You are here:

Machine Learning with Python - 6-week tutor-led online course (fully booked)

Date:

17/09/2020 - 22/10/2020

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

video conference logo

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, pycaret 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 (to qualify for the Course Attendance Certificate) plus an additional 1 calendar month for the completion of the data science project (to obtain the graded Course Completion Certificate).

In between the six weekly online live tutorials (2.5 hours long each) you will improve your skills working through set tasks and homework exercises which will require 4-6 hours of your time commitment per week (24-36 hours). We estimate that the total time commitment for the Course Attendance Certificate is 40-50 hours over 6 teaching weeks, and for the Course Completion Certificate it will equate to 70-80 hours (over 2.5-month period) including the project report writing time.

Start date: Thursday, 17th of September 2020 @14:30 London (UK) time
Schedule of sessions: Every Thursday at 14:30 London (UK) time for 6 weeks
Deadline for registrations: Tuesday, 15th of September 2020 @ 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.

 

Additionally, in order to receive the full Course Completion Certificate, you will have to submit a short data analysis report (up to 2,000 words) along with R data processing and analysis scripts within one calendar month from the last day of Week 6. The project will be assessed and graded. You will also receive a formal written feedback about your project.

 

3. Course pre-requisites and further instructions

  • We recommend that all attendees have the most recent version of Anaconda Individual Edition of Python 3.7 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.anaconda.com/distribution/. Please contact us should you have any questions or issues with the installation process.
  • We recommend that the attendees have at least basic knowledge of Python programming language and 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) during the tutor-led video sessions - please note we use open-source Jitsi video conferencing application directly deployed on our secure server (located in Ireland, European Union, and provided by Microsoft).
  • You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) in order to attend the video-streamed tutorials. You may also use your mobile phone (Android or iOS) to connect to our tutor-led video sessions, in that case please install Jitsi Meet Mobile available at https://jitsi.org/downloads/. Meeting ID, along with personal usernames and passwords will be provided to the registered learners before the course.
  • The primary spoken and written language of the course is English.

 

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/.

Cost:

By 27th of August 2020 (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.

Region:

Greater London

Keywords:

Regression Methods, Ordinary least squares (OLS), Generalized liner model (GLM), Linear regression, Logistic regression, Hierarchical models, Forecasting, Data Mining, Machine learning, Dynamic models, Python, clustering , classification

Related publications and presentations:

Regression Methods
Ordinary least squares (OLS)
Generalized liner model (GLM)
Linear regression
Logistic regression
Hierarchical models
Forecasting
Data Mining
Machine learning
Dynamic models
Python

Back to archive...