Advanced Machine Learning: Techniques and Applications with Python
This course is the natural next step after the Fundamentals class A002. It builds on the knowledge acquired in the first course and takes a deeper dive into Regression methods and Classification techniques. The course then expands into the world of Clustering and Recommender systems, providing participants with a broader understanding of the most important Machine Learning approaches. As in the previous course, all concepts are illustrated with practical examples and real-world use cases that can be adapted to a wide range of situations. By the end of the course, participants will have developed strong practical machine learning skills that they can apply confidently in personal, professional, and business contexts.
What you will learn
This course will provide you with two complementary types of skills. First, you will develop strong technical skills and gain a deeper understanding of key Machine Llearning techniques. Second, you will develop practical consulting skills: knowing when to apply which technique, how to select the right approach for a given problem, and where to find relevant examples and resources. By the end of the course, you will have gained:
- A deep dive into Regression methods
- A deep dive into Classification methods
- A comprehensive exploration of the most important Clustering approaches
- An introduction to Recommender Systems
- Practical experience applying these techniques to real-world challenges
More importantly, this course prepares you for the next step in your Machine Learning Journey. You will leave with the knowledge, practical skills, and confidence to tackle a wide range of Machine Learning challenges in both professional and personal contexts.
Programme
- A recap of Machine Learning within the broader AI landscape
- Non-Linear Regression
- Polynomial Regression
- Logistic Regression
- Decision Trees
- K-Means Clustering
- DBSCAN Clustering
- Hierarchical Clustering
- Content-Based Recommender Systems
- Collaborative Filtering Recommender Systems
- Machine Learning Quality Metrics
And, of course, plenty of practical exercises and hands-on labs!