Python machine learning bootcamp. (Packt Publishing, 2022)
In this course, we will cover many different types of machine learning aspects. We will start by going through a sample machine learning project from an idea to developing a final working model. You will learn many important techniques around data preparation, cleaning, feature engineering, optimization and learning techniques, and much more. Once we have gone through the whole machine learning project, we will then dive deeper into several different areas of machine learning, to better understand each task, and how each of the models we can use to solve these tasks work, and then also using each model and understanding how we can tune all the parameters we learned about in the theory components. We will dive deeper into classification, regression, ensembles, dimensionality reduction, and unsupervised learning. At the end of this course, you should have a solid foundation of machine learning knowledge. You will be able to build machine learning solutions to different types of problems you will come across and be ready to start applying machine learning on the job or in technical interviews. What You Will Learn Learn how to take an ML idea and flush it out into a fully functioning project Learn the different types of ML approaches and the models within each section Get a theoretical and intuitive understanding of how each model works See the practical application and implementation for each model we cover Learn how to optimize models Learn the common pitfalls and how to overcome them Audience This course is designed for beginner Python programmers and data scientists who want to understand ML (Machine Learning) models in depth and be able to use them in practice. Basic Python knowledge is required and some previous experience with the Pandas and Matplotlib libraries will be helpful. About The Author Maximilian Schallwig: Maximilian Schallwig is a data engineer and a proficient Python programmer. He holds a bachelor's degree in physics and a master's degree in astrophysics. He has been working on data for over five years, first as a data scientist and then as a data engineer. He can talk endlessly about big data pipelines, data infrastructure, and his unwavering devotion to Python. Even after two unsuccessful attempts in high school, he still decided to learn Python at the University. He cautiously stepped into the realm of data, beginning with a simple Google search for "what does a data scientist do". He was determined to pursue a career in data science to become a data engineer by learning about big data tools and infrastructure design to build scalable systems and pipelines. He enjoys sharing his programming skills with the rest of the world.