Natural Language Preprocessing
58m 50s
English
Paid
Natural Language Preprocessing is a 9-lesson 58 minutes self-paced course by LunarTech. Embark on an exciting journey into the world of Natural Language Processing (NLP)—a field where linguistics and artificial intelligence intersect, enabling machines to understand human language.
Course facts
- Lessons
- 9
- Duration
- 58 minutes
- Level
- All levels
- Language
- English
- Updated
- Instructor
- LunarTech
- Price
- Premium
Embark on an exciting journey into the world of Natural Language Processing (NLP)—a field where linguistics and artificial intelligence intersect, enabling machines to understand human language. This introductory course is designed to provide you with key knowledge and skills in NLP, ensuring a solid foundation for further in-depth study. Start your journey with confidence and make the most of the opportunities this course offers!
Who teaches Natural Language Preprocessing? LunarTech
LunarTech is an online tech academy focused on data science, machine learning, and quantitative analysis — covering both the theoretical foundations (linear algebra, calculus, statistics) and the practical Python / SQL toolchain that working data scientists use. The school operates globally with cohort-based and self-paced tracks.
The CourseFlix listing carries twelve LunarTech courses spanning machine-learning theory, deep learning, applied data-science workflows, and the math fundamentals underlying the field. Material is paid and aimed at engineers and analysts transitioning into formal data-science roles or upskilling within them.
What lessons are included in Natural Language Preprocessing?
0:00
/ #1: 0. Introduction
All Course Lessons (9)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | 0. Introduction Demo | 04:29 | |
| 2 | 1. Text Preprocessing in NLP | 10:28 | |
| 3 | 2. Tokenization | 04:16 | |
| 4 | 3. Bag-of-Words | 06:23 | |
| 5 | 4. Word Embeddings | 06:07 | |
| 6 | 5. Semantic Analysis | 06:57 | |
| 7 | 6. Term Frequency-Inverse Document Frequency (Tf-Idf) | 07:18 | |
| 8 | 7. Machine Learning Based NLP | 05:02 | |
| 9 | 8. Recent Developers in NLP and AI | 07:50 |
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Frequently asked questions
What are the prerequisites for this course?
This course is introductory and does not require prior experience in Natural Language Processing (NLP). However, a basic understanding of linguistics and artificial intelligence concepts might be beneficial to better grasp the course material.
What projects or exercises will I work on during the course?
The course involves practical exercises related to text preprocessing, tokenization, and the application of techniques like Bag-of-Words and Term Frequency-Inverse Document Frequency (Tf-Idf). These exercises are designed to reinforce the theoretical concepts covered in the lessons.
Who is the target audience for this course?
The course is aimed at beginners who are interested in exploring the field of Natural Language Processing. It is suitable for students, researchers, and professionals who want to gain foundational knowledge in NLP.
How does this course compare to other introductory NLP courses?
This course provides a foundational understanding of NLP by covering essential topics such as text preprocessing, tokenization, and word embeddings. It offers a balance of theoretical insights and practical exercises, making it suitable for those new to the field.
What specific tools or platforms are covered in the course?
The course covers fundamental techniques like Bag-of-Words and Tf-Idf, and discusses recent developments in NLP and AI. While it does not focus on any specific software tools, the methods taught can be applied using popular NLP libraries.
What topics are not covered in this course?
Advanced NLP topics such as neural network-based models, deep learning approaches, and complex language models are not covered in this introductory course. The focus is on foundational concepts to prepare for more complex studies.
How can the knowledge from this course be applied to other areas or careers?
The foundational knowledge gained from this course can be applied to various fields requiring text analysis and understanding, such as data science, computational linguistics, and artificial intelligence development. It also provides a stepping stone for more advanced NLP studies.