Text and Speech analysis projects with Huggingface transformers, Tensorflow hub , Textblob and NLP Libraries .

What you will learn

Natural Language Processing : Token, Tagging and Stemming.

NLP Modelling and Testing.

Transformers-Hugging face.



Text Analysis with Natural language Processing.

Speech Analysis with Natural language Processing.


In this course you will learn about natural language processing basics , How to develop trained and pre-trained model . You will also learn how to use NLP libraries such as Huggingface transformers, Tensorflow Hub and Textblob.

You will also start developing basic models for text and speech analysis.

Natural Language Processing

Get Instant Notification of New Courses on our Telegram channel.

Up to the 1980s, most natural language processing systems were based on complex sets of hand-written rules. Starting in the late 1980s, however, there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing. This was due to both the steady increase in computational power due to Moore’s law and the gradual lessening of the dominance of Chomskyan theories of linguistics (e.g. transformational grammar), whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine-learning approach to language processing.

Neural networks

Popular techniques include the use of word embeddings to get semantic properties of words, and an increase in end-to-end learning of a higher-level task (e.g., question answering) instead of relying on a pipeline of separate intermediate tasks (e.g., part-of-speech tagging and dependency parsing). In some areas, this shift has entailed substantial changes in how NLP systems are designed, such that deep neural network-based approaches may be viewed as a new paradigm distinct from statistical natural language processing. For instance, the term neural machine translation (NMT) emphasizes the fact that deep learning-based approaches to machine translation directly learn sequence-to-sequence transformations, obviating the need for intermediate steps such as word alignment and language modeling that was used in statistical machine translation (SMT).




Introduction to Natural Language Processing
Introduction to Natural Language Processing Libraries
Introduction to Natural Language Processing Applications

NLP : Tokenization , Tagging and Stemming with NLTK

Tokenization and Tagging with NLTK
NLP : Stemming

NLP : Text Processing and Analysis

NLP : Text Processing and Analysis
NLP : Classification Models for Text Processing

Supervised Machine Learning : Natural Language Processing

NLP Model Architecture using Tensorflow Hub
Course Quiz

Applied Development : NLP For Text Generation

Project 1: Text Generation using NLP
Project 1 : Text Generation Using NLP
Project 2 : Text to Speech Using NLP API