Learn Data Mining and Machine Learning With Python

Learn how to create Machine Learning algorithms in Python and use them in Data Mining

(272 ratings)

726 students

Created by Data Science Guide

Last updated 8/2020

English

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What you'll learn

Learn everything about Data Mining and its applications
Understand Machine Learning and its connection with Data Mining
Learn all Machine Learning algorithms, their types, and their usage in business
Learn how to implement Machine Learning algorithms in different business scenarios
Learn how to install and use Python programming language to create machine learning algorithms in a simple way
Learn how to import your data sets into Python and make required cleaning before creating the algorithms
Learn how to interpret the results of each algorithms and compare them with each other to choose the optimum one
Learn how to create graphs in Pythons, such as scattered and regression graphs and use them in your analyses

5 sections • 82 lectures • 6h 33m total length

Introduction7 lectures • 28min

Introduction to Course
Preview02:20

Course Contents
Preview01:47

Introduction to Data Mining
Preview09:25

Data Mining Definition
1 question

Introduction to Machine Learning
Preview02:39

Machine Leaning Sub-fields.
1 question

How Does Machine Learning Work
03:36

Train and Test Sets.
1 question

Machine Learning Algorithms Types
07:49

Machine Leaning Types
1 question

Course Rating
00:16

Setup Programming Environment1 lecture • 6min

Supervised Learning Algorithms47 lectures • 3hr 56min

Introduction to Supervised Learning Algorithms
01:10

Types of Variables
02:17

Data Types
1 question

Introduction to Regression Model
05:54

Regression Model
1 question

Regression Model Slope
06:14

Regression Slope
1 question

The Intercept Value
1 question

R-Squared
07:00

P-Value
03:43

Simple Linear Regression
00:49

Concepts used in Machine Learning (Important**)
00:06

Overview on the dataset
01:11

Create Simple Linear Regression Model in Python-Part 1
04:26

Create Simple Linear Regression Model in Python-Part 2
12:45

Create Simple Linear Regression Model in Python-Part 3
05:51

Create Simple Linear Regression Model in Python-Part 4
06:11

Multiple Linear Regression
02:41

Dummy Variables
04:11

Dummy Variables Trap
00:16

Step-wise Approach
05:48

Assumptions of Multiple Linear Regression
07:29

Overview on the business problem data
01:02

Create Multiple Linear Regression Model in Python-Part 1
15:01

Create Multiple Linear Regression Model in Python-Part 2
11:21

Create Multiple Linear Regression Model in Python-Part 3
09:59

Create Multiple Linear Regression Model in Python-Part 4
06:37

Polynomial Regression
02:33

Overview on the business problem data
01:03

Create Polynomial Regression Model in Python-Part 1
08:00

Create Polynomial Regression Model in Python-Part 2
11:24

Course Rating
00:16

Introduction to Classification
04:07

Introduction to Logistic Regression
07:59

Confusion Matrix
04:09

Standard Scaler
02:52

Overview on the business problem data
01:27

Create Logistic Regression Model in Python-Part 1
10:04

Create Logistic Regression Model in Python-Part 2
06:00

KNN Classification Algorithm
04:04

Create KNN Model in Python
06:56

Support Vector Machine (SVM) Classification Algorithm
03:39

Create Support Vector Machine in Python
06:40

Naive Bayes Algorithm Part 1
04:45

Naive Bayes Algorithm Part 2
06:52

Create Naive Bayes Model in Python
03:30

Decision Tree Algorithm
06:26

Create Decision Tree Model in Python
03:19

Random Forest Algorithm
01:15

Create Random Forest Model in Python
05:53

Course Rating
00:16

Unsupervised Learning Algorithms15 lectures • 1hr 21min

Review Unsupervised Learning Algorithms
01:40

Hierarchical Clustering Algorithm
03:14

Dendrogram Diagram Method
06:32

Overview on the business problem data
00:37

Create Hierarchical Clustering Algorithm in Python-1
12:07

Create Hierarchical Clustering Algorithm in Python-2
06:35

K-means Clustering Algorithm
03:49

Using Elbow Method to Determine Optimal Number of Clusters
09:38

Create K-means Clustering Algorithm Model in Python - 1
07:49

Create K-means Clustering Algorithm Model in Python - 2
03:00

Association Rules (Market Basket Analysis)
09:20

Overview on the business problem data
01:44

Create Association Rules (Market Basket Analysis) Model in Python - 1
08:15

Create Association Rules (Market Basket Analysis) Model in Python - 2
04:00

Create Association Rules (Market Basket Analysis) Model in Python - 3
02:23

Deep Learning12 lectures • 44min

Introduction to Deep Learning
05:54

Use Deep Learning in Classification
02:13

How Does Deep Learning Work
04:58

Activation Functions
06:04

What is Tensorflow
02:15

Introduction to the Deep Learning Problem and Dataset
01:05

Create Artificial Neural Network Model in Python Part-1
05:53

Create Artificial Neural Network Model in Python Part-2
07:41

Create Artificial Neural Network Model in Python Part-3
05:04

The Newer Version of Keras Python code to Create the Model and Add the Layers
00:33

Create Artificial Neural Network Model in Python Part-4
01:45

Course Rating
00:16

Requirements

Basic knowledge in Statistics and operating systems

Description

If you need to learn how to understand and create Machine Learning models used to solve business problems, this course is for you. You will learn in this course everything you need about Data Mining process, Machine Learning and how to implement Machine Learning algorithms in Data Mining. This course was designed to provide information in a simple and straight forward way so ease learning methods. You will from scratch and keep building your knowledge step by step until you become familiar with the most used Machine Learning algorithms.

Who this course is for:

Anyone who need to use machine learning algorithms in data mining for business implementation

Featured review

Sofia Longart

6 courses

5 reviews

a month ago

This is a very comprehensive, easy to follow and understand course that I love to take and feel so passionnés about the topics being taught there. I'm very lucky I'm learning valuable information and skills ou of this course. Highly recommended.

Data Science Guide

Data Scientist & SQL Developer

4.5 Instructor Rating
1,416 Reviews
3,124 Students
5 Courses

My name is Ridha Joudah

I have worked in IT and Data Science for more than 15 years. After completed my bachelor’s in computer science, I worked Database Administrator in one of the engineering companies. I have obtained several certificates from Microsoft like MCSE, MCDBA and MCSA. After several years of working in IT, I started focusing on Data Science field and learning SQL in depth to enhance my business data analysis skills. I have worked Data Analyst in several companies. Over several years of working in this field I mastered using several analytical tools, such as: R, SAS, SQL, Tableau, and Excel. As I love and enjoy working at data science I pursued my study in this major till I obtained my master’s degree in Business Analytics from University of North Texas.

I love teaching Data Science, So I decided to create several courses in this field to share my knowledge with others. I tried to present something new in my classes. Instead of keep repeating same materials and curriculum which are already existing everywhere, I added materials simulate real business scenario. I included examples that based on real business cases to learn something practical rather than learning everything about basics. In other words, I tried to create shortcuts for practical learning to focus on what is really needed in the work field.

This course includes:

Training 5 or more people?

Learn Data Mining and Machine Learning With Python

What you'll learn

Course content

Introduction7 lectures • 28min

Setup Programming Environment1 lecture • 6min

Supervised Learning Algorithms47 lectures • 3hr 56min

Unsupervised Learning Algorithms15 lectures • 1hr 21min

Deep Learning12 lectures • 44min

Requirements

Description

Who this course is for:

Featured review

Instructor