Machine Learning & Data Mining with Weka, MOA & "R" Open Source Software Tools

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!