Learn Machine Learning & Data Mining in Python

Name: Learn Machine Learning & Data Mining in Python
Rating: 4.4 (395 reviews)

Learn Building Machine Learning & Deep Learning Models in Python, and use the Results in Data Mining Analyses

Created byData Science Guide

Last updated 8/2025

English

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
Learn data analysis in PySpark

Course content

6 sections • 146 lectures • 8h 44m total length

Introduction to Data Mining & Machine Learning in Python (Course 1)2:32
Course Contents1:40
Control a pace of a video0:44
Introduction to Data Mining2:02
Data Mining Definition
The Relationship between Data Mining and Machine Learning0:22
Business Applications of Data Mining4:19
Data Mining Process Pyramid1:55
Introduction to Machine Learning2:01
Machine Leaning Sub-fields.
How Does Machine Learning Work3:46
Train and Test Sets.
Machine Learning Algorithms Types3:32
Machine Leaning Types
Reinforcement Learning overview2:05
Course Rating0:16

Introduction to Supervised Learning Algorithms1:10
Types of Variables2:05
Data Types
Introduction to Regression Analysis5:54
Regression Model
Regression Model Slope5:49
Regression Slope
The Intercept Value0:34
The Intercept Value
R-Squared Value6:51
P-Value3:43
Simple Linear Regression0:49
Concepts used in Machine Learning (Important**)0:06
Use Google Colab as development environment instead of Anaconda0:14
Import a dataset file in Google Colab4:00
How to import a dataset file in Google Colab0:35
Overview on the dataset0:53
Import Dataset file of simple linear regression1:35
Create Simple Linear Regression Model in Python-Part 15:17
Create Simple Linear Regression Model in Python-Part 23:47
Create Simple Linear Regression Model in Python-Part 33:58
Create Simple Linear Regression Model in Python-Part 48:00
Multiple Linear Regression2:41
Dummy Variables4:11
Dummy Variables Trap0:16
Stepwise Approach5:48
Assumptions of Multiple Linear Regression7:29
Overview on the business problem data0:53
Import the dataset file in Python2:19
Create Multiple Linear Regression Model in Python-Part 14:15
Create Multiple Linear Regression Model in Python-Part 28:56
Import numpy0:08
Create Multiple Linear Regression Model in Python-Part 38:37
Polynomial Regression2:29
Overview on the business problem data1:03
Import the dataset file in Python1:21
Create Polynomial Regression Model in Python-Part 14:43
Create Polynomial Regression Model in Python-Part 27:35
Create Polynomial Regression Model in Python-Part 33:20
Course Rating0:16
Introduction to Classification4:07
Introduction to Logistic Regression7:59
Confusion Matrix4:09
Standard Scaler2:52
Overview on the business problem data1:27
Create Logistic Regression Model in Python-Part 16:40
Create Logistic Regression Model in Python-Part 25:12
KNN Classification Algorithm4:11
Create KNN Model in Python6:14
Support Vector Machine (SVM) Classification Algorithm3:56
Create Support Vector Machine in Python6:14
Naive Bayes Algorithm Part 14:45
Naive Bayes Algorithm Part 26:52
Create Naive Bayes Model in Python2:35
Decision Tree Algorithm6:26
Create Decision Tree Model in Python2:15
Random Forest Algorithm1:15
Create Random Forest Model in Python4:09
Course Rating0:16
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Review Unsupervised Learning Algorithms1:40
Hierarchical Clustering Algorithm2:58
Dendrogram Diagram Method4:33
Overview on the business problem data0:37
Create Hierarchical Clustering Algorithm in Python-18:31
Create Hierarchical Clustering Algorithm in Python-27:44
K-means Clustering Algorithm3:49
Using Elbow Method to Determine Optimal Number of Clusters9:38
Create K-means Clustering Algorithm Model in Python - 17:23
Create K-means Clustering Algorithm Model in Python - 23:03
Association Rules (Market Basket Analysis)9:20
Overview on the business problem data0:44
Create Association Rules (Market Basket Analysis) Model in Python - 16:03
Create Association Rules (Market Basket Analysis) Model in Python - 24:44
Create Association Rules (Market Basket Analysis) Model in Python - 33:01

