Machine Learning with R

Course Curriculum

Chapter 1: Machine Learning with R

Lecture 1: Introduction to Machine Learning

Lecture 2: How do Machine Learn

Lecture 3: Steps to Apply Machine Learning

Lecture 4: Regression and Classification Problems

Lecture 5: Basic Data Manipulation in R

Lecture 6: More on Data Manipulation in R

Lecture 7: Basic Data Manipulation in R – Practical

Lecture 8: Create a Vector

Lecture 9: 2.7 Problem and Solution

Lecture 10: 2.10 Problem and Solution

Lecture 11: Exponentiation Right to Left

Lecture 12: 2.13 Avoiding Some Common Mistakes

Lecture 13: Simple Linear Regression

Lecture 14: Simple Linear Regression Continues

Lecture 15: What is Rsquare

Lecture 16: Standard Error

Lecture 17: General Statistics

Lecture 18: General Statistics Continues

Lecture 19: Simple Linear Regression and More of Statistics

Lecture 20: Open the Studio

Lecture 21: What is R Square

Lecture 22: What is STD Error

Lecture 23: Reject Null Hypothesis

Lecture 24: Variance Covariance and Correlation

Lecture 25: Root names and Types of Distribution Function

Lecture 26: Generating Random Numbers and Combination Function

Lecture 27: Probabilities for Discrete Distribution Function

Lecture 28: Quantile Function and Poison Distribution

Lecture 29: Students T Distribution, Hypothesis and Example

Lecture 30: Chai-Square Distribution

Lecture 31: Data Visualization

Lecture 32: More on Data Visualization

Lecture 33: Multiple Linear Regression

Lecture 34: Multiple Linear Regression Continues

Lecture 35: Regression Variables

Lecture 36: Generalized Linear Model

Lecture 37: Generalized Least Square

Lecture 38: KNN- Various Methods of Distance Measurements

Lecture 39: Overview of KNN- (Steps involved)

Lecture 40: Data normalization and prediction on Test Data

Lecture 41: Improvement of Model Performance and ROC

Lecture 42: Decision Tree Classifier

Lecture 43: More on Decision Tree Classifier

Lecture 44: Pruning of Decision Trees

Lecture 45: Decision Tree Remaining

Lecture 46: Decision Tree Remaining Continues

Lecture 47: General concept of Random Forest

Lecture 48: Ada Boosting and Ensemble Learning

Lecture 49: Data Visualization and Preparation

Lecture 50: Tuning Random Forest Model

Lecture 51: Evaluation of Random Forest Model Performance

Lecture 52: Introduction to Kmeans Clustering

Lecture 53: Kmeans Elbow Point and Dataset

Lecture 54: Example of Kmeans Dataset

Lecture 55: Creating a Graph for Kmeans Clustering

Lecture 56: Creating a Graph for Kmeans Clustering Continues

Lecture 57: Aggregation Function of Clustering

Lecture 58: Conditional Probability with Bayes Algorithm

Lecture 59: Venn Diagram Naive Bayes Classification

Lecture 60: Component OF Bayes Theorem using Frequency Table

Lecture 61: Naive Bayes Classification Algorithm and Laplace Estimator

Lecture 62: Example of Naive Bayes Classification

Lecture 63: Example of Naive Bayes Classification Continues

Lecture 64: Spam and Ham Messages in Word Cloud

Lecture 65: Implementation of Dictionary and Document Term Matrix

Lecture 66: Executes the Function Naive Bayes

Lecture 67: Support Vector Machine with Black Box Method

Lecture 68: Linearly and Non- Linearly Support Vector Machine

Lecture 69: Kernal Trick

Lecture 70: Gaussian RBF Kernal and OCR with SVMs

Lecture 71: Examples of Gaussian RBF Kernal and OCR with SVMs

Lecture 72: Summary of Support Vector Machine

Lecture 73: Feature Selection Dimension Reduction Technique

Lecture 74: Feature Extraction Dimension Reduction Technique

Lecture 75: Dimension Reduction Technique Example

Lecture 76: Dimension Reduction Technique Example Continues

Lecture 77: Introduction Principal Component Analysis

Lecture 78: Steps of PCA

Lecture 79: Steps of PCA Continues

Lecture 80: Eigen Values

Lecture 81: Eigen Vectors

Lecture 82: Principal Component Analysis using Pr-Comp

Lecture 83: Principal Component Analysis using Pr-Comp Continues

Lecture 84: C Bind Type in PCA

Lecture 85: R Type Model

Lecture 86: Black Box Method in Neural Network

Lecture 87: Characteristics of a Neural Networks

Lecture 88: Network Topology of a Neural Networks

Lecture 89: Weight Adjustment and Case Update

Lecture 90: Introduction Model Building in R

Lecture 91: Installing the Package of Model Building in R

Lecture 92: Nodes in Model Building in R

Lecture 93: Example of Model Building in R

Lecture 94: Time Series Analysis

Lecture 95: Pattern in Time Series Data

Lecture 96: Time Series Modelling

Lecture 97: Moving Average Model

Lecture 98: Auto Correlation Function

Lecture 99: Inference of ACF and PFCF