Python Machine Learning for Beginners
Alex Campbell
Have you thought about a career in data science? It's where the money is right now, and it's only going to become more widespread as the world evolves. Machine learning is a big part of data science, and for those that already have experience in programming, it's the next logical step.
Machine learning is a subsection of AI, or Artificial Intelligence, and computer science, using data and algorithms to imitate human thinking and learning. Through constant learning, machine learning gradually improves its accuracy, eventually providing the optimal results for the problem it has been assigned to.
It is one of the most important parts of data science and, as big data continues to expand, so too will the need for machine learning and AI.
Here's what you will learn in this quick guide to machine learning with Python for beginners:
What machine learning isWhy Python is the best computer programming language for machine learningThe different types of machine learningHow linear regression worksThe different types of classificationHow to use SVMs (Support Vector Machines) with Scikit-LearnHow Decision Trees work with ClassificationHow K-Nearest Neighbors worksHow to find patterns in data with unsupervised learning algorithmsYou will also find plenty of code examples to help you understand how everything works.
If you are ready to take your programming further, scroll up, click Buy Now, and find out why machine learning is the next logical step.
Duration - 2h 52m.
Author - Alex Campbell.
Narrator - Sean Leyden.
Published Date - Sunday, 22 January 2023.
Copyright - © 2022 Alex Campbell ©.
Location:
United States
Description:
Have you thought about a career in data science? It's where the money is right now, and it's only going to become more widespread as the world evolves. Machine learning is a big part of data science, and for those that already have experience in programming, it's the next logical step. Machine learning is a subsection of AI, or Artificial Intelligence, and computer science, using data and algorithms to imitate human thinking and learning. Through constant learning, machine learning gradually improves its accuracy, eventually providing the optimal results for the problem it has been assigned to. It is one of the most important parts of data science and, as big data continues to expand, so too will the need for machine learning and AI. Here's what you will learn in this quick guide to machine learning with Python for beginners: What machine learning isWhy Python is the best computer programming language for machine learningThe different types of machine learningHow linear regression worksThe different types of classificationHow to use SVMs (Support Vector Machines) with Scikit-LearnHow Decision Trees work with ClassificationHow K-Nearest Neighbors worksHow to find patterns in data with unsupervised learning algorithmsYou will also find plenty of code examples to help you understand how everything works. If you are ready to take your programming further, scroll up, click Buy Now, and find out why machine learning is the next logical step. Duration - 2h 52m. Author - Alex Campbell. Narrator - Sean Leyden. Published Date - Sunday, 22 January 2023. Copyright - © 2022 Alex Campbell ©.
Language:
English
Opening Credits
Duration:00:00:13
Opening credits. Book 1.
Duration:00:00:14
Introduction
Duration:00:03:15
Chapter 1
Duration:00:09:09
Chapter 2
Duration:00:08:25
Chapter 3
Duration:00:15:07
Chapter 4
Duration:00:10:33
Chapter 5
Duration:00:10:33
Chapter 6
Duration:00:11:38
Chapter 7
Duration:00:08:48
Chapter 8
Duration:00:07:48
Conclusion
Duration:00:01:10
Closing credits. Book1.
Duration:00:00:16
Opening credits. Book 2.
Duration:00:00:12
Python intro
Duration:00:01:50
Part one. Getting started
Duration:00:00:40
Python installation
Duration:00:08:03
Common machine learning packages
Duration:00:05:23
Collecting the data
Duration:00:07:30
Preparing the data
Duration:00:23:14
Choosing the machine learning model
Duration:00:13:57
Training the machine learning model
Duration:00:07:50
Evaluating your model
Duration:00:08:25
Tuning the parameters
Duration:00:06:36
Making predictions
Duration:00:00:23
Closing credits. Book 2.
Duration:00:00:12
Ending Credits
Duration:00:00:14