Duration: 24 hours
DescriptionWe’ll look at the theory on classification, regression, predictions, and ensembles. The entire course is built around practical cases with datasets.
For each case, we go through the entire life cycle of a machine learning project. Exploring, cleaning, and preparing data. Selecting a learning method to match the task (linear regression for regression, random forest for classification, K-average and DBSCAN for clustering). Learning with the use of the selected method. Outcome assessment. Model optimization. Representing the result to the customer.
We will also devote time to discussing practical tasks that you might deal with, which can be solved by using the reviewed methods.
- Which tasks are better solved by machine learning and which are being solved. What will happen if instead of a Data Scientist you hire a non-specialist in a given domain (just a developer/analyst/manager), expecting that they will learn everything in the process.
Preparing, cleaning, and exploring data
- How to gain insight into initial business data (and find whatever order in it at all). Processing sequence. What can and should be done by domain analysts, and what should better be done by a Data Scientist. Priorities in solving a specific task.
Classifiers and Regressors
- Practice – well formalized tasks with prepared data. Differences between tasks (binary/nonbinary/probabilistic classification, regression), redistribution of tasks across classes. Examples of practical tasks classification.
- Where and how to do clustering: exploring data, task setting check, and validation of results. Which cases can be reduced to clustering.
- Business metrics and technical metrics. Metrics for tasks of classification and regression, error matrix. Internal and external metrics of clustering quality. Cross validation. Overfitting.
- What makes one model better than another: parameters, traits, and ensembles. Parameter management. Traits selection practice. Overview of tools for searching best parameters/traits/methods.
Graphs, reports, dealing with real-life tasks
- How to visualize and present results. Semi-automated tests, process control points. From real-life tasks to complete R&D process (“R&D in practice”) – reviewing and analyzing tasks from the audience.
- Understand what tasks can be solved with the help of machine learning (and find out that Big Data is just a subsection, not a mandatory requirement)
- Learn how to utilize initial methods of machine learning, and by using fast prototyping tools learn how to answer the question “Can you evaluate an actual income from possible implementation?”
- Highlight data that should be collected and what can be required from it in near future. Why “we want to store petabytes” – it’s not always just a whim
- Get prepared for more complex subjects, particularly to complete solutions of real complex business problems
- See how exactly machine learning fits with classical analytics. In particular, make sure that it’s unnecessary (or even harmful) to dismiss all existing analysts for concept implementation
- Project Managers who deal with data
- Technical Leads / Senior Developers in any data related projects
- Business Analysts
- Data Engineers
- Architects, System Designers
- Ability to read simple code in Python and to write in any script language.