Udemy – Intermediate Machine Learning 2024-11

Students will then be introduced to the various subsets of machine learning, with a particular focus on the most popular supervised and unsupervised machine learning models, as well as several deep learning architectures. We will also cover semi-supervised and reinforcement learning to a lesser extent. Lectures on specific models will be given with the aim of educating students about the main idea behind the models, the main differences between the different models, and what are considered their advantages and disadvantages. We will not provide detailed mathematical explanations of these models, but some of the discussions will provide insights into aspects of the underlying mathematics that affect how the models work and the problems for which they are suitable.

What you will learn in the Intermediate Machine Learning course

• Identifying the different important steps in the data pipeline
• Identify common pitfalls when conducting data collection for machine learning projects
• Reviewing collected data to find any potential attribute relationships and data quality concerns
• Creating data visualizations that help identify any patterns that can be used
• Selecting features that are likely to be informative
• Clean the dataset by addressing potential data quality issues.
• Organizing data sets so they are ready for model input
• Distinguish between supervised, unsupervised, semi-supervised, and self-supervised learning
• Comparing traditional machine learning and deep learning
• Discussion of various popular supervised and unsupervised learning models
• Using Scikit-learn to solve basic to intermediate machine learning problems
• Explaining what neural networks are
• Comparing different types of neural networks
• Implementing beginner to intermediate deep learning solutions using PyTorch

This course is suitable for people who:

• This course is intended for engineers and developers who want to learn more about machine learning and who want to potentially move into a data scientist or machine learning engineer role. We won’t discuss the underlying mathematical principles, but you’ll know enough by the end of this course to be able to use existing implementations to solve beginner to intermediate machine learning problems.
• This course will be useful for students who plan to pursue a career as a data scientist or machine learning engineer. It will provide you with a strong foundational knowledge of most of the key aspects of a machine learning project, providing you with a strong foundation to continue your understanding of the field.
• This course will also be useful for managers who want to understand the important aspects of machine learning projects and what to consider when pursuing such a project. It will also provide them with enough knowledge to participate in machine learning discussions and know which questions are important.
• This course can be useful for someone who has taken our introductory machine learning course and wants to continue learning about different models and how to implement them in Scikit-learn or PyTorch. However, there is some overlap between the material in the two courses and so some information will be repeated (although we will usually provide more information in this course).
• And…