Data science : supervised machine learning in Python.

Format:

Video

Contributor:

Lazy Programmer, instructor.

Packt Publishing, publisher.

Language:

English

Subjects (All):

Python (Computer program language).

Machine learning.

Physical Description:

1 online resource (1 video file (3 hr., 51 min.)) : sound, color.

Edition:

[First edition].

Place of Publication:

[Birmingham, United Kingdom] : Packt Publishing, 2026.

Summary:

In this 3-hour course, you'll master supervised machine learning techniques using Python, including K-Nearest Neighbor (KNN), Naive Bayes, and Decision Trees. You'll implement algorithms on real datasets like MNIST, fine-tune models, and deploy them as web services for real-world applications. What I will be able to do after this course Understand and implement K-Nearest Neighbor (KNN) algorithm Master Naive Bayes for both continuous and discrete data Build and optimize decision trees for classification Apply advanced techniques like LDA, QDA, and non-Naive Bayes models Deploy machine learning models as web services Course Instructor(s) The Lazy Programmer, a seasoned educator with master's degrees in computer engineering and statistics, specializes in machine learning, deep learning, and pattern recognition. With a decade of experience, he's a full-stack software engineer with expertise in Python, bioinformatics, and algorithmic trading. He simplifies complex topics in data science and AI for students worldwide. Who is it for? This course is ideal for aspiring data scientists, machine learning engineers, and developers transitioning into data science. It's perfect for those familiar with Python programming who want to enhance their machine learning skills.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

9781807785796

OCLC:

1587305286