Introduction to Natural Language Processing in Python [2024]

Introduction to Natural Language Processing in Python [2024], available at $54.99, has an average rating of 4.95, with 80 lectures, based on 17 reviews, and has 73 subscribers.

You will learn about pandas numpy seaborn matplotlib spaCy lemmatization tokenization Stop-word removal Case folding N-grams XGBOOST Word2vec skip-gram Bag of words Zipf’s law TF-IDF Feature engineering WordCloud Hierarchical Clustering Sampling Removing Correlated features Dimensionality reduction Tree methods TextBlob keras This course is ideal for individuals who are Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets. or Data Scientists who want to take their AI Skills to the next level. or AI experts who want to expand on the field of applications. or Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Software developers, data scientists, and researchers interested in natural language processing or Professionals seeking to expand their skill set and explore new career opportunities in NLP and related fields or Students and academics looking to learn about state-of-the-art techniques and tools in NLP and apply them to their research projects It is particularly useful for Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets. or Data Scientists who want to take their AI Skills to the next level. or AI experts who want to expand on the field of applications. or Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Software developers, data scientists, and researchers interested in natural language processing or Professionals seeking to expand their skill set and explore new career opportunities in NLP and related fields or Students and academics looking to learn about state-of-the-art techniques and tools in NLP and apply them to their research projects.

Enroll now: Introduction to Natural Language Processing in Python [2024]

Summary

Title: Introduction to Natural Language Processing in Python [2024]

Price: $54.99

Average Rating: 4.95

Number of Lectures: 80

Number of Published Lectures: 80

Number of Curriculum Items: 80

Number of Published Curriculum Objects: 80

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

pandas

numpy

seaborn

matplotlib

spaCy

lemmatization

tokenization

Stop-word removal

Case folding

N-grams

XGBOOST

Word2vec

skip-gram

Bag of words

Zipf’s law

TF-IDF

Feature engineering

WordCloud

Hierarchical Clustering

Sampling

Removing Correlated features

Dimensionality reduction

Tree methods

TextBlob

keras

Who Should Attend

Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning

Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.

Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.

Data Scientists who want to take their AI Skills to the next level.

AI experts who want to expand on the field of applications.

Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.

Any people who are not satisfied with their job and who want to become a Data Scientist.

Software developers, data scientists, and researchers interested in natural language processing

Professionals seeking to expand their skill set and explore new career opportunities in NLP and related fields

Students and academics looking to learn about state-of-the-art techniques and tools in NLP and apply them to their research projects

Target Audiences

Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning

Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.

Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.

Data Scientists who want to take their AI Skills to the next level.

AI experts who want to expand on the field of applications.

Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.

Any people who are not satisfied with their job and who want to become a Data Scientist.

Software developers, data scientists, and researchers interested in natural language processing

Professionals seeking to expand their skill set and explore new career opportunities in NLP and related fields

Students and academics looking to learn about state-of-the-art techniques and tools in NLP and apply them to their research projects

Natural Language Processing (NLP) is a rapidly evolving field at the intersection of linguistics, computer science, and artificial intelligence. This course provides a comprehensive introduction to NLP using the Python programming language, covering fundamental concepts, techniques, and tools for analyzing and processing human language data.

Throughout the course, students will learn how to leverage Python libraries such as NLTK (Natural Language Toolkit), spaCy, and scikit-learn to perform various NLP tasks, including tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, sentiment analysis, text classification, and language modeling.

The course begins with an overview of basic NLP concepts and techniques, including text preprocessing, feature extraction, and vectorization. Students will learn how to clean and preprocess text data, convert text into numerical representations suitable for machine learning models, and visualize textual data using techniques such as word clouds and frequency distributions.

Next, the course covers more advanced topics in NLP, including syntactic and semantic analysis, grammar parsing, and word embeddings. Students will explore techniques for analyzing the structure and meaning of sentences and documents, including dependency parsing, constituency parsing, and semantic role labeling.

The course also introduces students to practical applications of NLP in various domains, such as information retrieval, question answering, machine translation, and chatbot development. Students will learn how to build and evaluate NLP models using real-world datasets and evaluate their performance using appropriate metrics and techniques.

By the end of the course, students will have a solid understanding of the fundamental principles and techniques of NLP and the ability to apply them to solve real-world problems using Python. Whether you are a beginner or an experienced Python programmer, this course will provide you with the knowledge and skills you need to start working with natural language data and build intelligent NLP applications.

Course Outline:

1. Introduction

2. Course strucure

3. How to make out of this course

4. Overview of natural language processing

5. Text pre-processing

6. Tokenization techniques (word-level, sentence-level) and its implementation

7. Regular expression and its implementation

8. Treebank tokenizer and its implementation

9. TweetTokenizer and its implementation

10. Stemming and its implementation

11. WordNet Lemmatizer and its implementation

12. spacy Lemmatizer and its implementation

13. The introduction and implementation of stop word removal

14. The introduction and implementation of Case folding

15. Introduction and implementation of N-grams

16. Text Representation

17. Introduction to Word2vec and implementation

18. skip-gram implementation

19. Bag of word implementation

20. How to perform basic feature extraction methods

21. What are types of data

22. Text cleaning and tokenization practice.

23. How to perform text tokenization using keras and TextBlob

24. Singularizing and pluralizing words and language translation

25. What does feature extraction mean in natural language processing

26. Implementation of  feature extraction in natural language processing.

27. Introduction to Zipf’s Law and implementation

28. Introduction to TF-IDF and implementation

29. feature engineering

30. Introduction to WordCloud and its implementation

31. spaCy overview and implementation

32. Introduction to spaCy

33. Tokenization Implementation

34. lemmatization Implementation

35. Text Classifier Implementation

36. Introduction to Machine learning

37. Introduction to Hierarchical Clustering and implementation

38. introduction to K-means Clustering and implementation

39. Introduction to Text Classification and implementation

40. introduction to tree methods and implementation

41. introduction to Removing Correlated Features and implementation

42. introduction to Dimensionality Reduction and implementation

Mode of Instruction:

The course will be delivered through a combination of lectures, demonstrations, hands-on exercises, and project work.

