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PG Diploma in Natural Language Processing

Universidad Católica de Murcia (UCAM)

The program equips learners to master the skills to get computers to understand, process, and manipulate human language. Build models on real data and hands-on experience with sentiment analysis, machine translation, and more.

 

Program Overview

Master the skills to get computers to understand, process, and, manipulate human language. Build models on real data, and hands-on experience with sentiment analysis, machine translation and more.

ML skills to get computers to understand, process, and manipulate human language. Build Learn cutting-edge natural language processing techniques to process speech and analyze text. Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to teach the computer to do tasks such as sentiment analysis and text classification, machine translation, and more!

Training Key Features

  • Flexible

    9 Months

  • Blended Learning

    Live classroom and Live online class.

  • +971 6 5310 843

    (09:00am - 17:30pm)

Partners of this Programme

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 and contribute to the further expansion of human knowledge through research and development.

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. The programmes offered are distinguished in Europe and worldwide, with good graduate employability prospects as well.

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 ?

1 Course

Choosing a course of study that you have a strong inclination to pursue a UK qualifying degree or for a skilled set is a good start in pursuing your educational goals. At ECX, you get a triple power MBA degree.

2 Place 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.

3 Affordable Fee

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

4 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.

5 Career 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.

Course Resources

For more detailed information about the course, please click on the links below.

Program Details

Eligibility

Students seeking admission to the course may have to fulfill the following criteria/requirements.

  • Bachelor’s Degree from a recognized University
  • Proficiency in the English language

Learning Path

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 a 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

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)

 

Module description

This module offers a guide to the parts of the Python programming language and its data oriented library ecosystem and tools that will equip students to become effective data analysts. The module focuses specifically on Python programming, libraries, and tools needed for data analysis. Essential Python libraries covered in this module are NumPy, pandas & matplotlib. NumPy provides the data structures, algorithms, and library glue needed for most scientific numerical data applications in Python. Pandas provide high-level data structures and functions that make working with structured or tabular data fast, easy, and expressive. Matplotlib libraries are used for producing plots and other two-dimensional data visualizations.

Learning Outcomes

LO1: Acquire practical skills in data analyzing, handling & visualization using Python tools

LO2: Perform mathematical operations on a wide range of data using NumPy

LO3: Operate Pandas to sort through and rearrange data, run analyses, and build data frames

LO4: Ability to analyze by visualizing data with Matplotlib

Contents Covered

Python Programming for AI & ML

  • Essential Python libraries for data analysis

  • Data storage and manipulation by NumPy

  • Data Visualization using Matplotlib

  • Data Analysis with Pandas

  • Basic introduction to Sci-kit-learn

 

Module Description

This module provides an in-depth understanding of established methods of artificial intelligence and machine learning techniques that enable computers to learn without being explicitly programmed. The module discusses various parts of artificial intelligence, which include ML (Machine Learning), DL (Deep Learning), NLP (Natural language Processing), RL (Reinforcement learning), and DRL (Deep reinforcement learning), and aims to explain the real-world application of improved algorithms such as linear regression, k-NN, decision trees, random forest, etc. for machine learning by supervised, unsupervised and reinforcement learning.

Learning Outcomes

LO1: Understand Artificial Intelligence and Machine Learning fundamentals

LO2: Demonstrate a comprehensive knowledge of the nature of the data and techniques

used for pre- processing the data for machine learning

LO3: Introduction to major machine learning algorithms like Classifiers (for image, spam, fraud), Regression (stock price, housing price, etc.), Clustering (unsupervised classifiers)

LO4: Demonstrate a deep critical understanding of algorithms and mathematics

behind established ML approaches

Content Covered

  • Introduction to Machine Learning & AI

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

  • Machine Learning Algorithms (Regression, Classifiers, Clustering)

  • Machine Learning Task (dataset, data cleaning, algorithm selection, training & testing model)

 

Course Description

This module explores advanced mathematics and discrete optimization to create resilient and high-performance machine learning systems. Learners get to employ Python to construct multivariate calculus for machine learning to investigate the role of mathematical intuitions in creating Natural Language processes and algorithms. Furthermore, observe a demonstration using calculus and mathematical operations using Python; and grasp the use of limits and series expansions in Python. Key aspects presented here include extracting synonyms, atonyms, process, and text analysis for machine learning utilizing the Natural Language Toolkit package for Python to generate extremely fast tokenization, parsing, entity identification, and lemmatization of text.

Learning Outcomes

LO1: Understand basic concepts and standard tools used in NLP

LO2: Acquire the prerequisite Python skills to move into Natural Language Processing

LO3: Understand NLP python packages to enable them to write scripts for text pre-processing

LO4: Learn popular machine learning algorithms, Feature Selection, and the Mathematical intuition behind them

Content Covered

Core Python for computer vision

  • Strings

  • Regex

Machine Learning algorithms

  • Regression

  • KNN

  • SVM

Computer vision tools

  • Keras

  • TensorFlow

 

Course Description

This module aims to examine machine learning models and techniques for Natural Language Processing by applying learning models from areas of knowledge of statistical decision theory, artificial intelligence, and deep learning. We will examine supervised learning methods for regression and classification, unsupervised learning approaches, and text-analysis applications. Throughout the module, we understand the relationship between ML and Natural Language processing by utilizing python for algorithm implementation. This module inculcates the traditional neural network learning methods, such as feed-forward neural networks, recurrent neural networks, and convolutional neural networks, with applications to natural language processing problems such as utterance classification and sequence tagging.

Learning Outcomes

L01.Concepts of deep learning to build artificial neural networks and traverse layers of data abstraction, and get a solid understanding of deep learning using TensorFlow and Keras

L02.Understanding text processing and vectorization for ML Use case

L03. Develop and build fully automated NLP algorithms in Burt and transformers

L04. Understand the concepts of NLP, feature engineering, natural language generation, automated speech recognition, speech-to-text conversion, text-to-speech conversion

Content Covered

  • Introduction to machine learning

  • Supervised learning

  • Unsupervised learning

  • ML deployment

  • Automated speech recognition

  • Text-to-speech conversion

  • Decision theory

  • Regression

  • Classification

  • Text Analysis applications

  • Feed-forward neural networks

  • Recurrent neural network

  • Convolutional neural network

  • Utterance classification

  • Sequence tagging

 

Course Team