Course Outline
Introduction to Applied Machine Learning
- Statistical learning versus Machine learning
- Iteration and evaluation processes
- Understanding the Bias-Variance trade-off
Machine Learning with Python
- Selecting appropriate libraries
- Utilizing add-on tools
Regression Techniques
- Linear regression
- Generalizations and Nonlinearity
- Practical exercises
Classification Methods
- Review of Bayesian principles
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
- Practical exercises
Cross-validation and Resampling
- Different cross-validation approaches
- Bootstrap methods
- Practical exercises
Unsupervised Learning
- K-means clustering
- Illustrative examples
- Challenges in unsupervised learning and advanced techniques beyond K-means
Requirements
Proficiency in the Python programming language is required. A foundational understanding of statistics and linear algebra is also recommended.
Testimonials (5)
The trainer showed that he has a good understanding of the subject.
Marino - EQUS - The University of Queensland
Course - Machine Learning with Python – 2 Days
It was a great intro to ML!! I liked the whole thing, really. The organization was perfect. The right amount of time for lectures/ demos and just us playing around. Lots of topics were touched, just at the right level. He was also very good at keeping us super engaged, even without any camera being on.
Zsolt - EQUS - The University of Queensland
Course - Machine Learning with Python – 2 Days
Clarity of explanation and knowledgeable response to questions.
Harish - EQUS - The University of Queensland
Course - Machine Learning with Python – 2 Days
The knowledge of the trainer was very high and the material was well prepared and organised.
Otilia - TCMT
Course - Machine Learning with Python – 2 Days
I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.
