Machine Learning with Python - 6-week tutor-led online course
10/06/2022 - 15/07/2022
Mind Project Ltd
Simon Walkowiak MSc, MBPsS
Intermediate (some prior knowledge)
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: Friday, 10th of June 2022 @14:00 London (UK) time
Schedule of sessions: Every Friday at 14:00 London (UK) time for 6 weeks
Deadline for registrations: Wednesday, 8th of June 2022 @ 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 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.
Week 5: Binary and multinomial classification - part 2: overview of other important approaches
- 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,
- Tree-based Random Forests ensemble,
- Bagging and boosting (e.g. Adaptive Boosting – AdaBoost and Extra Gradient Boosting – XGBoost algorithms).
Should you have any questions please contact Mind Project Ltd at email@example.com or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/machine-learning-with-python-6-week-tutor-led-online-course-june-2022/.
By 13th of May 2022 (Early Bird offer): £345 (normally £420) per person for the whole course (regular fee). £225 (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:
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 , Python
Related publications and presentations:
Ordinary least squares (OLS)
Generalized liner model (GLM)
Principal components analysis