Natural Language Processing (NLP) Training

Live Online & Classroom Enterprise Training

Natural Language Processing (NLP) is a field of AI that enables machines to understand, interpret, and generate human language for tasks like translation, sentiment analysis, and chatbots.

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What is Natural Language Processing (NLP) Course about?

In this course, you will learn popular machine learning algorithms, principal component analysis, and regularisation by building a movie recommendation system. You will learn about training data and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it, such as cross-validation. All these skills are fundamental to machine learning.

What are the objectives of Natural Language Processing (NLP) Course ?

Work with text data using the Natural Language Tool Kit.
Load and manipulate custom text data.
Analyse text to discover sentiment, essential keywords, and statistics.

Who is Natural Language Processing (NLP) Course for?

This course is for anyone unfamiliar with Natural Language Processing and looking for a way to start.
This course is probably not for you if you already understand Natural Language Processing and the Natural Language Tool Kit.

What are the prerequisites for Natural Language Processing (NLP) Course?

None

Available Training Modes

Live Online Training

30 Hours

Self-Paced Training

18 Hours

Course Outline Expand All

Expand All

Module 1: Introduction to NLP and Deep Learning

An overview of Natural Language Processing using classic machine learning methods and cuttingedge deep learning methods.

Module 2: Neural models for machine translation and conversation

Introduction to Statistical Machine Translation and neural models for translation and conversation

Module 3: Deep Semantic Similarity Models (DSSM)

Introduction to Deep Semantic Similarity Model (DSSM) and its applications.

Module 4: Natural Language Understanding

Introduction to methods applied in Natural Language Understanding, such as continuous word

representations and neural knowledge base embedding.

Module 5: Deep reinforcement learning in NLP

Introduction to deep reinforcement learning techniques applied in NLP

Module 6: Vision-Language Multimodal Intelligence

Introduction to neural models applied in Image captioning and visual question answering

Who is the instructor for this training?

The trainer for this Natural Language Processing (NLP) Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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