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Machine Learning using R and Python

Machine Learning using R Programming and Python Programming
  • Introduction to Machine Learning
    26:30
    Preview
  • Introduction to R Programming
    42:57
    Preview
  • R Installation & Setting R Environment
    50:16
    Preview
  • Variables, Operators & Data types
    53:10
    Preview
  • Structures
    47:08
  • Vectors
    04:04
  • Vector Manipulation & Sub-Setting
    06:03
  • Constants
    41:38
  • RStudio Installation & Lists Part 1
    02:20
  • Lists Part 2
    47:44
  • List Manipulation, Sub-Setting & Merging
    45:01
  • List to Vector & Matrix Part 1
    49:52
  • Matrix Part 2
    44:02
  • Matrix Accessing
    48:26
  • Matrix Manipulation, rep fn & Data Frame
    56:08
  • Data Frame Accessing
    54:01
  • Column Bind & Row Bind
    50:32
  • Merging Data Frames Part 1
    50:04
  • Merging Data Frames Part 2
    54:26
  • Melting & Casting
    52:55
  • Arrays
    43:50
  • Factors
    50:53
  • Functions & Control Flow Statements
    40:27
  • Strings & String Manipulation with Base Package
    53:22
  • String Manipulation with Stringi Package Part 1
    58:33
  • String Manipulation with String Package Part 2 & Date and Time Part 1
    48:13
  • Date and Time Part 2
    53:19
  • Data Extraction from CSV File
    42:02
  • Data Extraction from EXCEL File
    50:40
  • Data Extraction from CLIPBOARD, URL, XML & JSON Files
    50:04
  • Introduction to DBMS
    50:22
  • Structured Query Language
    41:35
    Preview
  • Data Definition Language Commands
    02:24
  • Data Manipulation Language Commands
    47:29
  • Sub Queries & Constraints
    16:07
  • Aggregate Functions, Clauses & Views
    07:21
  • Data Extraction from Databases Part 1
    52:31
  • Data Extraction from Databases Part 2 & DPlyr Package Part 1
    52:39
  • DPlyr Package Part 2
    51:36
  • DPlyr Functions on Air Quality Data Set
    57:01
  • Plyr Package for Data Analysis
    46:51
  • Tidyr Package with Functions
    50:48
  • Factor Analysis
    57:11
  • Prob.Table & CrossTable
    50:22
  • Statistical Observations Part 1
    51:48
  • Statistical Observations Part 2
    40:35
  • Statistical Analysis on Credit Data set
    00:29
  • Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts
    59:20
  • Box Plots
    54:38
  • Histograms & Line Graphs
    45:26
  • Scatter Plots & Scatter plot Matrices
    03:47
  • Low Level Plotting
    56:01
  • Bar Plot & Density Plot
    46:31
  • Combining Plots
    35:37
  • Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot
    51:07
  • MatPlot, ECDF & BoxPlot with IRIS Data set
    02:55
  • Additional Box Plot Style Parameters
    01:41
  • Set.Seed Function & Preparing Data for Plotting
    09:42
  • QPlot, ViolinPlot, Statistical Methods & Correlation Analysis
    59:26
  • ChiSquared Test, T Test, ANOVA
    54:42
  • Data Exploration and Visualization
    51:00
  • Machine Learning, Types of ML with Algorithms
    04:53
  • How Machine Solve Real Time Problems
    43:33
  • K-Nearest Neighbor(KNN) Classification
    07:45
  • KNN Classification with Cancer Data set Part 1
    03:15
  • KNN Classification with Cancer Data set Part 2
    43:12
  • Navie Bayes Classification
    43:53
  • Navie Bayes Classification with SMS Spam Data set & Text Mining
    58:43
  • WordCloud & Document Term Matrix
    56:39
  • Train & Evaluate a Model using Navie Bayes
    11:40
  • MarkDown using Knitr Package
    02:15
  • Decision Trees
    57:16
  • Decision Trees with Credit Data set Part 1
    47:03
  • Decision Trees with Credit Data set Part 2
    45:11
  • Support Vector Machine, Neural Networks & Random Forest
    46:50
  • Regression & Linear Regression
    44:04
  • Multiple Regression
    48:24
  • Generalized Linear Regression, Non Linear Regression & Logistic Regression
    35:37
  • Clustering
    29:04
  • K-Means Clustering with SNS Data Analysis
    06:18
  • Association Rules (Market Basket Analysis)
    39:33
  • Market Basket Analysis using Association Rules with Groceries Dataset
    56:19
  • Python Libraries for Data Science
    22:32

Description

This course has been prepared for professionals aspiring to learn the basics of R and Python and develop applications involving machine learning techniques such as recommendation, classification, regression and clustering.

Through this course, you will learn to solve data-driven problems and implement your solutions using the powerful yet simple programming language like R and Python and its packages.

After completing this course, you will gain a broad picture of the machine learning environment and the best practices for machine learning techniques.

Who this course is for:

  • All graduates or pursuing students

What Will I Get ?

  • This course has been prepared for professionals aspiring to learn the basics of R and Python to develop applications involving machine learning techniques such as recommendation, classification, and clustering. Through this course, you will learn to solve

Requirements

  • Before you start proceeding with this course, we assume that you have a prior exposure to R packages and Python, Numpy, pandas, scipy, matplotlib, Windows and any of the Linux operating system flavors. If you are new to any of these concepts, here you can
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Machine Learning using R and Python
This Course Includes :

69.5 hours

83 Lectures

Lifetime Access

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