Machine Learning Fundamentals with Python Training Course
Duration14 hours (usually 2 days including breaks)
Knowledge of Python programming language. Basic familiarity with statistics and linear algebra is recommended.
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Introduction to Applied Machine Learning
- Statistical learning vs. Machine learning
- Iteration and evaluation
- Bias-Variance trade-off
Machine Learning with Python
- Choice of libraries
- Add-on tools
- Linear regression
- Generalizations and Nonlinearity
- Bayesian refresher
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
Cross-validation and Resampling
- Cross-validation approaches
- K-means clustering
- Challenges of unsupervised learning and beyond K-means
Bookings, Prices and Enquiries
- Public Classroom
- Participants from multiple organisations. Topics usually cannot be customised
- Private Classroom
- Participants are from one organisation only. No external participants are allowed. Usually customised to a specific group, course topics are agreed between the client and the trainer.
- Private Remote
- The instructor and the participants are in two different physical locations and communicate via the Internet
The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.
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