Watch Natural Language Processing with Python

Natural Language Processing with Python from Packt Publishing is an in-depth course that offers a comprehensive introduction to natural language processing (NLP) and machine learning techniques used to build models that can analyze human language. This course is an excellent starting point for those who want to learn the basics of NLP and apply those skills to real-world problems.

The course is divided into ten chapters, starting with an introduction to NLP and the essential concepts needed to start working with human language. This introduction covers topics such as tokenization, stemming, and stopwords, which are fundamental aspects of NLP. The course then moves on to explore Python's essential libraries for NLP, which include Natural Language Toolkit (NLTK), spaCy, and scikit-learn.

In the second chapter of the course, the focus is on text classification, a common problem in NLP. The course covers techniques such as bag of words, feature extraction, and sentiment analysis, which are all used to classify text data into various categories.

The third chapter shifts the focus to NLP pipelines, which are workflows that process and transform text data. This chapter covers topics such as data preparation, feature engineering, and model selection, which are essential for building robust NLP models.

In chapter four, the course dives into named entity recognition (NER), a problem that involves identifying and extracting entities such as names, locations, and dates from text data. The course covers popular NER techniques such as the rule-based approach, the spaCy library, and the conditional random fields (CRF) model.

In the fifth and sixth chapters, the course covers topic modeling and text summarization, respectively. Topic modeling involves discovering hidden topics or themes in a text corpus, while text summarization is the process of creating a shorter version of a long text document. The course covers popular techniques such as Latent Dirichlet Allocation (LDA) for topic modeling and the Textrank algorithm for text summarization.

The seventh chapter of the course introduces the concept of word embeddings, which are vector representations of words that capture the meaning and context of the word based on its usage. The course covers popular techniques such as Word2Vec and GloVe for learning word embeddings.

In chapter eight, the focus shifts to sequence labeling, which is used to assign labels or tags to each word in a sentence to identify its syntactic or semantic role. The course covers popular techniques such as part-of-speech (POS) tagging and dependency parsing.

The ninth chapter covers the use of deep learning techniques such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) for NLP. The course covers topics such as text classification, sentiment analysis, and language generation using RNNs and CNNs.

Finally, in chapter ten, the course covers advanced NLP topics such as machine translation, question answering, chatbots, and speech recognition. These topics are relevant in real-world applications such as language translation, virtual assistants, and customer support chatbots.

In conclusion, Natural Language Processing with Python from Packt Publishing is an excellent course for those who want to learn the basics of natural language processing and apply those skills to real-world problems. This course covers essential concepts, popular libraries, and various techniques used in NLP, making it an excellent starting point for beginners. The course is suitable for anyone with basic Python programming skills and an interest in NLP, including students, developers, and data scientists.

Natural Language Processing with Python is a series that ran for 1 seasons (22 episodes) between December 28, 2017 and on Packt Publishing