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Machine Learning with Python - 6-week tutor-led online course

Date:

17/03/2021 - 21/04/2021

Organised by:

Mind Project Ltd

Presenter:

Simon Walkowiak MSc, MBPsS

Level:

Intermediate (some prior knowledge)

Contact:

Mind Project Ltd
Simon Walkowiak MBPsS
Phone: 02033223786
Email: info@mindproject.co.uk

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Venue: Online

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, Decision Trees and ensemble algorithms e.g. Random Forests, Adaptive Boosting or Extra Gradient Boosting approaches 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.

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. Please note this training course doesn’t include Neural Networks and Deep Learning approaches.

 

2. Course programme.

This instructor-led course duration is planned over 6 teaching weeks.

In between the six weekly online live tutorials (2.5 hours long each) you will improve your skills by watching pre-recorded instruction videos via our Mind Project Learning Platform and working through set tasks (e.g. quizzes) as well as homework coding exercises which will require 4-6 hours of your time commitment per week (24-36 hours). We estimate that the total time commitment is 40-50 hours over 6 teaching weeks.

Start date: Wednesday, 17th of March 2021 @10:00 am London (UK) time

Schedule of sessions: Every Wednesday at 10:00 am London (UK) time for 6 weeks

Deadline for registrations: Monday, 15th of March 2021 @ 17:00 London (UK) time

 

Week 1: Introduction to Machine Learning with Python

  • Concepts, terminology and context: unsupervised vs. supervised vs. semi-supervised approaches,
  • Overview of methods and applications,
  • Preparing data for Machine Learning tasks: revision of probability distributions, data normalisation and standardisation techniques, feature engineering, dealing with missing values,
  • Dimensionality reduction with Singular Value Decomposition, Principal Component Analysis and Factor Analysis.

Week 2: Unsupervised learning with clustering approaches

  • K-means and k-medians clustering,
  • Hierarchical clustering,
  • Evaluating clustering solutions, describing clusters and estimating cluster profiles,
  • Overview of other important clustering methods: mean-shift, DBSCAN, Gaussian mixtures.

Week 3: Predicting continuous data with linear and non-linear models

  • Multiple linear regression and selecting suitable predictors with stepwise regression,
  • Ridge and lasso regularisation,
  • Regression metrics for model evaluation, comparing models,
  • Polynomial regression, splines and generalised additive models (GAMs).

Week 4: Binary and multinomial classification - part 1: methods, evaluation metrics, model selection

  • Introduction to classification with logistic regression - understanding probabilities and log-odds,
  • Model selection and classification metrics: sensitivity, specificity, F score, Kappa, log-loss, R-squared etc.,
  • Linear and quadratic discriminant analysis,
  • Cross-validation and bootstrapping.

Week 5: Binary and multinomial classification - part 2: overview of other important approaches

  • Stochastic Gradient Descent classifier,
  • Distance-based classification: k-Nearest Neighbours algorithm,
  • Probabilistic Naive Bayes classifier and kernel-based Support Vector Machines,
  • Semi-automated and automated tuning of classification models.

Week 6: From decision trees to ensembles

  • Classification and Regression Trees (CART),
  • Estimating variable importance, bagging and boosting,
  • Tree-based Random Forests ensemble,
  • Extra Gradient Boosting (XGBoost) algorithm.

 

3. Course pre-requisites and further instructions

  • We recommend that all attendees have the most recent version of Anaconda Individual Edition of Python 3.8 (or at least Python 3.5) installed on their PCs (any operating system). Anaconda’s Python is a free and fully-supported distribution and you can download it directly from https://www.anaconda.com/products/individual#Downloads. Please contact us should you have any questions or issues with the installation process. A short list of additional Python libraries to pre-install before the course will be sent to the enrolled attendees in the Welcome Pack alongside other Joining Instructions.
  • We recommend that the attendees have practical experience in data processing or quantitative research – gathered from either professional work or university education/research. A good knowledge of statistics would be beneficial. We suggest that the course is preceded with our “Python for Data Analysis” open-to-public tutor-led online training course.
  • Your PC needs to be connected to a stable WiFi/Internet network (either home or office-based) and have Zoom video-conferencing application installed.
  • You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) to access our Mind Project Learning Platform.

 

4. Your course instructor.

Your instructor for this course will be Simon Walkowiak. Simon is a director at Mind Project Limited and a Ph.D. researcher in Artificial Intelligence at the Bartlett Centre for Advanced Spatial Analysis (University College London) and the Alan Turing Institute in London. Simon holds BSc (First Class Honours) in Psychology with Neuroscience and MSc (Distinction) in Big Data Science. He conducts and manages research projects on implementation and computational optimisation of novel AI approaches applicable to large-scale datasets to predict human behaviour and spatial cognition. Simon is the author of “Big Data Analytics with R” (2016) – a widely used textbook on high-performance computing with R language and its compatibility with ecosystem of Big Data tools e.g. SQL/NoSQL databases, Spark, Hadoop etc. Apart from research and data management consultancy, during the past several years, Simon has taught at more than 150 in-house or open-to-public statistical training courses in the UK, Europe, Asia and USA. His major clients include organisations from finance and banking (HSBC, RBS, GE Capital, European Central Bank, Credit Suisse etc.), research and academia (GSMA, CERN, UK Data Archive, Agri-Food Biosciences Institute, Newcastle University etc.), health (NHS), and government (Home Office, Ministry of Justice, Government Actuary’s Department etc.).

 

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-tutor-led-online-course-mar21/.

Cost:

By 21st of February 2021 (Early Bird offer):
£345 (normally £420) per person for the whole course (regular fee).
£210 (normally £270) per person for the whole course applicable to undergraduate and postgraduate students, representatives of registered charitable organisations and NHS employees only (discounted fee).
Additional discounts available for multiple bookings and groups.

Website and registration:

Region:

Greater London

Keywords:

Regression Methods, Ordinary least squares (OLS), Generalized liner model (GLM), Linear regression, Logistic regression, Discrete choice/count models, Forecasting, Data Mining, Neural networks, Machine learning, Python

Related publications and presentations:

Regression Methods
Ordinary least squares (OLS)
Generalized liner model (GLM)
Linear regression
Logistic regression
Discrete choice/count models
Forecasting
Data Mining
Neural networks
Machine learning
Python

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