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

Learn how to start your Machine Learning journey with Weka, MOA to Build your next Predicative Machine Learning Models.

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)