PG Diploma in Artificial Intelligence and Machine Learning

There are a variety of topics covered in this AI-ML program, including Statistics, Machine Learning, Deep Learning, Natural Language Processing, and Reinforcement Learning. Our interactive learning model incorporates live sessions by global practitioners, labs, and industry projects into this program. This course focuses on acquiring skills and techniques commonly used in the industry.


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Program Overview

Course Overview 

The PG Diploma in Artificial Intelligence & Machine Learning is intended to enhance the skill set of students from diverse educational backgrounds by providing them with the necessary mathematics and programming foundations. This will enable them to acquire knowledge in AI & ML easily. The program’s curriculum includes theoretical concepts and practical learning through the latest industry practices. Students will learn about the collection and cleaning of big data and how it can be used in machine-learning algorithms to predict outcomes for decision-making and problem-solving. Additionally, they will acquire knowledge of the fundamentals of deep learning through the use of neural networks enabling them to build algorithms that can perform tasks independently by identifying the best approach to accomplish the task.

9 Months
UCAM University, Spain
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

    • You will learn essential mathematics for AIML such as linear algebra, statistics, probability & statistic tools 
    • You will learn Python programming & libraries (NumPy, Pandas & Matplotlib) essential for data science 
    • You will learn the fundamentals of building machine learning models using TensorFlow & Scikit Learn You will learn an elective of your choice, Natural Language Processing (NLP) or Computer Vision (CV) 

    Skills Covered

    Machine Learning

    Deep Learning

    Natural Language Processing

    Reinforcement Learning

    Computer Vision

    Neural Networks


    Who Can Apply for the Course?

    • Any graduate who has a keen interest in Artificial Intelligence
    • Individuals working in eCommerce, Healthcare, Engineering, Finance, Education, Marketing & Retail
    • Professionals aiming to upskill their career for better job opportunities 
    • Professionals who wish to transition to roles such as Data Scientist, ML Engineer or AI Product Manager


    Tools/ Frameworks/ Libraries


    Scripting Tools




    Pandas, numPy, seaborn, matploltlib, cufflinks, scikit, NLTK, CoreNLP, spaCy, PyNLP, Tensorflow, Keras, Open CV


    IDE Shell

    Jupyter Notebook, google colab, pycharm, visualstudio code


    Automated Machine Learning Models

    Supervised, Unsupervised, Reinforced learnings


    Application And Use Cases

    eCommerce: AI in eCommerce can predict behavior patterns to provide recommendations to users 

    Banking: AI helps banks predict future outcome & trends, identify fraud, predict risk allotting loans

    Marketing & Sales: AI helps businesses to market & sell product & service to the right audience.

    Natural Language Processing: AI enables machines to understand the complex human language. 

    Health Care: AI diagnose, detect & notify abnormality quickly for faster treatments thus saving lives

    Forecasting: AI forecasting can consume real time data with higher accuracy minimizing risk & loss.

    Manufacturing: AI helps to make on budget products & on time delivery more accurately than humans

     Education: AI enables universal access to personalized learning helping students improve weak spots

     Retail: AI helps to improve demand forecasting, pricing decisions & optimize product placement



    • 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 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
    Specialisation Modules - Natural Language Processing (NLP) pathway
    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 Computer Vision (CV) pathway This pathway is designed for learners who intend to specialize in the Computer Vision (CV) pathway.
    Module 7
    Advanced Python for Computer Vision (CV)
    Course Description This module begins by learning about numerical processing using the NumPy library, reading and changing photographs using the OpenCV library to open and deal with picture essentials, and gaining insight into using current deep learning network models like CNN & RNN. Comprehend image processing and apply various effects, including colour mappings, mixing, thresholds, gradients, etc. Learners master video basics using OpenCV, including dealing with streaming video from a webcam. The module will overview Image Processing & Computer Vision using Python. It will cover how TensorFlow and deep learning can be used for computer vision applications. Learners will learn to develop techniques to help computers see and understand the content of digital images, such as photographs and videos, using CNN (Convolution Neural Network). Learning Outcomes L01. Understand the Basic python tools used for Computer Vision L02. Understand image processing python packages to enable them to write scripts for text pre-processing L03. Learn popular machine learning algorithms, Feature Selection, and Mathematical intuition behind it L04. Understand basic concepts and standard tools used in computer vision Content Covered Core Python for computer vision Strings Regex Machine Learning algorithms Regression KNN SVM Computer vision tools Keras TensorFlow
    Module 8
    Machine Learning for Computer Vision (CV)
    Course Description This module will provide learners with knowledge and understanding of the application of machine learning methodologies to handle industrial difficulties, to a more extensive array of data mining and classification type activities. Learners will discover the machine learning algorithms by utilizing neural networks, k-means clustering, and support vector machines in computer vision to analyze data based on supervised, unsupervised, and partially supervised. Additionally covered in this module are, Tensor flow, Faster- RCNN-Inception-V2 model, and Anaconda software development environment utilized to recognize autos and individuals in pictures that provides insight into the usage of current deep learning network models like CNN 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 L02. Develop and build fully automated CV algorithms USING YOLO L03. Develop the usage of Deep learning models like CNN and RNN L04. Gain insights about advancements in CV, AI, and Machine Learning techniques Content Covered Introduction to Computer Vision (CV) Deep Learning Network Models Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Introduction to Keras Model Life-Cycle Image Data Manipulation using Pillow Python library. Convert Images to NumPy Arrays and Back
    Module 9
    Capstone Project - PG Level Project/Dissertation
    Module Description The purpose of this module is to discuss and explain the role of Artificial Intelligence and Machine Learning in an organization and their influence on its overall performance and competence. Learners will be encouraged to pick a research/development project that displays their past learning in the AI & ML domain. It is meant to understand various aspects of AI, such as Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision, to name a few. It also endeavors to highlight the role and significance of AI & ML during the planning, decision-making, and implementation of change in the organization. Upon completing the module, the participants will have comprehensive knowledge and the ability to demonstrate their expertise in Artificial Intelligence and Machine Learning to potential employers or educational programs. Learning Outcomes: LO1: Conduct independent research and development within the context of an AL & ML project LO2: Produce detailed documentation to a standard expected of a professional in the field of AI & ML LO3: Communicate technical information clearly and succinctly to a broad, non-specialist audience LO4: Apply knowledge of research principles and methods to plan and execute a research based industry project with a high level of personal autonomy and accountability Content Covered Clarifying the terms of the research Suggesting areas of reading Apply the knowledge base and abilities taught throughout the course to a real-world scenario The Problem, Understanding It, and Getting Data Frame a business issue in a manner that can be solved with AI & ML Apply Exploratory Data Analysis and Modeling Identify the methodology or algorithm that will handle the proposed challenge Reviewing the proposed methodology Establishing a research timetable, including initial dates for further meetings between the student and supervisor Advising students about appropriate standards & conventions concerning the assessment. Providing means of contact in addition to tutorials Educate learners to research and write their results and thoughts correctly, clearly, logically, and to a high-professional degree

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