3 years ago

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The first step is to learn the basics of Natural Language Processing in Python. Then, build your own Deep Learning Sentiment Analysis.
What you’ll learn
Text Mining and Natural Language Processing in Python

Students will be able to use Jupyter Notebook to manage Python modules and install Jupyter Notebook.
Natural Language and Its Applications is a definition of natural language and its applications.
Learn the fundamentals of natural language processing.
NLTK and spaCy are used to teach the fundamentals of text processing.
Traditional feature engineering models are a good place to start.
Create a sentiment analysis model that works.
All of these points can be coded in Python.
Requirements
Python knowledge is required.
Machine learning models should be implemented in advance.
should be interested in learning about text mining and natural language processing in practice (NLP).
Description
Do you want to know if product reviews or social media posts are good or bad?
Do you want to teach computers how to interpret natural language?

If that’s the case, this course is for you! We’ll go through the fundamental theoretical underpinnings of Natural Language Processing (NLP) and put them into practice in Python.

Keeping track of massive volumes of social media postings about their brand or product reviews has become increasingly crucial for businesses and organizations. Sentiment analysis is a subfield of natural language processing that attempts to automate this process. Finally, a deep learning model can analyze a sentence and predict if it is favorable or bad. If you want to learn how to make a model like this, this is the course for you!

Learn the fundamentals of natural language processing and text mining, as well as how to apply them in Python:
My course will show you how to use Python modules like spaCy or NLTK to apply the concepts you’ve learnt. You’ll work with so-called Transformer models, which are state-of-the-art in natural language processing, in addition to studying the fundamentals of NLP and common methodologies. Finally, you’ll put your newly acquired knowledge together to create a working deep learning model that can accept the text as input and predict emotion. You’ll learn how to apply several phases of text preparation, combine it with datasets, and develop a deep learning model in TensorFlow with this comprehensive course.
Learn from a machine learning engineer who has also taught at a university:
My name is Niklas Lang, and I work for a German IT system house as a machine learning engineer. I’ve worked with a variety of textual data sources, such as our e-commerce website, product descriptions, and online reviews, which we transform into effective and functional machine learning models. Aside from that, I’ve previously taught Data Science and Business Intelligence courses at the university level.

What you’ll receive is as follows:
An Overview of Jupyter Notebooks with Python Module Management
Natural Language Processing (NLP) and Its Applications ( Introduction to Natural Language Processing and Its Applications
Python Techniques for Text Preprocessing in Depth
Bag of Words and BERT Embeddings are examples of feature engineering approaches.
A Comprehensive Overview of Convolutional Neural Networks for Classification Tasks
Using TensorFlow to construct a machine learning model for sentiment analysis
Understanding the Python Process for Building, Compiling, and Training a Deep Learning Model
Who this course is for:
Those interested in learning how to do practical text mining and natural language processing will find

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