MACHINE LEARNING WITH R

R for Machine Learning

R Language Programming Basics

R language Data Types

Structures and conditional statements

Introduction to R Studio

Data filtering and selecting

Data Mugging techniques in R

Basic Statistics for Machine Learning

Basic Statistics and Exploratory Analysis

Descriptive summary statistics with Numpy

Summarize continuous and categorical data

Outlier analysis

Introduction to Machine Learning

Overview of Supervised and Unsupervised Machine Learning

Linear Regression

Clustering with K-means

Naive Bayes Classification

Introduction to Neural Networks

Data Processing for Machine Learning

Advanced Data Mugging

Outlier Analysis

Treating for missing values

Normalization vs Standardization of data

Machine Learning Algorithms

Supervised Machine Learning algorithms

K-Nearest Neighbors (KNN) concept and application

Naive Bayes concept and application

Logistic Regression concept and application

Classification Trees concept and application

Unsupervised Machine Learning algorithms

Clustering with K-means concept and application

Hierarchical Clustering concept and application

Building and Training Machine Learning models

Setting up the project with ML workflow.

Data Preprocessing and statistical exploration

Building, Training and evaluation of Machine Learning Model

Hands on case study: Property Estimations with ML

Analysis the case study: Property Estimations

Setting up the project with ML workflow.

Data Preprocessing and statistical exploration

Choosing the Machine Learning Algorithm for this case

Building, Training and evaluation of Machine Learning Model