cross-validation, performance metrics and common mistakes.

3
Duration: 180 minutes

3 — Random Forests and gradient boosting

For real world, business data it is hard to beat random forest or gradient boosted tree models. They regularly take top spot in Kaggle competitions. In this module we will dive into tree based models. Their strengths, weaknesses and some tricks of the trade to make them work really well.

4
Duration: 180 minutes

4 — Neural networks and deep learning

Neural networks are the foundation of the current deep learning and AI resurgence. They rule supreme when it comes to image classification and object detection. In addition most state of the art models for sound and natural language understanding are based on neural networks. We will venture into the field of deep learning and its applications to image classification, object detection and image segmentation.

5
Duration: 180 minutes

5 — Interpretable decisions and debugging

Most machine-learning are seen as black-box decision makers. In large part because the tools and techniques for looking inside are not as well known as they should be. In this module we will learn about techniques like LIME and partial dependence plots. We will apply them to neural networks and tree based ensembles to debug our models and generate explanations for customers.

6
Duration: 15 minutes

6 — Closing

Recap of what you learned and pointers to resources to keep learning. Stay afterwards for an informal chat and networking.

Skill up!