Machine Learning Using R and Python
Learn Machine Learning with R and Python: The Complete Guide
Updated on Apr, 2024
Language - English
Lectures -84
Resources -83
Duration -69.5 hours
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Course Description
Machine Learning Using R and Python course covers the fundamentals of machine learning with R and Python. You'll learn how to use both languages to solve real-world machine-learning problems in this project-based course.
Machine Learning Using R and Python Overview
This course is designed for anyone who wants to learn how to use R and Python for machine learning. Machine learning is a powerful tool that can be used to solve a wide variety of problems, from predicting customer behavior to detecting fraud.
R and Python are two of the most popular programming languages for machine learning, and they offer a wide range of tools and libraries to help you get started. This course covers various topics including Data preprocessing, Machine learning algorithms, Model evaluation, and Model deployment.
Goals
What will you learn in this course:
Use R and Python to solve real-world machine-learning problems.
Understand the different types of machine learning algorithms and how to choose the right one for your problem.
Train and evaluate machine learning models.
Deploy machine learning models to production.
Prerequisites
What are the prerequisites for this course?
Basic knowledge of programming is required. No prior knowledge of R or Python is necessary.
Curriculum
Check out the detailed breakdown of what’s inside the course
Machine Learning using R and Python
83 Lectures
-
Introduction to Machine Learning
26:30
26:30
-
Introduction to R Programming
42:57
42:57
-
R Installation & Setting R Environment
50:16
50:16
-
Variables, Operators & Data types
53:10
53:10
-
Structures
47:08
47:08
-
Vectors
01:04:04
01:04:04
-
Vector Manipulation & Sub-Setting
01:06:03
01:06:03
-
Constants
41:38
41:38
-
RStudio Installation & Lists Part 1
01:02:20
01:02:20
-
Lists Part 2
47:44
47:44
-
List Manipulation, Sub-Setting & Merging
45:01
45:01
-
List to Vector & Matrix Part 1
49:52
49:52
-
Matrix Part 2
44:02
44:02
-
Matrix Accessing
48:26
48:26
-
Matrix Manipulation, rep fn & Data Frame
56:08
56:08
-
Data Frame Accessing
54:01
54:01
-
Column Bind & Row Bind
50:32
50:32
-
Merging Data Frames Part 1
50:04
50:04
-
Merging Data Frames Part 2
54:26
54:26
-
Melting & Casting
52:55
52:55
-
Arrays
43:50
43:50
-
Factors
50:53
50:53
-
Functions & Control Flow Statements
40:27
40:27
-
Strings & String Manipulation with Base Package
53:22
53:22
-
String Manipulation with Stringi Package Part 1
58:33
58:33
-
String Manipulation with String Package Part 2 & Date and Time Part 1
48:13
48:13
-
Date and Time Part 2
53:19
53:19
-
Data Extraction from CSV File
42:02
42:02
-
Data Extraction from EXCEL File
50:40
50:40
-
Data Extraction from CLIPBOARD, URL, XML & JSON Files
50:04
50:04
-
Introduction to DBMS
50:22
50:22
-
Structured Query Language
41:35
41:35
-
Data Definition Language Commands
01:02:24
01:02:24
-
Data Manipulation Language Commands
47:29
47:29
-
Sub Queries & Constraints
16:07
16:07
-
Aggregate Functions, Clauses & Views
07:21
07:21
-
Data Extraction from Databases Part 1
52:31
52:31
-
Data Extraction from Databases Part 2 & DPlyr Package Part 1
52:39
52:39
-
DPlyr Package Part 2
51:36
51:36
-
DPlyr Functions on Air Quality Data Set
57:01
57:01
-
Plyr Package for Data Analysis
46:51
46:51
-
Tidyr Package with Functions
50:48
50:48
-
Factor Analysis
57:11
57:11
-
Prob.Table & CrossTable
50:22
50:22
-
Statistical Observations Part 1
51:48
51:48
-
Statistical Observations Part 2
40:35
40:35
-
Statistical Analysis on Credit Data set
01:00:29
01:00:29
-
Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts
59:20
59:20
-
Box Plots
54:38
54:38
-
Histograms & Line Graphs
45:26
45:26
-
Scatter Plots & Scatter plot Matrices
01:03:47
01:03:47
-
Low Level Plotting
56:01
56:01
-
Bar Plot & Density Plot
46:31
46:31
-
Combining Plots
35:37
35:37
-
Analysis with ScatterPlot, BoxPlot, Histograms, Pie Charts & Basic Plot
51:07
51:07
-
MatPlot, ECDF & BoxPlot with IRIS Data set
01:02:55
01:02:55
-
Additional Box Plot Style Parameters
01:01:41
01:01:41
-
Set.Seed Function & Preparing Data for Plotting
01:09:42
01:09:42
-
QPlot, ViolinPlot, Statistical Methods & Correlation Analysis
59:26
59:26
-
ChiSquared Test, T Test, ANOVA
54:42
54:42
-
Data Exploration and Visualization
51:00
51:00
-
Machine Learning, Types of ML with Algorithms
01:04:53
01:04:53
-
How Machine Solve Real Time Problems
43:33
43:33
-
K-Nearest Neighbor(KNN) Classification
01:07:45
01:07:45
-
KNN Classification with Cancer Data set Part 1
01:03:15
01:03:15
-
KNN Classification with Cancer Data set Part 2
43:12
43:12
-
Navie Bayes Classification
43:53
43:53
-
Navie Bayes Classification with SMS Spam Data set & Text Mining
58:43
58:43
-
WordCloud & Document Term Matrix
56:39
56:39
-
Train & Evaluate a Model using Navie Bayes
01:11:40
01:11:40
-
MarkDown using Knitr Package
01:02:15
01:02:15
-
Decision Trees
57:16
57:16
-
Decision Trees with Credit Data set Part 1
47:03
47:03
-
Decision Trees with Credit Data set Part 2
45:11
45:11
-
Support Vector Machine, Neural Networks & Random Forest
46:50
46:50
-
Regression & Linear Regression
44:04
44:04
-
Multiple Regression
48:24
48:24
-
Generalized Linear Regression, Non Linear Regression & Logistic Regression
35:37
35:37
-
Clustering
29:04
29:04
-
K-Means Clustering with SNS Data Analysis
01:06:18
01:06:18
-
Association Rules (Market Basket Analysis)
39:33
39:33
-
Market Basket Analysis using Association Rules with Groceries Dataset
56:19
56:19
-
Python Libraries for Data Science
22:32
22:32
Instructor Details
DATAhill Solutions Srinivas Reddy
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