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Machine Learning using R and Python

Created by DATAhill Solutions Srinivas Reddy, Last Updated 24-Jan-2020, Language:English

Machine Learning using R and Python

Machine Learning using R Programming and Python Programming

Created by DATAhill Solutions Srinivas Reddy, Last Updated 24-Jan-2020, Language:English

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

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

Course Content

  • Machine Learning using R and Python

    83 Lectures 69:41:41

    • Introduction to Machine Learning

      Preview
      00:26:30

    • Introduction to R Programming

      Preview
      00:42:57

    • R Installation & Setting R Environment

      Preview
      00:50:16

    • Variables, Operators & Data types

      Preview
      00:53:10

    • Structures

      00:47:08

    • Vectors

      01:04:04

    • Vector Manipulation & Sub-Setting

      01:06:03

    • Constants

      00:41:38

    • RStudio Installation & Lists Part 1

      01:02:20

    • Lists Part 2

      00:47:44

    • List Manipulation, Sub-Setting & Merging

      00:45:01

    • List to Vector & Matrix Part 1

      00:49:52

    • Matrix Part 2

      00:44:02

    • Matrix Accessing

      00:48:26

    • Matrix Manipulation, rep fn & Data Frame

      00:56:08

    • Data Frame Accessing

      00:54:01

    • Column Bind & Row Bind

      00:50:32

    • Merging Data Frames Part 1

      00:50:04

    • Merging Data Frames Part 2

      00:54:26

    • Melting & Casting

      00:52:55

    • Arrays

      00:43:50

    • Factors

      00:50:53

    • Functions & Control Flow Statements

      00:40:27

    • Strings & String Manipulation with Base Package

      00:53:22

    • String Manipulation with Stringi Package Part 1

      00:58:33

    • String Manipulation with String Package Part 2 & Date and Time Part 1

      00:48:13

    • Date and Time Part 2

      00:53:19

    • Data Extraction from CSV File

      00:42:02

    • Data Extraction from EXCEL File

      00:50:40

    • Data Extraction from CLIPBOARD, URL, XML & JSON Files

      00:50:04

    • Introduction to DBMS

      00:50:22

    • Structured Query Language

      Preview
      00:41:35

    • Data Definition Language Commands

      01:02:24

    • Data Manipulation Language Commands

      00:47:29

    • Sub Queries & Constraints

      00:16:07

    • Aggregate Functions, Clauses & Views

      00:07:21

    • Data Extraction from Databases Part 1

      00:52:31

    • Data Extraction from Databases Part 2 & DPlyr Package Part 1

      00:52:39

    • DPlyr Package Part 2

      00:51:36

    • DPlyr Functions on Air Quality Data Set

      00:57:01

    • Plyr Package for Data Analysis

      00:46:51

    • Tidyr Package with Functions

      00:50:48

    • Factor Analysis

      00:57:11

    • Prob.Table & CrossTable

      00:50:22

    • Statistical Observations Part 1

      00:51:48

    • Statistical Observations Part 2

      00:40:35

    • Statistical Analysis on Credit Data set

      01:00:29

    • Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts

      00:59:20

    • Box Plots

      00:54:38

    • Histograms & Line Graphs

      00:45:26

    • Scatter Plots & Scatter plot Matrices

      01:03:47

    • Low Level Plotting

      00:56:01

    • Bar Plot & Density Plot

      00:46:31

    • Combining Plots

      00:35:37

    • Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot

      00:51:07

    • MatPlot, ECDF & BoxPlot with IRIS Data set

      01:02:55

    • Additional Box Plot Style Parameters

      01:01:41

    • Set.Seed Function & Preparing Data for Plotting

      01:09:42

    • QPlot, ViolinPlot, Statistical Methods & Correlation Analysis

      00:59:26

    • ChiSquared Test, T Test, ANOVA

      00:54:42

    • Data Exploration and Visualization

      00:51:00

    • Machine Learning, Types of ML with Algorithms

      01:04:53

    • How Machine Solve Real Time Problems

      00:43:33

    • K-Nearest Neighbor(KNN) Classification

      01:07:45

    • KNN Classification with Cancer Data set Part 1

      01:03:15

    • KNN Classification with Cancer Data set Part 2

      00:43:12

    • Navie Bayes Classification

      00:43:53

    • Navie Bayes Classification with SMS Spam Data set & Text Mining

      00:58:43

    • WordCloud & Document Term Matrix

      00:56:39

    • Train & Evaluate a Model using Navie Bayes

      01:11:40

    • MarkDown using Knitr Package

      01:02:15

    • Decision Trees

      00:57:16

    • Decision Trees with Credit Data set Part 1

      00:47:03

    • Decision Trees with Credit Data set Part 2

      00:45:11

    • Support Vector Machine, Neural Networks & Random Forest

      00:46:50

    • Regression & Linear Regression

      00:44:04

    • Multiple Regression

      00:48:24

    • Generalized Linear Regression, Non Linear Regression & Logistic Regression

      00:35:37

    • Clustering

      00:29:04

    • K-Means Clustering with SNS Data Analysis

      01:06:18

    • Association Rules (Market Basket Analysis)

      00:39:33

    • Market Basket Analysis using Association Rules with Groceries Dataset

      00:56:19

    • Python Libraries for Data Science

      00:22:32

DATAhill Solutions Srinivas Reddy

Data Scientist

Mr. Srinivas Reddy is Founder & MD of DATAhill Solutions

He is Research Scholar (Ph.D) on Artificial Intelligence & Machine Learning

He Received Masters of Technology in Computer Science & Engineering from JNTU, MICROSOFT Certified Professional, IBM Certified Professional & Certified from IIT Kanpur & IIT Ropar.

Having 10+ Years of Experience in Software & Training.

His Experience includes Managing, Data Processing, Data Cleaning, Predicting and Analyzing of Large volume of Business Data.

Expertise in Data Science, Data Analytics, Machine Learning, Deep Learning, Artificial Intelligence, Python, R, Weka, Data Management & BI Technologies.

Having Patents and Publications in Various Fields such as Artificial Intelligence, Machine Learning and Data Science Technologies.

Professionally, He is Data Science Management Consultant with over 7+ years of Experience in Finance, Retail, Transport and other Industries.


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