Machine Learning using R and Python

Machine Learning using R Programming and Python Programming

DATAhill Solutions Srinivas Reddy

Development, Data Science and AI ML, Machine Learning

Tags - Featured

- Machine Learning using R and Python
  
  83 Lectures
  - Introduction to Machine Learning
    26:30
    Preview
    Resources
    
    Main-content.pdf
  - Introduction to R Programming
    42:57
    Preview
    Resources
    
    Main-content.pdf
  - R Installation & Setting R Environment
    50:16
    Preview
    Resources
    
    Main-content.pdf
  - Variables, Operators & Data types
    53:10
    Preview
    Resources
    
    Main-content.pdf
  - 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
    Resources
    
    Main-content.pdf
  - 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