Об этом курсе: This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
Для кого этот курс: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data and/or a basic nltk tutorial. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Преподаватели: V. G. Vinod Vydiswaran, Assistant Professor
School of Information
| Основные сведения | Course 4 of 5 in the Applied Data Science with Python Specialization |
| Уровень | Intermediate |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
- Reading: Course Syllabus
- Reading: Help us learn more about you!
- Видео: Introduction to Text Mining
- Видео: Handling Text in Python
- Reading: Notice for Auditing Learners: Assignment Submission
- Notebook: Working with Text
- Видео: Regular Expressions
- Notebook: Regex with Pandas and Named Groups
- Видео: Demonstration: Regex with Pandas and Named Groups
- Practice Quiz: Practice Quiz
- Видео: Internationalization and Issues with Non-ASCII Characters
- Discussion Prompt: Introduce Yourself
- Reading: Resources: Common issues with free text
- Notebook: Assignment 1
- Видео: Basic Natural Language Processing
- Notebook: Module 2 (Python 3)
- Видео: Basic NLP tasks with NLTK
- Видео: Advanced NLP tasks with NLTK
- Practice Quiz: Practice Quiz
- Discussion Prompt: Finding your own prepositional phrase attachment
- Notebook: Assignment 2
- Видео: Text Classification
- Видео: Identifying Features from Text
- Видео: Naive Bayes Classifiers
- Видео: Naive Bayes Variations
- Видео: Support Vector Machines
- Видео: Learning Text Classifiers in Python
- Notebook: Case Study - Sentiment Analysis
- Видео: Demonstration: Case Study - Sentiment Analysis
- Notebook: Assignment 3
- Видео: Semantic Text Similarity
- Видео: Topic Modeling
- Видео: Generative Models and LDA
- Practice Quiz: Practice Quiz
- Видео: Information Extraction
- Reading: Additional Resources & Readings
- Notebook: Assignment 4
- Reading: Post-Course Survey
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Its awesome as a fresh learner data science era.
Excellent introduction to practical text mining in Python. Very interesting labs as well.
Great Content.
Very good overview of the NLP tasks. The assignments were again really challenging and required a lot of navigating the documentation and forums. The autograder is really frustrating sometimes though especially when it can't upload your file and you miss that part and change your correct code. Again, the assignments are really difficult without help from the forums but it was worth it.