Machine learning path with R
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Machine learning path with R

  • Learn Machine learning path with R

Created by Selva Prabhakaran

  • English

  • English captions

What you will learn

01

Complete R programming skills

02

Data analysis to
find patterns

03

Perform statistical significance tests

04

Perform and interpret statistical tests

05

Build end-to-end ML models

06

Create and present actionable insights

07

Domain based data science strategies

08

Derive high business impact insights

Courses in the learning path (23+ Hours)

    #1: Base R-Programming (6h)

    First you will learn to write your own R code, perform basic programming tasks. Master key data structures - vectors, lists, dataframes. Understand the core programming constructs. Create full featured plots for data analysis using Base Graphics. Finally get enough coding practice during this course!

    #2: Dplyr for data wrangling (2h)

    You will move to advanced coding and data wrangling in R based on the tidyverse using the dplyr package. You will learn to use elegant pipe syntax provided by magrittr package.  Master data manipulation verbs. Summarize, group and join data for analysis.

    #3: Wrangling data with Data table (1.5h)

    You will move on to master the data.table package. You will learn to master advanced capabilities for fast data manipulation. Apply techniques to make your R code run super fast. Do fast data imports and create pivot tables. Get comfortable with wrangling data.

    #4: GGPlot2 visualization for data analysis (2h)

    You will start creating professional plots using GGPlot2 package. Generate box plots, scatterplots, dual-axis, plots. Learn the syntax for labels, themes, annotations, legends. Understand the underlying structure behind plots.

    #5: Statistical foundations for Machine Learning (4h)

    Gain mastery over the statistics for ML. Grasp core statistics concepts such as the law of large numbers, central limit theorem, normal distribution. Master statistical significance tests and apply on use cases.

    Read More

    #6: Regression Modeling (8h)

    Master core regression based ML algorithms in R.

    Work on multiple features and real data set, and explore alternate approaches to modeling and eval

    # Includes Industrial Projects

    Solve industrial projects in regression modeling

    Predict customer purchase propensity in banking domain

    Predict US (California) institutes performance, in education domain

    Requirements

    • Courses Page1 High school maths
    • Courses Page1 Intent to learn

    Who should attend this course?

    • Data Science Aspirants

    • Data Science Professionals

    • Software/Data engineers interested in quantitative analysis

    • Statistics students and professionals

    • Data analysts, economists, researchers

    Instructor

    Selva Prabhakaran Principal Data Scientist

    My name is Selva, and I am super excited to mentor you on this project!

    I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I’ve deployed 20+ global products. I’m also the Founder & Chief Author of Machine Learning Plus, which has over 4M annual readers.

    I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I’ve put up the series of courses and projects with detailed explanations – just like an on the job experience. Hope you love it!

    • 4.5+Instructor rating

    • 200+ reviews

    • 10K+students

    • 15+ Courses

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