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
  
  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

Having 10+ Years of Experience in Software Industry which includes Development, Support & Training.

My Experience Includes Managing, Processing, Predicting and Analyzing of Large volume of Business Data.

Expertise in Data Management, BI Technologies & Data Science with Data Analytics, Machine Learning, Deep Learning & Artificial Intelligence using R Programming, Python Programming, WEKA and EXCEL.

Having publications and patents in various fields such as machine learning, data security, and data science technologies.

I received my Masters of Technology in Computer Science & Engineering from JNTU.

Professionally, I am a Data Science management consultant with over 8 years of experience in finance, retail, transport and other industries.