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1. What is Feature Engineering? Using intuition to design new features, by transforming or combinging original features. Example) House price. If we are given the dimension of the house such as length and width, we can create a new variable to include for price prediction of a house. For example, we create a area variable with length and width. 2. Feature engineering in Polynomial Regression We ..

1. Checking Gradient descent for convergence How to check if Gradient descent is working well? 1.1 Graph This graph is called Learning curve. As the iteration increases, the minimum cost should decrease. If the graph remains constant after enough iterations, we call that it has converged 1.2 Epsilon / Automatic convergence test Let epsilon be 0.001 If the cost function decreaes by epsilon in one..

1. Intuition House example: Lets say we are predicting the price of a house based on its size and number of bed rooms. Number for size of a house is relatively larger than the number of bed rooms. This large difference between numbers will make it hard to accurately predict the price. How is this related to gradient descent? Since the number for size is large, it takes smaller w value to make a ..

1. Intuition Instead of prediction house price with only the size of a house, we can use multiple features of a house to make a better prediction This is a standard notation for multiple linear regression W and X can be represented in the form of vectors. This is the other format of writing multiple linear regression equation. Multiplication of vectors are called "Dot Product" 2. Vectorization D..

Gradient Descent Definition - This is an algorithm to find minimum cost function. - finding w and b in f(x) = wx + b that makes minimum cost function Goal: Example) Problem: What is the fastest way to get down to the lowest ground from the hill? How: By using Gradient Descent Implementation Gradient descent algorithm Alpha represents the learning rate Learning rate decides how big step you are g..

1. Linear Regression Purpose: To predict the output based on the given examples or dataset 1. 1 Terminology Univariate linear regression: Linear regression with one(single feature x) variable fw,b(x) = wx + b 1.2 Code Necessary Libraries #Numpy, a popular library for scientific computing import numpy as np #Matplotlib, a popular library for plotting data import matplotlib.pyplot as plt x_train =..

What is Machine Learning? Def: Subfield of Artificial Intelligence, application of AI (AI 의 활용) Training Methods 1. Supervised Learning (지도 학습) Def: - Give learning algorithms an example - Learns from being given “right examples”(right output) Types of Algorithms 1.1 Regression Def) Predicts a number based on the dataset 1.2 Classification Def) Prediction a small number of categories by drawing ..