I will present basic concepts related to data processing, model selection, model training, and validation. For this purpose, I will use the Python programming language. The lecture will consist of a practical part, where I will discuss the code fragments implementing the techniques. The entry threshold to Machine Learning, contrary to appearances, is not so high, but for many people, the knowledge available on the Internet is not presented in a clear way. During this course, systematized knowledge and the possibility of explaining any ambiguities on the fly will be the key to break down these barriers.

On the workshop you will

Learn from lecture supported by slides

Solve coding challenges

Complete practical exercises

During the workshop we cover:

At my workshop, you will learn what is Machine Learning, and how to use it in practice. I will tell, what was the reason for almost 20 years of winter in AI. I will point out the differences between the standard process of writing software, and building Machine Learning tools, and explain why confusion matrix is an excellent quality metric.

The practical Part

The main focus of the workshop will be the practical part, where using interactive notebooks I will present you with the basic ML tools, algorithms, and techniques. After each part there will be a short task in which you will be able to apply the newly learned techniques to process data, build models and evaluate them.

ML Tools

Introduction to libraries pandas (data storage and manipulation), numpy (numerical processing), matplotlib (data visualization), sklearn (modeling), etc.

Data processing

Handling missing data, scaling, normalization, visualization, exploratory analysis, coding of categorical variables

Modeling

Regression and classification problem: models (linear regression, decision trees, k-nearest neighbors), cost functions, quality metrics, model training, cross-validation, regularization, model evaluation

Mini Project

The final assignment, this time a bit larger will touch on all the areas you have learned. There will be room for more flexibility and experimentation. I expect there will be many questions during this phase that we can answer during the course, and during feedback on the projects.

###Summary

The workshop aims at the practical use of ML, writing code by hand as soon as possible, empirical learning of technical aspects of data processing and modeling. During the workshop, I use various forms of multimedia, and a variety of comparisons to stimulate imagination and give a chance to better understand all the concepts. The interactive formula is designed to encourage active participation and frequent questioning.

Requirements

  • Familiarity with Python or other programming languages. No fear of mathematics and understanding of basic concepts of statistics.
Mateusz Dorobek

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Mateusz Dorobek

Data Scientist, Freelancer & AI conference speaker with 5 years of teaching experience. Working in the financial industry using Machine Learning to solve business problems.

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