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 “Machine Learning with Python” training course, you will learn to utilise the most cutting edge Python libraries for clustering, customer segmentation, predictive analytics and machine learning on the real-world data.

The course explores practical applications of the most frequently used machine learning approaches such a Multiple Linear, Polynomial (Non-Linear) and Logistic Regressions, k-Means and Hierarchical Clustering, k-Nearest Neighbours, Naive Bayes and Decision Trees algorithms using Python’s major scientific libraries such as NumPy, pandas, SciPy as well as more specialised, statistical and machine learning oriented packages e.g. scikit-learn, statsmodels, and h2o. It also provides a good introduction to more advanced techniques e.g. Adaptive Boosting and Random Forests.

Apart from this, you will learn to evaluate the predictive models based on the obtained metrics such as sensitivity, specificity, F-score, Kappa etc., and optimise the accuracy and efficiency of these models using various methods of cross-validation, grid-search and performance boosting.

The course is suitable for data scientists, researchers, data analysts, developers and engineers, who are currently using Python language (preferably at intermediate level) and would like to expand their skills to include machine learning and predictive analytics toolkit.

Please note this training course doesn’t include Neural Networks and Deep Learning approaches – our “Deep Learning and AI with Python” course is specifically designed to cover these methods in detail.

2. Programme.

The course will run for three days (Monday to Wednesday) 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, business and finance 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.

The programme for this course covers the following concepts and topics:

• Predicting continuous target variables with different regression analysis techniques including multiple linear regressions, stepwise regressions, Lasso/Ridge regularised regressions, non-linear (polynomial) regressions and methods of their evaluation and optimisation,
• Understanding density functions and OLS normality assumption: screening for outliers, testing for normality (QQ-plots, histograms, Shapiro-Wilk and Kolmogorov-Smirnov tests), continuous data normalisation techniques, testing for multi-collinearity (creating correlation matrices, heatmaps etc.),
• Fitting polynomial regressions and regularisation approaches for polynomials (Lasso, Ridge, Elastic Net), searching for optimal lambda hyperparameter, overfitting vs underfitting,
• Applying k-means and hierarchical clustering algorithms for feature selection, dimensionality reduction and customer segmentation purposes,
• Implementing hierarchical clustering algorithm using different distance calculations and various linkage solutions; visualising clusters and understanding dendrograms, extracting segments and estimating cluster profiles,
• Implementing selected classification algorithms e.g. logistic regression and Naïve Bayes for binary and multinomial classification tasks,
• Choosing “best” models depending on obtained classification metrics e.g. confusion matrix, sensitivity, specificity, F score, Kappa statistic, logarithmic loss, R-squared, mean absolute error, root mean squared error, Gini score, area under ROC curve etc.,
• Feature engineering, cross-validation and grid-search methods for classification purposes,
• Applying more advanced classification and predictive analytics algorithms e.g. decision trees and their ensembles e.g. random forests and adaptive boosting in more complex machine learning applications.

3. What is included?

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

• printed course pack with all presentation slides, cheatsheets and other essential course information,

• digital (USB memory stick) Course Manual including all presentation slides, Python course code scripts (Jupyter notebooks) and a list of reference books and online resources,

• additional home exercises and all data sets available to download,

• stimulating, friendly and inclusive learning environment in a small group (typically 10-14 attendees) led by experienced and energetic tutors and course leaders,

• modern and comfortable training venue located in the heart of City of London – at the London Institute of Banking & Finance, next to the Monument underground station,

• refreshments and a light, energising lunch on each day of the course,

• Wi-Fi access,

• networking opportunity,

• Mind Project course attendance certificate.

4. Further instructions.

• In order to benefit from the course, we recommended that all 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.

• This course is targeted at Python users with some Python coding experience (preferably at Intermediate level) and interest in Machine Learning algorithms. Our open-to-public “Python for Data Analysis” training course (details at https://www.mindproject.io/product/python-for-data-analysis-london-june-2019/) is a good pre-requisite to participate in this course.

• The deadline for registrations on this training course is Thursday, 4th of July 2019 at 16:00 London (UK) time. 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-july-2019/.

Cost:

£825 per person for the whole course (regular fee).
£675 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 delegates, please contact us directly.

Website and registration:

https://www.mindproject.io/product/machine-learning-with-python-london-july-2019/

Region:

Greater London

Keywords:

Statistical Theory and Methods of Inference, Probability theory , Bayesian methods, Regression Methods, Ordinary least squares (OLS), Generalized liner model (GLM), Linear regression, Logistic regression, Forecasting, Data Mining, Machine learning, Quantitative Software, Python

Related publications and presentations:

Statistical Theory and Methods of Inference
Probability theory
Bayesian methods
Regression Methods
Ordinary least squares (OLS)
Generalized liner model (GLM)
Linear regression
Logistic regression
Forecasting
Data Mining
Machine learning
Quantitative Software
Python

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