Course summary

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.

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.

Who is this course for?

This tutor-led online course is suitable for data scientists, researchers, Master’s or PhD students, post-doctoral researchers, data analysts, developers and data engineers, who are currently using Python programming language (preferably at intermediate level) and would like to expand their skills to include machine learning and predictive analytics toolkit.

What is included in the course fee?

At Mind Project, we pride ourselves for delivering great quality, goal-directed, interactive and cost-effective training. Our remote, hybrid, and on-premises tutor-led courses are designed to maximise your opportunity to learn complex topics in shortest possible time while being able to discuss and interact with our tutor and other fellow attendees. Therefore, the registration fee for this course includes a number of benefits that many other training providers don’t offer:

• Weekly (90-minute long) remotely delivered live group webinars with our expert tutor over the period of 6 weeks – you will be able to ask questions, interact and discuss the topic with other attendees from the comfort of your home/office,
• you will have access to course online materials for 1 year via our Mind Project Learning Platform e.g. tutorial videos, exercises and quizzes, Python code scripts used during tutorials and live sessions, example datasets, optional and mandatory reading (e.g. blog articles, academic papers, industry reports), and other external recommended resources e.g. online books,
• 2-week post-course follow-up period during which you will be able to work on homework exercises and submit them for our feedback – the feedback will be provided either via email or Microsoft Teams (i.e. as a 15-minute 1-2-1 session with our tutor),
• exercises and tasks completed by you between the live webinar sessions will help you in better material retention and will enhance your learning progress,
• email supervision and weekly check-ins during the course period will maximise your learning outcomes and improve your learning experience; we will provide you with support throughout the course and the 2-week post-course follow-up period – feel free to ask us questions about the material presented at the training during this period,
• small group size allows easy interaction and stimulating environment for successful learning.

Programme outline

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.