PG Diploma in Natural Language Processing

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Course Overview

  • Acquire expertise in training computers to comprehend, process, and manipulate human language. Develop models based on actual data and hands-on training with tasks like sentiment analysis, machine translation, and other related applications. Learn advanced natural language processing techniques to analyse text and process speech, and create probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to enable the computer to perform tasks like sentiment analysis, text classification, machine translation, and other related tasks.

Key
Features

Brochure
Duration

9 months course duration.

Certification

UCAM University, Spain

Blended Learning

Live classroom and Live online class.




Natural Language Processing

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Partner of this Programme

UCAM University, Spain

Universidad Católica de Murcia (UCAM), founded in 1996, is a fully-accredited European University based out of Murcia, Spain. With learning centres in the Middle East and Southeast Asia, UCAM aims to provide students with the knowledge and skills to serve society.

The university offers various courses, including 30 official bachelor’s degrees, 30 master’s degrees and ten technical higher education qualifications through its Higher Vocational Training Institute, in addition to its in-house qualifications and language courses.

UCAM is accredited by ANECA (National Agency for Quality Assessment and Accreditation of Spain) and the Ministry of Education regarding 17 of its undergraduate degrees.





Why this Course ?

  • 1Place of Study

    In order to pursue their dream education, the key factor is that the students need ease in accessing the centre and at ECX we come to your nearest city to overcome any challenges faced in commutation or travelling abroad without compromising on the quality of education.

  • 2Affordable Fee

    Quality education abroad is highly expensive. At ECX you get the benefit to enroll for course that is affordable with flexible payment options.

  • 3 Academic Support

    You get enrolled to a UK degree, with blended teaching methodology and 360-degree academic assistance through our faculties with international standards for attaining a business management degree.

  • 4Career Opportunities

    You become an industry-ready business professional on completion of the degree as it brings in more of a realistic pursuit, thus transforming you with the better skill sets to approach the career market further.

Learning Path

Program Modules

Basics of Python
Module description This module discuss the basics of Python programming language and explore how to setup Python environment to work with machine learning. Demonstrate different parts of Python code such as keywords, variables, data types, statements, functions, loops, libraries and get familiarized with programming in python. Learning Outcomes LO1: Learn basic concepts of Python LO2: Acquire rudimentary skills to write programs in Python LO3: Ability to use Python for Data Science & Machine learning LO4: Get application-ready with essential Python libraries & tools Content Covered Basic Python Programming Variable and data types Conditional statements Loops Functions Essential Python libraries for data science Pandas Numpy Scikit Setting up Python for Machine Learning
Mathematics & Statistics for Machine Learning & Artificial Intelligence
Module Description Mathematics have a significant role in the foundation for programming and this module is designed to help students master the mathematical foundation required for writing programs and algorithms for Artificial Intelligence and Machine Learning. The module covers three main mathematical theories: Linear Algebra, Statistics and Probability Theory. Learning Outcomes LO1: Master the mathematical foundation required for writing programs LO2: Learn mathematical and statistical foundations required for AI & ML LO3: Acquire mathematical knowledge to build algorithms for data analysing LO4: Apply statistical analysis techniques using essential softwares on data sets Content Covered Linear Algebra Statistics Probability Theory Statistical Tools (CSV, Excel).
Python for Machine Learning
This module provides a comprehensive guide to Python programming and its data-oriented library ecosystem, focusing on equipping students with the skills to become effective data analysts. It covers essential libraries like NumPy, pandas, and Matplotlib. Students will learn practical data analyzing, handling, and visualization skills using Python tools, perform mathematical operations with NumPy, manipulate data with Pandas, and visualize data using Matplotlib. The module also introduces the basics of using Sci-kit-learn for data analysis.
Introduction to Machine Learning & Artificial Intelligence
This module explores artificial intelligence and machine learning techniques, including ML, DL, NLP, RL, and DRL. It covers real-world applications of algorithms like linear regression, k-NN, decision trees, and random forest for supervised, unsupervised, and reinforcement learning. Students will gain a strong foundation in AI and ML fundamentals, learn data pre-processing techniques, understand major ML algorithms (classifiers, regression, clustering), and develop critical thinking skills in analyzing established ML approaches. The content includes an introduction to ML and AI, supervised and unsupervised learning, reinforcement learning, machine learning algorithms, and the ML task process.
Advanced Python for NLP
This module covers advanced Python for NLP, exploring mathematics and optimization for resilient machine learning systems. It teaches multivariate calculus and mathematical intuition in Natural Language processing, including synonyms, antonyms, text analysis, and tokenization using the Natural Language Toolkit package. The course enables learners to understand NLP concepts, acquire Python skills for NLP, write scripts for text pre-processing, and learn machine learning algorithms and computer vision tools.
Machine Learning for NLP
This module explores machine learning models and techniques for Natural Language Processing, covering supervised and unsupervised learning, text analysis, and the application of Python for algorithm implementation. It also includes traditional neural network learning methods like feed-forward, recurrent, and convolutional networks, focusing on problems in natural language processing. The learning outcomes include understanding deep learning using TensorFlow and Keras, text processing and vectorization, building NLP algorithms, and exploring various NLP concepts and applications. The content covered encompasses machine learning introduction, supervised and unsupervised learning, ML deployment, speech recognition, text-to-speech conversion, decision theory, regression, classification, text analysis, and computer vision.
Midwest

Meet Our Academic Team

Team
Dean - Academic and Student Affairs

Dr. Vivek Mohan

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Faculty

Dr. Rekha Shewakramani

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Program Head – MBA Courses

Mohamed Irfan Shaikh

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Faculty

Dr. Noor un Nisa Shahani

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Faculty and Course Leader

Maria Monica

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Senior Faculty and Course Leader

Rajashree K.N

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