Experimental Machine Learning Data Mining- Weka, MOA R

Experimental Machine Learning & Data Mining: Weka, MOA & R, available at $54.99, has an average rating of 3.9, with 83 lectures, 21 quizzes, based on 123 reviews, and has 2719 subscribers.

You will learn about Download and Install Weka Practical use of Machine Learning Data sources and file formats Preprocess, Classifies, Filters & Datasets Practical use of Data Mining Experimenting & Comparing Algorithms Integrating open source tools with Weka Data Set Generation, Data Set & Data Stream and Classifier Evaluation How to use Weka with other open source software such as R Exploring MOA (Massive Online Analysis) Sentimental Analysis using Weka Data Science & Data Analytics tools ( Anaconda, Jupyter Notebook, Neural Network and Deep learning packages) Manipulating data with numpy and pandas libraries. This course is ideal for individuals who are Anyone curious about machine learning without programming. or Anyone who wants to explore data engineering and data science. or Whether youre a data enthusiast, aspiring data scientist, or industry professional looking to upgrade your skillset, this course is tailor-made for you. No prior experience is required—just bring your passion for learning, and well take care of the rest! Dont miss this incredible opportunity to accelerate your machine learning and data mining journey. Enroll now and unlock the door to a world of exciting possibilities! It is particularly useful for Anyone curious about machine learning without programming. or Anyone who wants to explore data engineering and data science. or Whether youre a data enthusiast, aspiring data scientist, or industry professional looking to upgrade your skillset, this course is tailor-made for you. No prior experience is required—just bring your passion for learning, and well take care of the rest! Dont miss this incredible opportunity to accelerate your machine learning and data mining journey. Enroll now and unlock the door to a world of exciting possibilities!.

Enroll now: Experimental Machine Learning & Data Mining: Weka, MOA & R

Summary

Title: Experimental Machine Learning & Data Mining: Weka, MOA & R

Price: $54.99

Average Rating: 3.9

Number of Lectures: 83

Number of Quizzes: 21

Number of Published Lectures: 83

Number of Published Quizzes: 21

Number of Curriculum Items: 130

Number of Published Curriculum Objects: 130

Number of Practice Tests: 2

Number of Published Practice Tests: 2

Original Price: $34.99

Quality Status: approved

Status: Live

What You Will Learn

Download and Install Weka

Practical use of Machine Learning

Data sources and file formats

Preprocess, Classifies, Filters & Datasets

Practical use of Data Mining

Experimenting & Comparing Algorithms

Integrating open source tools with Weka

Data Set Generation, Data Set & Data Stream and Classifier Evaluation

How to use Weka with other open source software such as R

Exploring MOA (Massive Online Analysis)

Sentimental Analysis using Weka

Data Science & Data Analytics tools ( Anaconda, Jupyter Notebook, Neural Network and Deep learning packages)

Manipulating data with numpy and pandas libraries.

Who Should Attend

Anyone curious about machine learning without programming.

Anyone who wants to explore data engineering and data science.

Whether youre a data enthusiast, aspiring data scientist, or industry professional looking to upgrade your skillset, this course is tailor-made for you. No prior experience is required—just bring your passion for learning, and well take care of the rest! Dont miss this incredible opportunity to accelerate your machine learning and data mining journey. Enroll now and unlock the door to a world of exciting possibilities!

Target Audiences

Anyone curious about machine learning without programming.

Anyone who wants to explore data engineering and data science.

Whether youre a data enthusiast, aspiring data scientist, or industry professional looking to upgrade your skillset, this course is tailor-made for you. No prior experience is required—just bring your passion for learning, and well take care of the rest! Dont miss this incredible opportunity to accelerate your machine learning and data mining journey. Enroll now and unlock the door to a world of exciting possibilities!

First Course:

This introductory course will help make your machine learning journey easy and pleasant , you will be learning by using the powerful Weka open source machine learning software, developed in New Zealand by the University of Waikato.

You will learn complex algorithm behaviors in a straightforward and uncomplicated manner. By exploiting Weka’s advanced facilities to conduct machine learning experiments, in order to understand algorithms, classifiers and functions such as ( Naive Bayes, Neural Network, J48, OneR, ZeroR, KNN, linear regression & SMO).

