Modern Machine Learning and Deep Learning [SOLD OUT]

NOTE: please buy the ticket for the event here https://www.eventbrite.co.uk/e/modern-machine-learning-and-deep-learning-2-day-course-tickets-49603205523?aff=ebdssbdestsearch
at least one week before the event.

DESCRIPTION
Modern Machine Learning and Deep Learning
In-class training in London.
Day 1
09:30-10:15 Session 1: Machine Learning basics
Key Machine Learning concepts and terminology

Key Deep Learning concepts

Examples in industry and research

10:15-11:15 Session 2: Formulate a typical Machine Learning problem
Formulate a typical machine learning problem and how to solve it using classical machine learning and neural networks.

11:15-11:30 Coffee break

11:30-12:15 Lab 1
How to quickly set-up an environment for both Machine Learning and Deep Learning.

12:15-13:00 Session 3 - Keras basics
Introduction to Keras and practical examples to explain all main concepts.

13:00-14:00 Lunch

14:00-14:30 Lab 2
Simple exercises to familiarize with Keras.

14:30-15:15 Lab 3
Problem to tackle: facial expression recognition

15:15-15:30 Coffee break

15:30-16:45 Session 4 - Convolutional Neural Networks (CNNs)
Understanding the convolutional architecture, convolutional and pooling layers, applications to image classification.

16:45-17:45 Lab 4
Extend the feed-forward network from Lab 2 with convolutional and pooling layers. CNNs are the most important technique for images.

Day 2
09:30-11:15 Session 5 - Deep Learning for NLP
Embeddings and how to use them in NLP, gensim, GloVe and FastText

11:15-11:30 Coffee break

11:30-13:00 Lab 5
Solving a Neural Network exercise.

13:00-14:00 Lunch

14:00-15:15 Session 6
Using pre-trained networks, transfer learning, visual debugging of DNNs

15:15-15:30 Coffee break

15:30-16:30 Lab 6
Practical lab session on the tricks learned in the previous theoretical session.

17:30-18:00 Closing remarks, feedback

Who is this course for?
Data Scientists, Analysts and Developers who are interested in Modern Machine Learning and Deep Learning.

Course requirements.
Basic Python knowledge.