Packt Publishing – Data Science Supervised Machine Learning In Python 2026
English | Tutorial | Size: 621.11 MB
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.
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