2023 Natural Language Processing in Python for Beginners

 

2023 Natural Language Processing in Python for Beginners - Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam & CV Parsing

Created by Laxmi Kant

Preview this Course  -  GET COUPON CODE

Welcome to KGP Talkie's Natural Language Processing (NLP) course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python.

We will learn Spacy in detail and we will also explore the uses of NLP in real life. This course covers the basics of NLP to advance topics like word2vec, GloVe, Deep Learning for NLP like CNN, ANN, and LSTM. I will also show you how you can optimize your ML code by using various tools of sklean in python. At the end part of this course, you will learn how to generate poetry by using LSTM. Multi-Label and Multi-class classification is explained. At least 12 NLP Projects are covered in this course. You will learn various ways of solving edge-cutting NLP problems.

What you'll learn

• Learn complete text processing with Python
• Learn how to extract text from PDF files
• Use Regular Expressions for search in text
• Use SpaCy and NLTK to extract complete text features from raw text
• Use Latent Dirichlet Allocation for Topic Modelling
• Use Scikit-Learn and Deep Learning for Text Classification
• Learn Multi-Class and Multi-Label Text Classification
• Use Spacy and NLTK for Sentiment Analysis
• Understand and Build word2vec and GloVe based ML models
• Use Gensim to obtain pretrained word vectors and compute similarities and analogies
• Learn Text Summarization and Text Generation using LSTM and GRU

Introduction to Spacy for Natural Language Processing

 Introduction to Spacy for Natural Language Processing

Kick start your Data Science career with NLP. This course is about Spacy. NLTK is not taught in this course.

Preview this Course  -  GET COUPON CODE

Welcome to "Introduction to Spacy for Natural Language Processing"! In this course, you will learn how to use the powerful Spacy library to perform various natural language processing tasks such as tokenization, tagging, parsing, and named entity recognition.

You will start by learning the basics of Spacy and how to install and use it in your Python projects. From there, you will dive into more advanced topics such as using Spacy's pre-trained models, creating custom pipeline components, and working with large datasets.

Throughout the course, you will work on real-world examples and hands-on exercises to solidify your understanding of the concepts. By the end of the course, you will have the skills and knowledge needed to confidently use Spacy in your own NLP projects.

This course is suitable for beginners to NLP and Spacy, as well as experienced developers looking to expand their skills. Sign up now and start your journey to mastering Spacy and NLP!

Spacy is a popular natural language processing library for Python that provides a wide range of features for working with text data. Some of the key features of Spacy include:

Tokenization: Spacy can quickly and accurately tokenize text into words and punctuation, making it easy to work with individual words and phrases.

Part-of-speech tagging: Spacy can identify and label the part-of-speech of each token in a sentence, such as nouns, verbs, adjectives, and more.

Named entity recognition: Spacy can identify and label specific entities in a text, such as people, organizations, and locations.

Dependency parsing: Spacy can analyze the grammatical structure of a sentence and identify the relationships between words, such as subject-verb-object.

Sentence detection: Spacy can detect and segment text into individual sentences, making it easy to work with multiple sentences at once.

Pre-trained models: Spacy includes pre-trained models for various languages, which can be easily loaded and used for tasks such as part-of-speech tagging and named entity recognition.

Custom pipeline components: Spacy allows developers to create custom pipeline components, which can be added to the existing pipeline to perform specific tasks.

Speed and efficiency: Spacy is designed to be fast and efficient, making it a good choice for working with large datasets.

Integration with other libraries: Spacy can be easily integrated with other popular Python libraries such as pandas, numpy, and scikit-learn for data analysis and machine learning tasks.

Spacy can be used in machine learning and deep learning in a number of ways. Some common use cases include:

Text classification: Spacy's pre-trained models and custom pipeline components can be used to extract features from text data, which can then be used as input to a machine learning model for text classification tasks such as sentiment analysis or topic classification.

Named entity recognition: Spacy's pre-trained models for named entity recognition can be used to extract named entities from text data, which can be used as input to a machine learning model for tasks such as entity linking or knowledge graph construction.

Text generation: Spacy can be used to preprocess text data and tokenize it into a format that can be used as input to a deep learning model for text generation tasks such as language translation or text summarization.

Text summarization: Spacy can be used to extract key phrases and entities from a text and use it as input to a deep learning model for text summarization tasks.

Text similarity: Spacy can be used to tokenize and vectorize text, which can then be used as input to machine learning models that calculate text similarity or perform tasks such as document clustering.

Text-to-Speech and Speech-to-Text: Spacy can be used to pre-process text data, tokenize and extract key phrases and entities, which can be used in TTS and STT models.

Overall, Spacy can provide a powerful set of features for natural language processing that can be easily integrated with machine learning and deep learning models to improve the performance of a wide range of NLP tasks.

Who this course is for:

• Beginners to natural language processing and Spacy who want to learn how to use the library for various NLP tasks.
• Experienced developers who want to expand their skills and learn how to use Spacy for their projects.
• Data Scientists, Machine learning Engineers, and NLP practitioners who want to extract features from text data and use it in their models.
• Anyone who is interested in learning about natural language processing and how to use Spacy to process and analyze text data.

Natural Language Processing with Deep Learning in Python

 Natural Language Processing with Deep Learning in Python - Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets

Created by Lazy Programmer Inc.

Preview this Course  -  GET COUPON CODE

In this course we are going to look at NLP (natural language processing) with deep learning.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.

These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.

What you'll learn

• Understand and implement word2vec
• Understand the CBOW method in word2vec
• Understand the skip-gram method in word2vec
• Understand the negative sampling optimization in word2vec
• Understand and implement GloVe using gradient descent and alternating least squares
• Use recurrent neural networks for parts-of-speech tagging
• Use recurrent neural networks for named entity recognition
• Understand and implement recursive neural networks for sentiment analysis
• Understand and implement recursive neural tensor networks for sentiment analysis
• Use Gensim to obtain pretrained word vectors and compute similarities and analogies