Machine Learning Using R and Python

person icon DATAhill Solutions Srinivas Reddy

4.5 ★★★★ ★

Machine Learning Using R and Python

Name: Machine Learning Course Using R and Python
Rating: 4.5 (228 reviews)
Author: DATAhill Solutions Srinivas Reddy

Learn Machine Learning with R and Python: The Complete Guide

updated on icon Updated on Oct, 2024

language icon Language - English

person icon DATAhill Solutions Srinivas Reddy

English [CC]

category icon Development ,Data Science,Machine Learning

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.

Course 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

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 in production.

Prerequisites

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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