Machine Learning using R and Python
Created by DATAhill Solutions Srinivas Reddy, Last Updated 24Jan2020, Language:English
Machine Learning using R and Python
Machine Learning using R Programming and Python Programming
Created by DATAhill Solutions Srinivas Reddy, Last Updated 24Jan2020, 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 datadriven 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
Preview00:26:30 
Introduction to R Programming
Preview00:42:57 
R Installation & Setting R Environment
Preview00:50:16 
Variables, Operators & Data types
Preview00:53:10 
Structures
00:47:08 
Vectors
01:04:04 
Vector Manipulation & SubSetting
01:06:03 
Constants
00:41:38 
RStudio Installation & Lists Part 1
01:02:20 
Lists Part 2
00:47:44 
List Manipulation, SubSetting & 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
Preview00:41:35 
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 
KNearest 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 
KMeans 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.