Learn Machine Learning Data Mining in Python

Learn Machine Learning & Data Mining in Python, available at $79.99, has an average rating of 4.4, with 146 lectures, 8 quizzes, based on 386 reviews, and has 1235 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who need to use machine learning algorithms in data mining for business implementation. or Anyone wants to learn Machine Learning in Python. or Anyone wants to learn data analysis in PySpark. It is particularly useful for Anyone who need to use machine learning algorithms in data mining for business implementation. or Anyone wants to learn Machine Learning in Python. or Anyone wants to learn data analysis in PySpark.

Enroll now: Learn Machine Learning & Data Mining in Python

Summary

Title: Learn Machine Learning & Data Mining in Python

Price: $79.99

Average Rating: 4.4

Number of Lectures: 146

Number of Quizzes: 8

Number of Published Lectures: 145

Number of Published Quizzes: 8

Number of Curriculum Items: 154

Number of Published Curriculum Objects: 153

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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 Matplotlibare 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. 

Course Curriculum

Chapter 1: Introduction to Data Mining & Machine Learning in Python (Course 1)

Lecture 1: Introduction to Data Mining & Machine Learning in Python (Course 1)

Lecture 2: Course Contents

Lecture 3: Control a pace of a video

Lecture 4: Introduction to Data Mining

Lecture 5: Business Applications of Data Mining

Lecture 6: Data Mining Process Pyramid

Lecture 7: Introduction to Machine Learning

Lecture 8: How Does Machine Learning Work

Lecture 9: Machine Learning Algorithms Types

Lecture 10: Reinforcement Learning overview

Lecture 11: Course Rating

Chapter 2: Introduction to Python Machine Learning Libraries and Google Colab

Lecture 1: Intro to Google Colab

Lecture 2: Login to Google Colab

Lecture 3: Create Lists in Python

Lecture 4: Create Tuples and Dictionaries in Python

Lecture 5: Loops and Functions in Python

Lecture 6: Introduction to Pandas library – 1

Lecture 7: Introduction to Pandas library – 2

Lecture 8: Introduction to NumPy – 1

Lecture 9: Introduction to NumPy – 2

Lecture 10: Introduction to Scikit-learn Library

Chapter 3: Supervised Learning Algorithms

Lecture 1: Introduction to Supervised Learning Algorithms

Lecture 2: Types of Variables

Lecture 3: Introduction to Regression Analysis

Lecture 4: Regression Model Slope

Lecture 5: The Intercept Value

Lecture 6: R-Squared Value

Lecture 7: P-Value

Lecture 8: Simple Linear Regression

Lecture 9: Concepts used in Machine Learning (Important**)

Lecture 10: Use Google Colab as development environment instead of Anaconda

Lecture 11: Import a dataset file in Google Colab

Lecture 12: How to import a dataset file in Google Colab

Lecture 13: Overview on the dataset

Lecture 14: Import Dataset file of simple linear regression

Lecture 15: Create Simple Linear Regression Model in Python-Part 1

Lecture 16: Create Simple Linear Regression Model in Python-Part 2

Lecture 17: Create Simple Linear Regression Model in Python-Part 3

Lecture 18: Create Simple Linear Regression Model in Python-Part 4

Lecture 19: Multiple Linear Regression

Lecture 20: Dummy Variables

Lecture 21: Dummy Variables Trap

Lecture 22: Stepwise Approach

Lecture 23: Assumptions of Multiple Linear Regression

Lecture 24: Overview on the business problem data

Lecture 25: Import the dataset file in Python

Lecture 26: Create Multiple Linear Regression Model in Python-Part 1

Lecture 27: Create Multiple Linear Regression Model in Python-Part 2

Lecture 28: Import numpy

Lecture 29: Create Multiple Linear Regression Model in Python-Part 3

Lecture 30: Polynomial Regression

Lecture 31: Overview on the business problem data

Lecture 32: Import the dataset file in Python

Lecture 33: Create Polynomial Regression Model in Python-Part 1

Lecture 34: Create Polynomial Regression Model in Python-Part 2

Lecture 35: Create Polynomial Regression Model in Python-Part 3

Lecture 36: Course Rating

Lecture 37: Introduction to Classification

Lecture 38: Introduction to Logistic Regression

Lecture 39: Confusion Matrix

Lecture 40: Standard Scaler

Lecture 41: Overview on the business problem data

Lecture 42: Create Logistic Regression Model in Python-Part 1

Lecture 43: Create Logistic Regression Model in Python-Part 2

Lecture 44: KNN Classification Algorithm

Lecture 45: Create KNN Model in Python

Lecture 46: Support Vector Machine (SVM) Classification Algorithm

Lecture 47: Create Support Vector Machine in Python

Lecture 48: Naive Bayes Algorithm Part 1

Lecture 49: Naive Bayes Algorithm Part 2

Lecture 50: Create Naive Bayes Model in Python

Lecture 51: Decision Tree Algorithm

Lecture 52: Create Decision Tree Model in Python

Lecture 53: Random Forest Algorithm

Lecture 54: Create Random Forest Model in Python

Lecture 55: Course Rating

Chapter 4: Unsupervised Learning Algorithms

Lecture 1: Review Unsupervised Learning Algorithms

Lecture 2: Hierarchical Clustering Algorithm

Lecture 3: Dendrogram Diagram Method

Lecture 4: Overview on the business problem data

Lecture 5: Create Hierarchical Clustering Algorithm in Python-1

Lecture 6: Create Hierarchical Clustering Algorithm in Python-2

Lecture 7: K-means Clustering Algorithm

Lecture 8: Using Elbow Method to Determine Optimal Number of Clusters

Lecture 9: Create K-means Clustering Algorithm Model in Python – 1

Lecture 10: Create K-means Clustering Algorithm Model in Python – 2

Lecture 11: Association Rules (Market Basket Analysis)

Lecture 12: Overview on the business problem data

Instructors

Data Science Guide
Data Scientist & SQL Developer

Rating Distribution

1 stars: 7 votes

2 stars: 7 votes

3 stars: 25 votes

4 stars: 96 votes

5 stars: 251 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!