PG Diploma in Natural Language Processing
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.

Monthly Recurring Batch

Program Duration

9 Months

Learning Format

Blended Learning

Program Fees


Course 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

For More Information

    What you will learn

    About UCAM

    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.

    Airtics Education’s PG Diploma Programs are certified by UCAM University.

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    Skills Covered


    Machine Translation

    Sentiment Analysis


    Attention Models

    Word Embeddings

    Locality-Sensitive Hashing

    Vector Space Models

    Parts-of-Speech Tagging

    N-gram Language Models


    Who Can Apply for the Course?

    Tools/ Frameworks/ Libraries

    Scripting tools

    Data science environment

    IDE shell

    Data Analytics Libraries

    Database Integrations

    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.

    Course Modules

    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.
    • Learn basic concepts of Python
    • Acquire rudimentary skills to write programs in Python
    • Ability to use Python for Data Science & Machine learning
    • 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 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.
    • Master the mathematical foundation required for writing programs
    • Learn mathematical and statistical foundations required for AI & ML
    • Acquire mathematical knowledge to build algorithms for data analysing
    • Apply statistical analysis techniques using essential softwares on data sets
    Content Covered
    • Linear Algebra
    • Statistics
    • Probability Theory
    • Statistical Tools (CSV, Excel)
    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.
    • Acquire practical skills in data analyzing, handling & visualization using Python tools
    • Perform mathematical operations on a wide range of data using NumPy
    • Operate Pandas to sort through and rearrange data, run analyses, and build data frames
    • Ability to analyze by visualizing data with Matplotlib
    Content 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
    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.
    • Understand Artificial Intelligence and Machine Learning fundamentals
    • Demonstrate a comprehensive knowledge of the nature of the data and techniques used for pre-processing the data for machine  learning
    • Introduction to major machine learning algorithms like Classifiers (for image, spam, fraud), Regression (stock price, housing price, etc.), Clustering (unsupervised classifiers)
    • 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)
    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, antonyms, 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.
    • Understand basic concepts and standard tools used in NLP
    • Acquire the prerequisite Python skills to move into Natural Language Processing
    • Understand NLP python packages to enable them to write scripts for text pre-processing
    • 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
    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.
    • 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
    • Understanding text processing and vectorization for ML Use case
    • Develop and build fully automated NLP algorithms in Burt and transformers
    • Understand the concepts of NLP, feature engineering, natural language generation, automated speech recognition, speech-to-text conversion and 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

    Interested in This Program? Secure your spot now.

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      Capstone Projects

      Showcase your capabilities with real-world projects

      Bring Your Own Project
      Learn to solve a problem that you/your organization is facing using Natural Language Processing


      Choose From Curated Capstone Projects

      Sentiment analysis

      News Articles classification

      Topic modeling

      Career Support

      Frequently Asked Questions

      Natural Language Processing is a field of computer science that deals with communication between computer systems and humans. It is a technique used in Artificial Intelligence and Machine Learning. It is used to create automated software that helps understand human spoken languages to extract useful information from the data it gets in the form of audio. Techniques in NLP allow computer systems to process and interpret data in the form of natural languages.

      Some of the best NLP tools from open sources are

      • SpaCy
      • TextBlob
      • Textacy
      • Natural language Toolkit (NLTK)
      • Retext
      • Detecting objects from an image
      • Facial Recognition
      • Speech Biometric
      • Text Summarization
      Ans: d) a) And b) are Computer Vision use cases, and c) is the Speech use case. Only d) Text Summarization is an NLP use case.

      Top-level benefits of NLP

      These are the key benefits of continual exploration of NLP:

      1. Connecting to purpose, values and motivation.
      2. Successful and fulfilled life.
      3. Improved results, relationships and resilience.

      If you are interested in engaging an NLP professional to support you through a particular issue, then we suggest you start by looking at our guide for choosing a good NLP professional. If you are wondering if NLP is a good fit for your organisation or workplace, then start by reading our guide for using NLP at work. If you are thinking about training in NLP for yourself, then start by looking at our guidance for choosing a good NLP trainer. If you are curious and want to know a bit more about NLP before diving in, then do have a look at our case studies and recommended book list, both of which can help you to make informed decisions about whether or not NLP is right for you.

      What is included in this course?

      I’m Interested in This Program

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        Course Preview

        PG Diploma in Natural Language Processing

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