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  1 - Introduction

   1 - Welcome to Machine Learning Course.mp4

   2 - What is Machine Learning.mp4

   3 - Supervised Learning.mp4

  2 - Linear Regression

   10 - Feature Scaling.mp4

   11 - Download RStudio.txt

   11 - housingprices.csv

   12 - Multiple Linear Regression in R [IMPLEMENTATION] English.vtt

   4 - Linear Regression English.vtt

   5 - Cost Function Intuition English.vtt

   6 - Cost Function Example English.vtt

   7 - Gradient Descent English.vtt

   8 - Gradient Descent Algorithm English.vtt

   9 - Multivariate Linear Regression English.vtt

  3 - Logistic Regression

   13 - Logistic Regression.mp4

   14 - Cost Function and Gradient Descent.mp4

  4 - Regularization

   15 - Problem of Overfitting.mp4

   16 - Regularized Linear Regression.mp4

   17 - Regularized Gradient Descent.mp4

  5 - Unsupervised Learning

   18 - What is Unsupervised Learning.mp4

   19 - Clustering Analysis.mp4

   20 - Similarity and Dissimilarity.mp4

   21 - Euclidean Distance.mp4

   22 - Partitional Vs Hierarchical Clustering.mp4

   23 - Agglomerative Vs Divisive Clustering.mp4

   24 - K Means Clustering Algorithm.mp4

  6 - Probabilistic Machine Learning Models

   25 - Conditional Probability Basics.mp4

   26 - Total Probability Theorem.mp4

   27 - Naive Bayes Classifier Intuition.mp4

   28 - Gaussian Naive Bayes Sex Classifier.mp4

   29 - Sex Classifier in Python SkLearn.mp4

  7 - Neural Networks

   30 - Neural Networks Representation.mp4

   31 - Perceptron Learning Algorithms.mp4

   32 - Practice Perceptron Learning.mp4

  Bonus Resources.txt

  Machine Learning Basics to Advanced.txt