Summary Introduction to Machine Learning: Concepts, Techniques, and Applications
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Course
Machine Learning
Institution
Machine Learning
Dive into the world of Machine Learning (ML) with this comprehensive introductory course designed to equip you with the fundamental concepts, techniques, and practical applications of ML. This course is tailored for beginners with a keen interest in understanding how machines can learn from data an...
Classification: Logistic Regression and K Nearest Neighbors
Gradient Boosting for Competitive Predictions
Gradient Boosting is a powerful technique for making competitive
predictions.
It involves combining the predictions of multiple weak models to create
a strong model.
The predictions are made in a sequential manner, with each model trying
to correct the mistakes of the previous one.
Logistic Regression
Logistic Regression is a classification algorithm used for predicting
binary outcomes.
It estimates the probability of an event occurring based on the input
features.
The Logistic Regression model outputs a value between 0 and 1, which
can be interpreted as the probability of the positive class.
K Nearest Neighbors (KNN)
K Nearest Neighbors is a simple yet effective classification algorithm.
It works by finding the K closest data points to a given test point and
then using the majority class of those K points to make a prediction.
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