01

Linear Regression

Understand how to model the relationship between variables and make predictions using least squares estimation.

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02

Logistic Regression

Learn binary classification using the sigmoid function and understand its role in prediction probability outcomes.

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03

Decision Tree

Explore how tree structures split datasets based on feature values for effective classification or regression.

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04

Random Forest

A powerful ensemble method that builds multiple decision trees and merges their output for improved accuracy.

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05

Support Vector Machine

Understand how SVM draws hyperplanes to separate data classes with maximum margin for better generalization.

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06

KNN Method

Learn the K-Nearest Neighbors approach for classification and regression based on distance from surrounding data.

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07

Naïve Bayes Classifier

Master the probabilistic classifier based on Bayes’ theorem with the “naïve” assumption of feature independence.

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08

K-Means Clustering

Group similar data points into clusters using iterative centroid optimization in unsupervised learning.

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09

Neural Network

Dive into deep learning models inspired by the human brain, with layers, activations, and backpropagation.

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