Hands-on:

Image, text & document classification & Data Visualization

How to convert bulk text & HTML files into a single ARFF file using one single command line

Difference between Supervised & Unsupervised Machine Learning methods

Practical tests, quizzes and challenges to reinforce understanding

Configuring and comparing classifiers

How to build & configure  J48 classifier

Challenge & Practical Tests

Installing Weka packages

Time Series and Linear Regression Algorithm

Where do we go from here..

The Bonus section (Be a Practitioner and upskill yourself, Installing MSSQL server 2017, Database properties, Use MS TSQL to retrieve data from tables, Installing Weka Deep Learning classifier, Use Java to read arff file, How to integrate Weka API with Java)

Weka’s intuitive, the Graphical User Interface will take you from zero to hero. You will be learning by comparing different algorithms, checking how well the machine learning algorithm performs till you build your next predicative machine learning model. 

Second Course:

New Course: Machine Learning & Data Mining With Weka, MOA & “R” Open Source Software Tools

Hands-On Machine Learning and Data Mining: Practical Applications with Weka, MOA & “R” Open Source Software Tools

Description:

This course emphasizes learning through practical experimentation with real-world scenarios, where different algorithms are compared to determine the most likely one that outperforms others.

Welcome to the immersive and practical course on “Hands-On Machine Learning and Data Mining” where you will delve into the world of cutting-edge techniques using powerful open-source tools such as Weka, MOA, “R” and other essential resources. This comprehensive course is designed to equip you with the knowledge and skills needed to excel in the field of data mining and machine learning.

Section 1: Data Set Generation and Classifier Evaluation

In this section, you will learn the fundamentals of data set generation, exploring various data types, and understanding the distinction between static datasets and dynamic data streams. You’ll delve into the essential aspects of data mining and the evaluation of classifiers, allowing you to gauge the performance of different machine learning models effectively.

Section 2: Data Set & Data Stream

In this section, we will explore the fundamental concepts of data set and data stream, crucial aspects of data mining. Understanding the differences between these two data types is essential for selecting the appropriate machine learning approach in different scenarios. Contents are as follows:

· What is the Difference between Data Set and Data Stream?

· We will begin by demystifying the dissimilarities between static data sets and dynamic data streams.

· Data Mining Definition and Applications

· We will delve into the definition and significance of data mining, exploring its role in extracting valuable patterns, insights, and knowledge from large datasets. You will gain a clear understanding of the data mining process and how it aids in decision-making and predictive analysis.

· Hoeffding Tree Classifier

· As an essential component of data stream mining, we will focus on Hoeffding tree classifier. You will learn how this online learning algorithm efficiently handles data streams by making quick and informed decisions based on a statistically sound approach. I will cover the theoretical foundations of the Hoeffding tree classifiers.

· Batch Classifier vs. Incremental Classifier

· In this part, we will compare batch classifiers with incremental classifiers, emphasizing the strengths and limitations of each approach.

· Section 3: Exploring MOA (Massive Online Analysis)

In this section, we will take a deep dive into MOA, a powerful platform designed to handle large-scale data streams efficiently. You will learn about the critical differences between batch and incremental settings, and how incremental learning is particularly valuable when dealing with continuous data streams. Additionally, we will conduct comprehensive comparisons of various classifiers and evaluators within MOA, enabling you to identify the most suitable algorithms for specific data scenarios.

Section 4: Sentimental Analysis using Weka.

This section will focus on Sentimental Analysis, an essential task in natural language processing. We will work with real-world Twitter datasets to classify sentiments using Weka, a versatile machine learning tool. You’ll gain hands-on experience in preprocessing textual data and extracting meaningful features for sentiment classification. Moreover, we will integrate open-source resources to augment Weka’s capabilities and boost performance.

Section 5: A closer look at Massive Online Analysis (MOA).

Contents:

What is MOA & who is behind it?

Open Source Software explained

Experimenting with MOA and Weka

Section 6: Integrating open source tools with more Weka packages for machine learning schemes and “R” the statistical programming language.

Contents:

Install Weka “LibSVM” and “LibLINEAR” packages.

Speed comparison

Data Visualization with R in Weka

Using Weka to run MLR Classifiers

By the end of this course, you will have gained the expertise to handle diverse datasets, process data streams, and evaluate classifiers effectively. You will be proficient in using Weka, MOA, and other open-source tools to apply machine learning and data mining techniques in practical applications. So, join us on this journey, and let’s embark on a transformative learning experience together!

