Data Science: Supervised Machine Learning in Python

Data Science: Supervised Machine Learning in Python
Last updated 11/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

What you’ll learn
Understand and implement K-Nearest Neighbors in Python
Understand the limitations of KNN
User KNN to solve several binary and multiclass classification problems
Understand and implement Naive Bayes and General Bayes Classifiers in Python
Understand the limitations of Bayes Classifiers
Understand and implement a Decision Tree in Python
Understand and implement the Perceptron in Python
Understand the limitations of the Perceptron
Understand hyperparameters and how to apply cross-validation
Understand the concepts of feature extraction and feature selection
Understand the pros and cons between classic machine learning methods and deep learning
Use Sci-Kit Learn
Implement a machine learning web service

Requirements
Python, Numpy, and Pandas experience
Probability and statistics (Gaussian distribution)
Strong ability to write algorithms

Description