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Machine Learning Algorithms

Mackenzie Biduk 2021-2022

The following are Machine Learning algorithms I learned during the coursera Machine Learning course I took from Stanford during the summer of 2021.

01. Food Truck Profits

Supervised Learning - Regression - Linear Regression

  • Supervised Learning
  • Linear Regression
  • Gradient Descent

Food Truck Profits - 2 - Linear Regression with Gradient Descent

02. Housing Prices

Supervised Learning - Regression - Multivariable Regression

  • Supervised Learning
  • Multivariable Regression
  • Multivariable Gradient Descent
  • Mean Normalization

Housing Prices - 2 - Multivariable Gradient Descent with Mean Normalization

Housing Prices - 3 - Normal Equation - Output

03. Admissions

Supervised Learning - Classification - Logistic Regression

  • Supervised Learning
  • Classification
  • Logistic Regression

Admissions - 2 - Logistic Regression - Figure

04. Microchips

Supervised Learning - Classification - Logistic Regression

  • Supervised Learning
  • Classification
  • Logistic Regression
  • Regularization

Microchips - 2 - Regularized Logistic Regression - Figure

05. Hand Writing Recognition

Supervised Learning - Classification - Naive Bayes & Neural Network

  • Supervised Learning
  • Classification
  • Naive Bayes (One Vs All)
  • Regularization
  • Neural Networks
  • Feedforward
  • Backpropagation

Handwritting Recognition - 1 - Data - Figure 1

temp

Handwritting Recognition - 4 - Neural Network - Figure 1

06. Dam Water Flow

Supervised Learning - Regression - Linear Regression

  • Supervised Learning
  • Linear Regression
  • Bias/Variance
  • Learning Curves
  • Selecting Lambda
  • Training/CV/Test Data

Dam Water Flow - 8 1 - Selecting Lambda - ErrorVsLambda

image

Dam Water Flow - 9 - Computing Test Error - Polynomial Regression Best L (Test Data)

07. Recognition Software

Supervised Learning - Classification - Support Vector Machines

  • Supervised Learning
  • Classification
  • Support Vector Machines
  • Linear Kernel

Recognition Software - 07 2 - Training Linear Kernel SVM - C=1

08. Engineering Graduates

Supervised Learning - Classification - Support Vector Machines

  • Supervised Learning
  • Classification
  • Support Vector Machines
  • Gaussian Kernel (Non-Linear)

Engineering Graduates - 08 3 - Training RBF Kernel SVM

09. NFL Linemen

Supervised Learning - Classification - Support Vector Machines

  • Supervised Learning
  • Classification
  • Support Vector Machines
  • Gaussian Kernel (Non-Linear)
  • Selecting the Best Values for C (1/Lambda) and the Varriance

NFL Linemen - 09 2 - Training RBF Kernel SVM

10. Spam Filter

Supervised Learning - Classification - Support Vector Machines

  • Supervised Learning
  • Classification
  • Support Vector Machines
  • Linear Kernel
  • Regular Expressions

Spam Filter - 10 5 - Top Predictors of Spam

Spam Filter - 10 6 - Classifying Additional Emails

11. School Locations

Unsupervised Learning - Clustering - K-Means

  • Supervised Learning
  • Clustering
  • K-Means

School Locations - 11 3 - K-Means Clustering Iteration 1

School Locations - 11 3 - K-Means Clustering Iteration 10

12. Image Compression

Unsupervised Learning - Clustering - K-Means

  • Supervised Learning
  • Clustering
  • K-Means

Image Compression - 12 2 - Image Compression