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Development Data Science Data Analysis

Learn Data Mining and Machine Learning With Python

Learn how to create Machine Learning algorithms in Python and use them in Data Mining
Rating: 4.4 out of 54.4 (305 ratings)
894 students
Created by Data Science Guide
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

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

Course content

5 sections • 84 lectures • 6h 7m total length

  • Preview02:22
  • Preview01:40
  • Control the Pace of a Lesson
    00:44
  • Preview09:25
  • Data Mining Definition
    1 question
  • 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

  • Install Anaconda package
    03:25
  • Introduction to Jupyter
    04:22

  • 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
    00:53
  • Create Simple Linear Regression Model in Python-Part 1
    06:01
  • Create Simple Linear Regression Model in Python-Part 2
    05:17
  • Create Simple Linear Regression Model in Python-Part 3
    03:47
  • Create Simple Linear Regression Model in Python-Part 4
    03:58
  • Create Simple Linear Regression Model in Python-Part 5
    14:55
  • 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
    00:53
  • Create Multiple Linear Regression Model in Python-Part 1
    04:25
  • Create Multiple Linear Regression Model in Python-Part 2
    05:58
  • Create Multiple Linear Regression Model in Python-Part 3
    08:56
  • Create Multiple Linear Regression Model in Python-Part 4
    08:37
  • Polynomial Regression
    02:29
  • Overview on the business problem data
    01:03
  • Create Polynomial Regression Model in Python-Part 1
    06:52
  • Create Polynomial Regression Model in Python-Part 2
    07:35
  • Create Polynomial Regression Model in Python-Part 3
    03:20
  • 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
    08:12
  • Create Logistic Regression Model in Python-Part 2
    05:12
  • KNN Classification Algorithm
    04:11
  • Create KNN Model in Python
    06:14
  • Support Vector Machine (SVM) Classification Algorithm
    03:56
  • Create Support Vector Machine in Python
    06:14
  • Naive Bayes Algorithm Part 1
    04:45
  • Naive Bayes Algorithm Part 2
    06:52
  • Create Naive Bayes Model in Python
    02:35
  • Decision Tree Algorithm
    06:26
  • Create Decision Tree Model in Python
    02:15
  • Random Forest Algorithm
    01:15
  • Create Random Forest Model in Python
    04:09
  • Course Rating
    00:16

  • Review Unsupervised Learning Algorithms
    01:40
  • Hierarchical Clustering Algorithm
    02:58
  • Dendrogram Diagram Method
    04:33
  • Overview on the business problem data
    00:37
  • Create Hierarchical Clustering Algorithm in Python-1
    08:31
  • Create Hierarchical Clustering Algorithm in Python-2
    07:44
  • 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
    07:23
  • Create K-means Clustering Algorithm Model in Python - 2
    03:03
  • Association Rules (Market Basket Analysis)
    09:20
  • Overview on the business problem data
    00:44
  • Create Association Rules (Market Basket Analysis) Model in Python - 1
    06:03
  • Create Association Rules (Market Basket Analysis) Model in Python - 2
    04:44
  • Create Association Rules (Market Basket Analysis) Model in Python - 3
    03:01

  • 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
    05:19
  • Create Artificial Neural Network Model in Python Part-2
    08:02
  • Create Artificial Neural Network Model in Python Part-3
    05:19
  • 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
Sofia Longart
7 courses
6 reviews
Rating: 5.0 out of 57 months 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.

Instructor

Data Science Guide
Data Scientist & SQL Developer
Data Science Guide
  • 4.3 Instructor Rating
  • 1,644 Reviews
  • 3,787 Students
  • 5 Courses

My name is Abraham 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.

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