Are you passionate about Artificial Intelligence and Natural Language Processing?

Do you want to pursue a career as a data scientist or as an AI engineer?

If that’s the case, then this is the perfect course for you!

In this Intro to Natural Language Processing in Python course you will explore essential topics for working with text data. Whether you want to create custom text classifiers, analyze sentiment, or explore concealed topics, you’ll learn how NLP works and obtain the tools and concepts necessary to tackle these challenges.

Natural language processing is an exciting and rapidly evolving field that fundamentally impacts how we interact with technology. In this course, you’ll learn to unlock the power of natural language processing and will be equipped with the knowledge and skills to start working on your own NLP projects.

The training offers you access to high quality Full HD videos and practical coding exercises. This is a format that facilitates easy comprehension and interactive learning. One of the biggest advantages of all trainings produced by 365 Data Science is their structure. This course makes no exception. The well-organized curriculum ensures you will have an amazing experience.

You won’t need prior natural language processing training to get started—just basic Python skills and familiarity with machine learning.

This introduction to NLP guides you step-by-step through the entire process of completing a project. We’ll cover models and analysis and the fundamentals, such as processing and cleaning text data and how to get data in the correct format for NLP with machine learning.

We'll utilize algorithms like Latent Dirichlet Allocation, Transformer models, Logistic Regression, Naive Bayes, and Linear SVM, along with such techniques as part-of-speech (POS) tagging and Named Entity Recognition (NER).

You'll get the opportunity to apply your newly acquired skills through a comprehensive case study, where we'll guide you through the entire project, covering the following stages:

• Text cleansing

• In-depth content analysis

• Sentiment analysis

• Uncovering hidden themes

• Ultimately crafting a customized text classification model