Learn Natural Language Processing using Spacy, NLTK, PyTorch, Text Pre-Processing, Embeddings, Word2Vec & Deep Learning

What you’ll learn

  • • The importance of Natural Language Processing (NLP) in Data Science.
  • • The reasons to move from classical sequence models to deep learning-based sequence models.
  • • The essential concepts from the absolute beginning with complete unraveling with examples in Python.
  • • Details of deep learning models for NLP with examples.
  • • A summary of the concepts of Deep Learning theory.
  • • Practical description and live coding with Python.
  • • Deep PyTorch (Deep learning framework by Facebook).
  • • The use and applications of state-of-the-art NLP models.
  • • Building your own applications for automatic text generation and language translators.
  • • And much more…

Requirements

  • • No prior knowledge is required. You will start from the fundamental concepts and slowly build your knowledge of the subject.
  • • A willingness to learn and practice.
  • • Knowledge of Python will be a plus.

Description


Thorough Course Description:

Regular Language Processing (NLP), a region of Artificial Intelligence (AI), is the capacity of a PC to comprehend human language the manner in which it's spoken and composed. Human language is regularly alluded to as normal language.

People additionally have various sensors. For example, ears fill the role of hearing, and eyes fill the role of seeing. Additionally, PCs have programs for perusing and amplifiers for gathering sound. Similarly as the human cerebrum processes an info, a PC program processes a particular information. Furthermore during handling, the program changes over the contribution to code that the PC gets it.

This course, Natural Language Processing (NLP), Theory and Practice in Python, acquaints you with the ideas, instruments, and strategies of AI for text information. You will get familiar with the rudimentary ideas just as arising patterns in the field of NLP. You will likewise find out with regards to the execution and assessment of various NLP applications utilizing profound learning techniques.

Why Use Python for NLP?

Python is the most favored language for NLP because of its broad apparatuses and libraries for text examination and PC usable information extraction. This course will take you through various procedures for text pre-handling, from nuts and bolts, for example, customary articulations and text standardization to complex subjects, for example, string coordinating, language models, and word embeddings.

You will think about a large portion of the models from the English language for understanding the calculations. However, the calculations can be adjusted to any language. (Subsequently, there's no language/punctuation reliance.) You will get openness to cutting edge bundles (NLTK, Gensim, SpaCy) just as structures (PyTorch) alongside broad execution/coding-focused substance in Python. The primary focal point of the course is on getting ready text information for AI models.

Despite the fact that we have separate seminars on Deep learning, we in all actuality do cover valuable ideas in this course momentarily to make this course more autonomous.

The course content is quite certain and direct. The learning material is an ideal mix of hypothetical ideas and viable applications. Models and test code have been incorporated to assist you with getting a handle on every idea with greater clearness. Every NLP idea is organized and introduced so that makes it simple for you to comprehend.

Excellent video content, convincing course material, evaluation questions, course notes, and presents are extra advantages that you will get. You can contact our cordial group if there should arise an occurrence of any questions.

This course urges you to gain speedy headway. Toward the finish of every module, you will get a chance to modify all that you have learned through Homework/undertakings/exercises. They have been intended to assess/further form your learning dependent on the ideas and strategies you have learned. The majority of these tasks are coding-based, and they will be helpful to advance you up and go beyond with executions.

The two little ventures in the last module—Neural Machine/Language Translator and Modify Language Translator a Bit and Build a Chatbot—center around the creative applications in this field. These smaller than normal undertakings assist you with applying the ideas of pre-handling text. You will utilize procedures, for example, grammatical forms labeling, lemmatization, and tokenization utilizing Python libraries.

NLP has made gigantic advances somewhat recently, and it's taken the jump from research labs to certifiable applications. While beginning in this field can be a difficult pursuit, this course gives you an unmistakable and significant guide. It makes the undertaking of achieving your profession objectives that a lot simpler.

This course is seriously estimated and conveys an incentive for cash, as you can gain proficiency with the ideas and approaches of NLP for a generally minimal price. The series of brief recordings and the point by point code journals abbreviate your expectation to learn and adapt.

Get everything rolling with this NLP course right away!

Course Content:

This total course comprises of the accompanying points:

After completing this course successfully, you will be able to:

  • · Apply the concepts to any language to build customized NLP models.
  • · Learn machine learning concepts in a more practical way.
  • · Understand the methodology of NLP using real datasets.

Who this course is for:

  • · Complete beginners to Natural Language Processing.
  • · People who want to upgrade their Python programming skills for NLP.
  • · Individuals who are passionate about numbers and programming.
  • · Data Scientists.
  • · Data Analysts.
  • · Machine Learning Practitioners.

Who this course is for:

  • • Complete beginners to Natural Language Processing.
  • • People who want to upgrade their Python programming skills for NLP.
  • • Individuals who are passionate about data science and machine learning.
  • • Data Scientists.
  • • Data Analysts.
  • • Machine Learning Practitioners.