Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 48.0 KHz
Language: English | Size: 2.71 GB | Duration: 5h 0m
Learn to create & test Machine Learning & Data Science Models in R from Data Science experts. Code templates included.
What you’ll learn
Basics of statistical modelling
Basics of data science using R and Python
Forecasting and prediction using Data
Data Visualisation
Requirements
No programming experience needed
Description
Are you interested in the field of Data Science and Machine Learning but haven’t had experience in it? Then this course is for you!
This course has been designed by a professional Data Scientist so that I can share my knowledge and industry experience and help you learn the basics of data science algorithms and coding libraries.
This course includes a step-by-step approach to Data Science and Machine Learning. With each lecture, you will develop the mathematical understanding as well as the understanding of necessary libraries to help you ace Data Science interviews and enter into this field.
The course is structured in a very crisp and comprehensive manner to help you understand industry-relevant algorithms. It is structured the following way
Part 1.) Getting started with R
Setting up R
Getting Started with R Studios IDE
Swirl
Part 2.) Introduction to Statistical Measures
Measures of Central Tendencies
Introduction to Data Science using R
Part 3.) Data Processing and Data Visualisation in R
Measures of Dispersions and Outlier Treatment
Missing Value Treatment using R
Data Visualization using R ( boxplots, bubble plots, heat plots, automated-EDA in R)
Part 4.) Building Regression Models in R
Linear Regression Theory
Linear Regression using R
Multivariate Linear Regression Theory
Multivariate Linear Regression using R (Multiple Linear Regression, R-square, Adjusted R-square, p-value, backward selection)
Part 5.) Building Classification Models in R
Classification using Logistic Regression
Logistic Regression and Generalized Linear Models in R & Measures of Accuracy for a Classification Models (AIC, AUC, Confusion Matrix, Precision, and Recall)
Part 6.) Random Forest Models in R
Introduction to decision tree classifier (trees package, Gini index, and tree pruning )
Creating decision tree and Random Forest in R (Random forest package in R, hyper-parameters tuning, visualizing a tree in R)
Building Random Forest Regressors
The course takes you through practical exercises that are based on real-life datasets to help you build models hands-on.
And as additional material, this course includes R code templates which you can download and re-use on your own projects.
Who this course is for
Engineering students
Beginner python and R data analysts
Data science enthusiasts
Business graduates
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