NLP Programming Cosine Similarity for Beginners

NLP Programming Cosine Similarity for Beginners, available at $19.99, with 7 lectures, and has 6 subscribers.

You will learn about Students will learn concepts about Natural Language Processing using Vector Space Model. Students will learn one of the techniques to calculate Cosine Similarity. Students will know how to program Cosine Similarity using Java Programming Language. Students will understand the basics of Machine Learning. This course is ideal for individuals who are Students who are involved in Machine Learning, Software Measurement activities, Semantic Information using Software Engineering Data and NLP. It is particularly useful for Students who are involved in Machine Learning, Software Measurement activities, Semantic Information using Software Engineering Data and NLP.

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Summary

Title: NLP Programming Cosine Similarity for Beginners

Price: $19.99

Number of Lectures: 7

Number of Published Lectures: 7

Number of Curriculum Items: 7

Number of Published Curriculum Objects: 7

Original Price: CA$24.99

Quality Status: approved

Status: Live

What You Will Learn

Students will learn concepts about Natural Language Processing using Vector Space Model.
Students will learn one of the techniques to calculate Cosine Similarity.
Students will know how to program Cosine Similarity using Java Programming Language.
Students will understand the basics of Machine Learning.

Who Should Attend

Students who are involved in Machine Learning, Software Measurement activities, Semantic Information using Software Engineering Data and NLP.

Target Audiences

Students who are involved in Machine Learning, Software Measurement activities, Semantic Information using Software Engineering Data and NLP.

This course shows how to perform document similarity using an information-based retrieval method such as vector space model by using cosine similarity technique.

In the first part of the course, students will learn key concepts related to natural language and semantic information processing such as Binary Text Representation, Bag of Words, Lemmatization, TF, IDF, TF-IDF, Cosine Similarity, CamelCase and Identifiers.

In the second part of the course, students will learn how to develop and implement a natural language software to perform document similarity. The course provides the basics to help students understand the theory and practical in Java Programming. The code sample also provides students techniques of how to modularize, trace and implements algebra functionalities.

We conclude the course by providing some guidelines about how to run and debug the program. Students are also given reference links to external resources which help them in gaining better understanding when dealing with natural language software or machine learning.

At the end of the course, you will have a complete understanding of the fundamental concepts of NLP using programming languages. The objective of the course is to learn and familiarise the concepts at the beginner level but an intermediate level of programming knowledge is required. The coding example in this course uses Java Programming Language to illustrate the document similarity.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Illustrating Cosine Similarity using an example

Lecture 1: Lecture 2: Explaining Cosing Similarity through a simple tutorial

Chapter 3: Lemmatization

Lecture 1: Lecture 3: What is lemmatization?

Chapter 4: Lemmatization in Java

Lecture 1: Lecture 4: Implementing Lemmatization in Java

Chapter 5: Performing document similarity using Cosine Similarity

Lecture 1: Lecture 5: Part A – Loading Documents

Lecture 2: Lecture 6: Part B – Performing Cosine Similarity

Chapter 6: Conclusion

Lecture 1: Final steps and Recap

Instructors

Ashwin Soorkeea
Consultant in Software Development

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