Machine Learning with Python - London - 3-Day Training Course

Description:

1. Course description.

Python has become a powerful language of data science and is now commonly used as the leading programming language for predictive analytics and artificial intelligence. During this hands-on 3-day “Machine Learning with Python” training course, the attendees will learn to utilise Python’s libraries for predictive analytics on the real-world data. The course will explore practical applications of major scientific libraries such as NumPy, pandas, SciPy and matplotlib, as well as more specialised, machine learning oriented SciKit-Learn, Theano, TensorFlow, Keras and H2O for Python.

The course will provide theoretical and practical understanding of major machine learning techniques such as:

• multiple linear regressions (including ridge and Lasso) and Generalized Linear Models e.g. binomial and multinomial logistic regressions as well as Poisson regressions,

• classification methods e.g. naive Bayes, k-nearest neighbours, decision trees, random forests, support vector machines,

• clustering and dimensionality reduction methods: k-means and principal component analysis,

• introduction to neural networks and deep learning.

The structure of the course will include short theoretical lectures introducing each of the above machine learning methods and practical tutorials presenting applications of these techniques using Python language. Apart from the machine learning methods, the attendees will also learn other concepts associated with predictive analytics and machine learning:

• feature extraction and engineering,
• normalisation and standardisation methods,
• model optimisation through parameter grid search,
• model validation and accuracy metrics including confusion matrix, precision, recall, F1 score, ROC, log-loss, Gini, MSE, RMSE, R-squared etc.,
• selected approaches for machine learning with Big Data using Python and its libraries.

The course will utilise Python 3.x (Anaconda distribution), with additional libraries e.g. SciKit-Learn, Theano, Keras, H2O, TensorFlow etc. The full list of packages will be confirmed with the attendees before the course.
During the tutorial on multi-core and GPU-accelerated machine learning in Python with H2O and TensorFlow, the attendees will be also provided with access to Mind Project computing cluster.

2. Programme.

The course will run for three days (Tuesday to Thursday) between 9:30am and ~5:00pm and will consist of alternating lecture-style presentations and practical tutorials. The example datasets used during tutorial sessions will come from social sciences, psychology and business fields, however the contents may vary depending on specific interests of participants (based on the Participant’s Skills Inventory). There will be two 15-minute coffee/tea breaks and one 1-hour lunch break on each day of the course.

3. What is included?

Apart from the contents of the course, Mind Project will provide the participants with the following:

• a digital (USB memory stick) Course Manual including all presentation slides, Python course script files and a list of reference books and online resources,
• additional home exercises and all data sets available to download,
• Wi-Fi access,
• Central London location - a 1-min walk from the Barbican station, 5 minutes away from Farringdon and St. Paul’s stations, 15 minutes from the Liverpool Street Station,
• networking opportunity,
• Mind Project course attendance certificate.

4. Further instructions.

• In order to benefit from the contents of the course, we recommend that attendees have the most recent version of Anaconda distribution of Python (by Continuum Analytics) installed on their laptops (any operating system). As Anaconda’s Python is a free and fully-supported distribution you can download it directly from https://www.continuum.io/downloads. Please contact us should you have any questions or issues with the installation process. The enrolled attendees will receive a list of additional Python libraries to install before the course. Please note that this course will utilise Python 3.6.
• This course is targeted at users with some Python experience (preferably at Intermediate level) and interest in Machine Learning algorithms. Our “Data Analysis in Python” training course (https://www.mindproject.io/product/data-analysis-in-python-london-march-2018/) is a good pre-requisite to participate in this course.
• Participants are encouraged to complete the online Participant's Skills Inventory at https://www.mindproject.io/participants-skills-inventory/ to allow Mind Project and our course tutors to customise the contents of the course depending on the level of participants' knowledge and their areas of interest. The data obtained through the Participant's Skills Inventory will be held fully-confidential and will only be used to provide a quality data analysis training.
• By purchasing a place on one of our courses you agree to the Terms and Conditions. Please read the Terms and Conditions available at https://www.mindproject.io/services/data-science-training/training-tcs/ before making a booking.
• The deadline for registrations on this training course is Friday, 23rd of March 2018 at 16:00 London (UK) time, however Mind Project reserves the right to end the registration process earlier if all places are booked before the deadline.

Should you have any questions please contact Mind Project Ltd at info@mindproject.co.uk or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/machine-learning-with-python-london-march-2018/

Cost:

£630 per person for the whole course (regular fee).
£450 per person for the whole course for UK registered undergraduate and postgraduate students, and representatives of registered charitable organisations (discounted fee).
For group bookings of 4 and more participants, please contact us directly.

Website and registration:

https://www.mindproject.io/product/machine-learning-with-python-london-march-2018/

Region:

Greater London

Keywords:

Statistical Theory and Methods of Inference, Bayesian methods, Regression Methods, Ordinary least squares (OLS), Generalized liner model (GLM), ANOVA, Linear regression, Logistic regression, Data Mining, Neural networks, Machine learning, Quantitative Approaches (other), Quantitative Software, Python

Related publications and presentations:

Statistical Theory and Methods of Inference
Bayesian methods
Regression Methods
Ordinary least squares (OLS)
Generalized liner model (GLM)
ANOVA
Linear regression
Logistic regression
Data Mining
Neural networks
Machine learning
Quantitative Approaches (other)
Quantitative Software
Python

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