Complete Data Science using R Programming
Last updated 6/2024
Duration: 14h50m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 5.18 GB
Genre: eLearning | Language: English
The course covers in depth coverage of topics on Statistics | Machine Learning Algorithms | R Programming | Data Science
What you'll learn
Fundamentals of Statistics essential for Machine Learning & Data Science
R Programming - a free, open-source programming language for statistical computing, data analysis, visualization, and machine learning.
Build Models with live cases
Learn Advance Modelling Techniques including Deep Learning.
Requirements
This is a Beginner to Intermediate level course. No Programming experience required. No prior knowledge of Statistics or Data Science is required.
Description
The course is divided into 4 modules and each module is further divided into 6-8 sub sections which covers each topic in detail along with practice assignments.
The four modules are as follows:
1- Basic Statistics- In this module we will go through the statistics which is essential for building models and forms the foundational knowledge. We will cover Data, Scales of measurement, Population & Sample, Measures of Central Tendency, Measures of position, Measures of dispersion, Covaraince, Correlation, Outliers, Noise & Standard error.
2- R Programming- The best way to learn programming, is by doing it. We will get our hands on, on the basic concepts of R and solve assignments in R. We will cover the basics of R, Data Structures & its types and then work with R through Assignments.
3- Modelling- In this module we will learn basics of modelling and understand various algorithms such as Linear regression, Logistic regression, Decision Tree, naive Bayes algorithm, resampling methods. This will then be followed with assignments on each of these topics in R.
4- Advance Modelling- In this module we will deep dive into modelling and will learn some advance algorithms such as Discriminant analysis, Principal Component analysis, Support Vector Machines, Clustering, Association/Market Basket Analysis, Neural Networks and Time series. This will then be followed with assignments on each of these topics in R.
My approach in this course is to explain the theoretical concepts in a way that even a beginner is able to understand and then the learning is reinforced through atleast 1 assignment exercise on each of these topic.
Who this course is for:
Beginners to Intermediate learners of Machine learning/Data Science.
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