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.
The course will run for three days (Wednesday to Friday) 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.
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 codes 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, in the heart of the City of London – a 1-min walk from the London Liverpool Street station, 5 minutes away from Moorgate tube station, 15 minutes from the Barbican and St. Paul’s tube stations,
- networking opportunity,
- Mind Project course attendance certificate.
In order to benefit from the contents of the course it is recommended 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 is a good pre-requisite to participate in this course.
Attendees are encouraged to complete the online Participant’s 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.
Deadline for registrations
The deadline for registrations on this training course is Monday, 12th of November 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.
Prices and discounts
- £575 + VAT (£690) per person for the whole course (regular fee).
- £415 + VAT (£498) 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.
Please mind that the course fee DOES NOT include the following:
- transport to and from the venue,
- accommodation and lunch.
Please contact us should you have any questions about this course. You may also want to visit the Training Courses – Frequently Asked Questions website, which gives further practical details about Mind Project training courses. You can book your place on the course by clicking Book ticket button in the top section of the course page. Please note that we accept all major credit/debit cards (through the PayPal and Stripe systems) and BACS payments. We can only confirm fully-paid bookings. Please contact us for other payment options e.g. if a Purchase Order is required. Please read Training & Events Terms & Conditions before your purchase.
The course will be held at 46 New Broad Street, London, EC2M 1JH. Please see the map below.