Gain technical skills to use machine learning on the job or for your own projects
Dive deep into classification, regression, ensembles, dimensionality reduction, and unsupervised learning
Get ready to start applying machine learning on the job or in technical interviews
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.
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.
All the resources for this course are available at: https://github.com/PacktPublishing/Python-Machine-Learning-Bootcamp
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