TIME ZONE – UK local time (GMT+0) – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
Course Overview:
Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in machine learning and for deep learning. In this two day course, we provide an introduction to machine learning and deep learning using Python. We begin by providing an overview of the machine learning and deep learning landscape, and discuss the prominent role that Python has come to play in this area. We then turn to machine learning in practice, and for this, we will primarily using the widely used and acclaimed scikit-learn toolbox. We begin with binary and multiclass classification problems, then look at decision trees and random forests, then look at unsupervised learning methods, all of which are major topics in machine learning. We then cover artificial neural networks and deep learning. For this, we will using the PyTorch deep learning toolbox. Here, we will cover the relatively easy to understand multilayer perceptron and then turn to convolutional neural networks.
Email oliverhooker@prstatistics.com with any questions
Machine Learning and Deep Learning using Python (PYML02) This course will be delivered live
https://www.prstatistics.com/course/machine-learning-and-deep-learning-using-python-pyml02/
12 May 2021 - 13 May 2021
TIME ZONE – UK local time (GMT+0) – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
Course Overview:
Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in machine learning and for deep learning. In this two day course, we provide an introduction to machine learning and deep learning using Python. We begin by providing an overview of the machine learning and deep learning landscape, and discuss the prominent role that Python has come to play in this area. We then turn to machine learning in practice, and for this, we will primarily using the widely used and acclaimed scikit-learn toolbox. We begin with binary and multiclass classification problems, then look at decision trees and random forests, then look at unsupervised learning methods, all of which are major topics in machine learning. We then cover artificial neural networks and deep learning. For this, we will using the PyTorch deep learning toolbox. Here, we will cover the relatively easy to understand multilayer perceptron and then turn to convolutional neural networks.
Email oliverhooker@prstatistics.com with any questions
Upcoming courses
Introduction to Python and Programming in Python (PYIN02)
28 April 2021 - 29 April 2021
https://www.prstatistics.com/course/introduction-to-python-and-programming-in-python-pyin02/
Introduction to Scientific, Numerical, and Data Analysis Programming
in Python (PYSC02)
5 May 2021 - 6 May 2021
https://www.prstatistics.com/course/introduction-to-scientific-numerical-and-data-analysis-programming-in-python-pysc02/
Machine Learning and Deep Learning using Python (PYML02)
12 May 2021 - 13 May 2021
https://www.prstatistics.com/course/machine-learning-and-deep-learning-using-python-pyml02/
Species distribution modelling with Bayesian statistics in R (SDMB02)
17 May - 21 May 2021
https://www.prstatistics.com/course/species-distribution-modelling-with-bayesian-statistics-in-r-sdmb02/
Introduction to spatial analysis of ecological data using R (ISPE04)
18 May - 28 May 2021
https://www.prstatistics.com/course/introduction-to-spatial-analysis-of-ecological-data-using-r-ispe04/
Introduction/Fundamentals of Bayesian Data Analysis statistics using R (FBDA01)
19 May 2021 - 20 May 2021
https://www.prstatistics.com/course/fundamentals-of-bayesian-data-analysis-statistics-using-r-fbda01/
Landscape genetic data analysis using R (LNDG04)
24 May - 28 May 2021
https://www.prstatistics.com/course/landscape-genetic-data-analysis-using-r-lndg04/
Bayesian Approaches to Regression and Mixed Effects Models using R and
brms (BARM01)
26 May 2021 - 27 May 2021
https://www.prstatistics.com/course/bayesian-approaches-to-regression-and-mixed-effects-models-using-r-and-brms-barm01/
Introduction to Bayesian modelling with INLA (BMIN02)
31 May 2021 - 4 June 2021
https://www.prstatistics.com/course/introduction-to-bayesian-modelling-with-inla-bmin02/
Introduction to Stan for Bayesian Data Analysis (ISBD01)
2 June 2021 - 3 June 2021
https://www.prstatistics.com/course/introduction-to-stan-for-bayesian-data-analysis-isbd01/
Introduction to Machine Learning and Deep Learning using R (IMDL01)
9 June 2021 - 10 June 2021
https://www.prstatistics.com/course/introduction-to-machine-learning-and-deep-learning-using-r-imdl01/
Meta-analysis in ecology, evolution and environmental sciences (METR02)
28 June 2021 - 2 July 2021
https://www.prstatistics.com/course/meta-analysis-in-ecology-evolution-and-environmental-sciences-metr02/
Introduction to Multi’omics Data Analysis from Microbial Communities (MOMC01)
3 August 2021 - 5 August 2021
https://www.prstatistics.com/course/introduction-to-multiomics-data-analysis-from-microbial-communities-momc01/
Missing Data Analytics (MDAR01) This course will be delivered live
8th September 2021 - 10th September 2021
https://www.prstatistics.com/course/online-course-missing-data-analytics-mdar01-this-course-will-be-delivered-live/