What you’ll learn:

Practical use of Data Mining

Experimenting & Comparing Algorithms

Preprocess, Classifies, Filters & Datasets

Integrating open source tools with Weka

Data Set Generation, Data Set & Data Stream and Classifier Evaluation

How to use Weka with other open source software such as “R”

Exploring MOA (Massive Online Analysis)

Sentimental Analysis using Weka

Integrating open source tools with more Weka packages for machine learning schemes and “R” the statistical programming language.

Optional – Data Science & Data Analytics tools (Install Anaconda, Jupyter Notebook, Neural Network and Deep learning packages)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the course

Lecture 2: What is Weka?

Lecture 3: How to download and install Weka?

Lecture 4: Downloading Weka version 3.8.5

Lecture 5: Wekas data sources and file formats

Lecture 6: Wekas Preprocess and Classifiers

Lecture 7: Wekas Package Manager

Lecture 8: Download more Datasets

Chapter 2: Practical use of Data Mining with Weka

Lecture 1: What is data mining?

Lecture 2: Prepare & clean datasets using filters

Lecture 3: Choose a classifier and apply it to datasets

Lecture 4: Deploy your model with a mini challenge

Chapter 3: Practical use of Machine Learning with Weka

Lecture 1: Whats Machine Learning?

Lecture 2: Classifiers performance with test datasets

Lecture 3: Tune & experiment with different algorithms

Chapter 4: Experiment#1: OneR vs ZeroR classifiers

Lecture 1: Create arff file from a csv file

Lecture 2: Run ZeroR & OneR classifiers

Lecture 3: A closer look at classifier outputs

Lecture 4: Mini challenge: A closer look at OneR classifier

Chapter 5: Experiment#2: J48 classifier performance

Lecture 1: Evaluating J48 performance

Lecture 2: Experimenting: Using random seed split

Chapter 6: How to build & configure J48 classifier?

Lecture 1: Build a J48 classifier-recap

Lecture 2: J48 classifier with default configuration

Lecture 3: J48 classifier with different configuration

Chapter 7: Experiment#3: KNN Regression Algorithm

Lecture 1: Know Nearest Neighbors (KNN)

Lecture 2: How does KNN learn?

Chapter 8: Experiment#4: Linear Regression Algorithm

Lecture 1: Predicting a class with numeric values

Lecture 2: Predicting car salesman next commission

Lecture 3: Challenge: Nominal To Binary-Intro

Chapter 9: Data Visualization with Weka

Lecture 1: Visualizing your data model

Lecture 2: Visualizing classifier errors

Chapter 10: Experiment#5: Image classification

Lecture 1: Creating arff data file for images

Lecture 2: Download image filter from GitHub

Lecture 3: Apply and use image filter in Weka

Lecture 4: Image classification experiment using J48 classifier

Lecture 5: Image classification: Mini Challenge

Chapter 11: Document Classification with Weka

Lecture 1: Document classification

Lecture 2: Creating arff training dataset

Lecture 3: Evaluation tarining dataset after applying StringToWordVector

Chapter 12: Text Classification with Weka

Lecture 1: Quick recap & Introduction

Lecture 2: Collecting and Analyzing dataset: The holiday reviews arff file

Lecture 3: Brief intro to Sentiment Analysis

Lecture 4: How to convert bulk text files into a single ARFF file

Lecture 5: Supervised & Unsupervised ML methods for classifying text

Chapter 13: Challenge & Practical Tests

Lecture 1: Running VotedPerceptron vs SMO

Lecture 2: Analyzing the results Challenge

Chapter 14: Challenge: Install Weka 3.8.6 and new packages

Lecture 1: To Do List – Task1 Challenge

Lecture 2: How to verify the installation

Chapter 15: Where do we go from here..

Lecture 1: Take Machine Learning to the next level..

Lecture 2: Hint and tips for your IT Career

Lecture 3: Take Weka to the next level

Chapter 16: New Course: Machine Learning & Data Mining: Weka, MOA & R Open Source

Lecture 1: Machine Learning & Data Mining with Weka, MOA & R Open Source

Lecture 2: Data Set Generation and Classifier Evaluation

Lecture 3: How to generate Dataset in Weka

Chapter 17: Data Set & Data Stream

Lecture 1: Data Set and Data Stream Explained.

Instructors

Shadi Oweda
ICT & QA Consultant

Rating Distribution

1 stars: 7 votes

2 stars: 8 votes

3 stars: 22 votes

4 stars: 40 votes

5 stars: 46 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!