Machine Learning Using R And Python

BY
Simpliv Learning

Learn machine learning with programming languages of R and Python by enrolling in the Simpliv Learning-offered programme.

Mode

Online

Duration

69 Hours

Fees

$ 9 49

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course overview

Machine Learning Using R And Python course, created by DATAhill Solutions Srinivas Reddy, is an online certificate course for professionals to go through machine learning making use of R and Python. The curriculum provides the learners the mastery of fundamental basics of R and Python and the capacity to write applications including machine learning techniques like a recommendation, classification, regression, and clustering.

Provided by Simpliv Learning, Machine Learning Using R And Python online course helps the participants to broaden their understanding of the machine learning environment and muster up the best practices for the techniques of machine learning. This short programme is designed with the assumption that the enrolled students possess exposure to R, Python, Numpy, pandas, scipy, matplotlib, Windows, and any of the Linux operating system flavors. However, those who are not yet familiar with these topics also can join the course and can kick start the learning from scratch.

Machine Learning Using R And Python certification also offer a 20-day money-back guarantee, course certificate at the end, and lifetime access to the learning materials including the lectures. The learners will have the opportunity to attend the programme with both android and iOS apps. 

The highlights

  • Online course
  • 20-Day Money-Back Guarantee
  • Learn at your own pace
  • Lifetime Access 
  • Certificate on Completion
  • Access on Android and iOS App

Program offerings

  • Certificate on completion
  • Access on android and ios app
  • 83 lectures
  • English videos
  • Certification of completion
  • 69+ hours completion time

Course and certificate fees

Fees information
$ 9  $49
certificate availability

Yes

certificate providing authority

Simpliv Learning

What you will learn

Machine learning R programming Knowledge of python Data science knowledge

By completing the Cybersecurity And Ethical Hacking online certification, the enrolled students will equip themselves with a deep understanding of machine learning with R and Python. Through this course, the learners will have developed the skills to deal with data-driven problems and apply the resolving measures with programming languages such as R, Python, and their packages.

The syllabus

Machine Learning using R and Python

  • Introduction to Machine Learning
  • Introduction to R Programming
  • R Installation & Setting R Environment
  • Variables, Operators & Data types
  • Structures
  • Vectors
  • Vector Manipulation & Sub-Setting
  • Constants
  • RStudio Installation & Lists Part 1
  • Lists Part 2
  • List Manipulation, Sub-Setting & Merging
  • List to Vector & Matrix Part 1
  • Matrix Part 2
  • Matrix Accessing
  • Matrix Manipulation, rep fn & Data Frame
  • Data Frame Accessing
  • Column Bind & Row Bind
  • Merging Data Frames Part 1
  • Merging Data Frames Part 2
  • Melting & Casting
  • Arrays
  • Factors
  • Functions & Control Flow Statements
  • Strings & String Manipulation with Base Package
  • String Manipulation with Stringi Package Part 1
  • String Manipulation with Stringi Package Part 2 & Date and Time Part 1
  • Date and Time Part 2
  • Data Extraction from CSV File
  • Data Extraction from EXCEL File
  • Data Extraction from CLIPBOARD, URL, XML & JSON Files
  • Introduction to DBMS
  • Structured Query Language
  • Data Definition Language Commands
  • Data Manipulation Language Commands
  • Sub Queries & Constraints
  • Aggregate Functions, Clauses & Views
  • Data Extraction from Databases Part 1
  • Data Extraction from Databases Part 2 & DPlyr Package Part 1
  • DPlyr Package Part 2
  • DPlyr Functions on Air Quality Data Set
  • Plyr Package for Data Analysis
  • Tidyr Package with Functions
  • Factor Analysis
  • Prob.Table & CrossTable
  • Statistical Observations Part 1
  • Statistical Observations Part 2
  • Statistical Analysis on Credit Data set
  • Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts
  • Box Plots
  • Histograms & Line Graphs
  • Scatter Plots & Scatter plot Matrices
  • Low-Level Plotting
  • Bar Plot & Density Plot
  • Combining Plots
  • Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot
  • MatPlot, ECDF & BoxPlot with IRIS Data set
  • Additional Box Plot Style Parameters
  • Set.Seed Function & Preparing Data for Plotting
  • QPlot, ViolinPlot, Statistical Methods & Correlation Analysis
  • ChiSquared Test, T-Test, ANOVA
  • Data Exploration and Visualization
  • Machine Learning, Types of ML with Algorithms
  • How Machine Solve Real-Time Problems
  • K-Nearest Neighbor(KNN) Classification
  • KNN Classification with Cancer Data set Part 1
  • KNN Classification with Cancer Data set Part 2
  • Navie Bayes Classification
  • Navie Bayes Classification with SMS Spam Data set & Text Mining
  • WordCloud & Document Term Matrix
  • Train & Evaluate a Model using Navie Bayes
  • MarkDown using Knitr Package
  • Decision Trees
  • Decision Trees with Credit Data set Part 1
  • Decision Trees with Credit Data set Part 2
  • Support Vector Machine, Neural Networks & Random Forest
  • Regression & Linear Regression
  • Multiple Regression
  • Generalized Linear Regression, Non-Linear Regression & Logistic Regression
  • Clustering
  • K-Means Clustering with SNS Data Analysis
  • Association Rules (Market Basket Analysis)
  • Market Basket Analysis using Association Rules with Groceries Dataset
  • Python Libraries for Data Science

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