Introduction to Deep Learning7:20
Use Deep Learning in Data Mining3:58
Cost (Loss) Functions4:42
Activation Functions6:19
How Does Deep Learning Work6:07
Gradient Descent Process4:52
Types of Gradient Descent Functions7:52
What is Tensorflow1:39
Introduction to the Deep Learning Problem and Dataset1:05
Create Artificial Neural Network Model in Python Part-15:19
Create Artificial Neural Network Model in Python Part-28:02
Create Artificial Neural Network Model in Python Part-35:19
Course Rating0:16

What is Statistics1:30
Sample And Population1:15
Descriptive and Inferential Statistics1:47
Data types3:54
Visualize Data2:00
Histogram4:38
Central Tendency Measures3:07
Variability Measures3:16
Calculate Central and Variability Measures (Practical)3:09
Symmetry and skewness in data1:47
Correlation and Covariance4:37
Introduction to Inferential Statistics2:11
Discrete Probability Distributions3:15
Normal Distribution4:55
Variable standardization2:27
Variable standardization Demo0:51
Introduction to Central Limit Theorem3:38
Estimators1:27
Introduction to Confidence Interval5:04
Calculate Confidence Interval for one Sample with a Known Population Variance5:49
Introduction to the Business Problem2:19
Calculate Confidence Interval in Excel8:12
t - Distribution3:52
Calculate Confidence Interval for one Sample with a Unknown Population Variance3:45
Reduce Margin of Error2:23
Confidence Interval for two Dependent Samples1:14
Calculate Confidence Interval for two Dependent Samples in Excel4:17
Confidence Interval for two Independent Samples with a Known Population Variance2:56
Calculate Confidence Interval for two Independent Samples Known Var in Excel4:31
Confidence Interval for two Independent Samples Unknown Population Variance5:01
What is a Statistical Hypothesis2:29
Types of Hypotheses3:54
P-Value2:22
Link to z-value Calculator0:02
Testing a Hypothesis for one Sample, Variance is Known1:42
Testing the Hypothesis in Excel4:08
Testing a Hypothesis for one Sample, Variance is Unknown2:58
Testing a Hypothesis for two Dependent Samples4:40
Link to t-value Calculator0:01
Testing a Hypothesis for two Independent Samples, Variance is Known3:58
Testing a Hypothesis for two Independent Samples, Variance is Unknown3:11

Requirements

Basic knowledge in Statistics and operating systems

Description

If you seek to learn how to create machine learning models and use them in data mining process, this course is for you. You will understand in this course what is data mining process and how to implement machine learning algorithms in data mining. Moreover, you will learn in details how deep learning does work and how to build a deep learning model to solve a business problem. In the beginning of the course, you will understand the basic concepts of data mining and learn about the business fields where data mining is implemented.

After that you will learn how to create machine learning models in Python using several data science libraries developed especially for this purpose. NumPy, Pandas, and Matplotlib are some examples of these models that you will learn how to import and use to create machine learning algorithms in Python. You will learn typing codes in Python from scratch without the need to have a pervious knowledge in coding. You will be familiar with the essential code needed to build machine learning models. This course is designed to provide you with the knowledge you need in a simple and straightforward way to smooth the learning process. You will build 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.
Anyone wants to learn Machine Learning in Python.
Anyone wants to learn data analysis in PySpark.

Learn Machine Learning & Data Mining in Python

What you'll learn

Explore related topics

Course content

Introduction to Data Mining & Machine Learning in Python (Course 1)12 lectures • 25min

Introduction to Python Machine Learning Libraries and Google Colab10 lectures • 31min

Supervised Learning Algorithms55 lectures • 3hr 23min

Unsupervised Learning Algorithms15 lectures • 1hr 14min

Deep Learning13 lectures • 1hr 3min

Appendix: Statistics Overview41 lectures • 2hr 9min

Requirements

Description

Who this course is for: