Machine Learning with Python - 6-week tutor-led online 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” immersive 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.

A mixture of online pre-recorded instruction videos, weekly live group webinars with our tutor, additional 1-2-1 check-ins (either via email or on Microsoft Teams) and several homework exercises throughout the duration of the course will ensure you will be able to apply Machine Learning methods using Python language to your own data and research questions.

 

2. Course structure and programme.

This instructor-led course is planned over six teaching weeks with an additional two-week follow-up period during which you will complete a small piece of work and receive a 1-2-1 feedback from our tutor. During the course, you will attend weekly live webinars (90 minutes each) with our tutors who will explain specific topics, answer your questions and discuss different statistical and machine learning concepts with Python programming language.

In between the six weekly online live tutorials (90 minutes long each) you will improve your skills by watching our pre-recorded instruction video tutorials at the 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. We estimate that the total time commitment is 40-50 hours over 6 teaching weeks.

During the course, you will also have weekly 1-2-1 check-ins (either via email or as a 15-minute Microsoft Teams call) with our tutor to supervise your progress and answer your questions.

This training course is tutor-led – online tutorials are presented live by our expert instructor, you can ask questions, discuss the topic and interact with other learners. You can also email the tutor during and after the course if you have any questions related to the material presented during the course.

The course will be recorded – you will have access to the pre-recorded video tutorials, recordings of the course live webinars and additional resources such as datasets, Python code, academic papers and other publications related to the topics of the course, as well as essential and supplementary coding exercises via Mind Project Learning Platform.

Start date: Thursday, 2nd of November 2023, 1:00 pm London (UK) time

Schedule of live sessions: Every Thursday at 1:00 pm London (UK) time for 6 weeks

Deadline for registrations: Tuesday, 31st of October 2023 @ 17:00 London (UK) time

 

During this course, you will learn and implement a variety of statistical and machine learning approaches including dimensionality reduction, common classifiers and clustering methods as well as more advanced predictive analytics models such as ensembles and supermodels. The course will be run according to the following schedule:

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 clustering,

• Hierarchical clustering,

• Evaluating clustering solutions, describing clusters and estimating cluster profiles,

• Overview of other important clustering methods: mean-shift, DBSCAN, and affinity propagation.

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.,

• Cross-validation and re-sampling methods e.g. bootstrapping,

• Probabilistic Naive Bayes classifier.

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

• Distance-based classification: k-Nearest Neighbours algorithm,

• Kernel-based Support Vector Machines,

• Semi-automated and automated tuning of classification models,

• Classification and Regression Trees (CART),

• Estimating variable importance.

Week 6: From decision trees to ensembles

• Tree-based Random Forests ensemble,

• Bagging and boosting (e.g. Adaptive Boosting – AdaBoost and Extra Gradient Boosting – XGBoost algorithms),

• Supermodels and machine learning pipelines.

Should you have any questions please contact Mind Project Ltd at info@mindproject.co.uk. Please visit the course website at Machine Learning with Python - 6-Week Tutor-Led Training Course - November 2023.

Cost:

By 12th of October 2023 (Early Bird offer): £780 (normally £900) - Commercial fee - individual customers representing commercial/business entities, £630 (normally £750) - NGO/Gov/Academic fee - applicable to representatives of registered charitable organisations, national health service employees (e.g. NHS in the UK), employed academic staff (e.g. research assistants, lecturers, post-docs and above), and employees of governmental departments (e.g. civil servants), £450 (normally £600) - Student fee - applicable to undergraduate and postgraduate students only (confirmation of student status required). Additional discounts available for multiple bookings and groups.

Website and registration:

Register for this course

Region:

Greater London

Keywords:

Bayesian methods, Regression Methods, Ordinary least squares (OLS), Generalized liner model (GLM), Linear regression, Logistic regression, Principal components analysis, Factor analysis, Cluster analysis, Data Mining, Machine learning, Bootstrap simulation , Permutation tests , Python, Decision Trees, ensembles, clustering, evaluation metrics, gradient boosting, supermodels

Related publications and presentations from our eprints archive:

Bayesian methods
Regression Methods
Ordinary least squares (OLS)
Generalized liner model (GLM)
Linear regression
Logistic regression
Principal components analysis
Factor analysis
Cluster analysis
Data Mining
Machine learning
Bootstrap simulation
Permutation tests
Python

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