Master Machine Learning: Basics, Jobs and Interview Bootcamp

Explore simple linear regression by defining the dependent variable and the independent variable, and show how the regression coefficient and the line predict outcomes such as salary.

Learn to use dummy variables in a linear regression model, categorize states like New York, California, and Los Angeles, and avoid the dummy variable trap by dropping one category.

Select a full model with all predictors, then iteratively discard the worst predictors using p-values and a 0.05 significance level to refine backward elimination.

Train a multiple regression model after preprocessing and a train-test split, then evaluate predictions and prepare for backward elimination by examining p-values and model significance.

Explore backward elimination in multiple linear regression by fitting a full model, evaluating p-values against a 0.05 significance level, and sequentially removing least significant predictors.

Discover why polynomial regression is needed when linear regression fails to fit exponential-like data. Learn how polynomial regression captures complex trends, with stock market analysis and investment strategy applications.

Practice polynomial regression to model salaries by level, transforming the single feature into polynomial features and comparing linear and polynomial fits to select an appropriate degree.

Explore random forest regression by loading a dataset, fitting the model with a chosen number of trees and a random state, and using ensemble averaging to predict and visualize results.

Master machine learning basics with an introduction to k-nearest neighbors, showing how to choose k, compute Euclidean distances, and classify a new point by majority among its five nearest neighbors.

Apply k-nearest neighbor on a labeled dataset by importing libraries, preparing X and Y, using training and testing data, and evaluating with a confusion matrix and accuracy using Euclidean distance.