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
publication date
2022-12-27
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
key features
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
approach
This course focuses on covering first the theoretical background of how the model works so that you can build a proper intuition around its behavior. Then we will have the practical component, where we will implement the machine learning model and use it on actual data. In this way, you gain both hands-on as well as a solid theoretical foundation of how the different machine learning models work, and you will be able to use this knowledge to better choose and fix models, depending on the situation.
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.
short description
Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.
subtitle
Become essential in a world centered around machine learning
keywords
Python, Machine Learning, Scikit Learning, Classification, Regression, Ensembles, Dimensionality Reduction, Unsupervised Learning
Product ISBN
9781804619049