Students will have access to online resources, including lecture slides, code examples, and additional reading materials.

Instructor-led sessions will be supplemented with self-paced learning modules and group discussions.

certification:

Upon successful completion of the course, students will receive a certificate of completion, indicating their proficiency in natural language processing with Python.

Join us on a journey into the fascinating world of natural language processing and discover the endless possibilities for building intelligent applications that can understand and interact with human language data. Enroll now and take the first step towards mastering the art of NLP with Python!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Structure

Lecture 2: How to make out of the course

Lecture 3: Overview of Natural Language Processing

Chapter 2: Text Preprocessing

Lecture 1: Introduction to Tokenization in Natural Language Processing

Lecture 2: Tokenization Implementation Part 1

Lecture 3: Introduction to Regular Expression

Lecture 4: Regular Expression Implementation

Lecture 5: Introduction to Treebank tokenizer

Lecture 6: Treebank tokenizer Implementation

Lecture 7: Introduction to TweetTokenizer

Lecture 8: TweetTokenizer Implementation

Lecture 9: Introduction to Word Normalization

Lecture 10: Introduction to Stemming

Lecture 11: Stemming Implementation

Lecture 12: Introduction to Lemmatization

Lecture 13: Introduction WordNet lemmatizer

Lecture 14: WordNet lemmatizer implementation

Lecture 15: The introduction and implementation of Spacy lemmatizer

Lecture 16: The introduction and implementation of stop word removal

Lecture 17: The introduction and implementation of Case folding

Lecture 18: Introduction and implementation of N-grams

Chapter 3: Text Representation

Lecture 1: Introduction to Word2vec

Lecture 2: Introduction to skip-gram method

Lecture 3: Word2vec implementation Part 1

Lecture 4: Word2vec implementation Part 2

Lecture 5: Skip-gram Implementation part 1

Lecture 6: Skip-gram Implementation part 2

Lecture 7: Skip-gram Implementation part 3

Lecture 8: Skip-gram Implementation part 4

Lecture 9: Skip-gram Implementation part 5

Lecture 10: Skip-gram Implementation part 6

Lecture 11: Skip-gram Implementation part 7

Lecture 12: Introduction to Bag-of-Words algorithm

Lecture 13: Bag of words algorithm Implementation

Chapter 4: How to perform basic feature extraction methods

Lecture 1: What are types of data

Lecture 2: Text cleaning and tokenization practice.

Lecture 3: How to perform text tokenization using keras and TextBlob

Lecture 4: Singularizing and pluralizing words and language translation

Lecture 5: What does feature extraction mean in natural language processing

Lecture 6: Implementation of feature extraction in natural language processing Part 1

Lecture 7: Implementation of feature extraction in natural language processing Part 2

Lecture 8: Introduction to Zipfs Law

Lecture 9: Zipfs Law Implementation

Lecture 10: Introduction to TF-IDF

Lecture 11: TF-IDF implementation

Lecture 12: Introduction to feature engineering

Lecture 13: Feature engineering implementation

Lecture 14: Introduction to WordCloud and its implementation

Chapter 5: spaCy overview and implementation

Lecture 1: Introduction to spaCy

Lecture 2: Tokenization Implementation with SpaCy Part 1

Lecture 3: Tokenization Implementation with SpaCy Part 2

Lecture 4: Tokenization Implementation with SpaCy final Part

Lecture 5: Lemmatization implementation with spaCy

Chapter 6: Text Classifier Implementation

Lecture 1: Introduction to Machine learning

Lecture 2: What is Hierarchical Clustering?

Lecture 3: Hierarchical Clustering Implementation Part 1

Lecture 4: Hierarchical Clustering Implementation Final Part

Lecture 5: What is K-means Clustering?

Lecture 6: K-means Clustering Implementation

Lecture 7: What is supervised learning?

Lecture 8: What is classification

Lecture 9: What is logistic regression?

Lecture 10: What is Naive Bayes Classifiers

Lecture 11: What is K-Nearest Neighbors

Lecture 12: Text Classification implementation

Lecture 13: What is regression?

Lecture 14: Regression Implementation

Lecture 15: What is tree methods

Lecture 16: What is Random Forest

Lecture 17: What is GBM and XGBoost

Lecture 18: Implementation of tree methods

Lecture 19: What is Sampling

Lecture 20: Sampling implementation

Lecture 21: What is Removing Correlated Features?

Lecture 22: Removing Highly Correlated Feature Implementation

Lecture 23: what is Dimensionality Reduction

Lecture 24: Dimensionality Reduction Implementation

Lecture 25: introduction to evaluating the Performance of a Model

Lecture 26: How to calculate the RMSE and MAPE

Chapter 7: Thank you

Lecture 1: Thank you

Instructors

Hoang Quy La
Electrical Engineer

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2 stars: 0 votes

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4 stars: 1 votes

5 stars: 15 votes

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