PG Diploma in Natural Language Processing

The program aims to provide learners with the ability to proficiently teach computers to comprehend, process, and handle human language. This is achieved through the use of real-world data and practical experience in various language processing tasks, including sentiment analysis, machine translation, and other related applications. By the end of the program, learners should have a mastery of these skills.


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Program Overview

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. 

9 Months
Ucam University
Blended Learning
Live classroom and Live online class.
+971 6 5310 843
(09:00am - 17:30pm)

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    Key Features

    Training Key Features

    • 144 hours of live instructor-led training
    • 3 industry-based projects, 6 assignments
    • 24/7 support and LMS Access
    • Hands-on experience with latest tools & applied projects
    • Live engagement classes by seasoned academics and professionals
    • Internship/Projects
    • Flexible timing for working professionals
    • EMI option


    What You Will Learn

    • Learn key machine learning concepts and deep learning methods to build cutting-edge NLP systems in any specific domain
    • Develop graphical models for lemmatization – a key step in many NLP tasks
    • Synthesize n-gram language models and make qualitative/quantitative comparison of simple to complex n-gram models
    • Utilize neural networks to label parts of speech (POS) and named entities (NER) in English and other languages
    • Train dependency parsers from treebanks and use them to perform NLP tasks
    • Automatically detect phrase structure grammar from treebanks by generating parse trees

    Skills Covered

    • Word2vec
    • Machine Translation
    • Sentiment Analysis
    • Transformers
    • Attention Models
    • Word Embeddings
    • Locality-Sensitive Hashing
    • Vector Space Models
    • Parts-Of-Speech Tagging
    • N-Gram Language Models
    • Autocorrect


    Who Can Apply for the Course?

    • Professionals who have a keen interest in AI
    • Career starters interested in the field of AI
    • Students aiming their career in AI and Machine learning
    • Software and IT Professionals
    • Individuals and professionals looking for a career change
    • Engineers, Marketing, Healthcare, Finance professionals aiming for career excellence.


    Tools/ Frameworks/ Libraries


    Scripting Tools

    Python, MySQL


    IDE Shell



    Database Integrations



    Data Science Environment




    Pandas, NumPy, Seaborn, Matplotlib, Scikit, Pytorch, Spacy, NLTK 


    Automated Machine Learning Models

    Supervised, Unsupervised


    Application And Use Cases

    Clinical Documentation

    Checking Grammar

    Search Autocorrect and Autocomplete

    Business Analytics

    Document classification

    Text summarization

    Automated speech/voice recognition



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



    Due to its involvement in modern Machine Learning algorithms with math and programming, candidates having knowledge of linear algebra, probability, and calculus could be a plus.

    Partners 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 ?


    Choosing a course of study where you are strongly inclined to pursue a European qualifying degree or for a skill set is a good start in pursuing your educational goals. At ECX, you would be empowered to lead the world.

    Place of Study

    To pursue your dream education, the key factor is that the students need ease in accessing the centre. At ECX, we come to your nearest city to overcome any challenges faced in commuting or travelling abroad without compromising on the quality of education.

    Affordable Fee

    Quality education abroad is highly expensive. At ECX, you benefit from enrolling on an affordable course with flexible payment options.

    Academic Support

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

    Career Opportunities

    You become professionals in your respective field of study 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.

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    Program Details

    A three-year, practical-based degree applicable to a wide range of sectors. It equips you with the opportunity to develop the IT skills required to manage and improve your performance in a business environment.

    Learning Path

    Module 1
    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 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 2
    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)
    Module 3
    Python for Machine Learning
    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 4
    Introduction to Machine Learning & Artificial Intelligence
    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)
    Module 5
    Advanced Python for NLP
    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
    Module 6
    Machine Learning for NLP
    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

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    Suite 703, City Gate Tower, Al Ittihad Road, Al Tawun, Sharjah, UAE, +971 6 531 2511