Part-Time Distance Education Professional Artificial Intelligence and Machine Learning
(GVETAB (USA) and CPD (UK) Accredited)

Level 03 Certificate

Please continue scrolling down to view all course information such as course syllabus, times, dates etc.

  • 5
  • 6
  • The course is accredited by The Global Vocational Education and Training Accreditation Board (USA) and is also CPD (UK) accredited which is a UK based accreditation board recognized in over 130+ countries internationally
    • This course is a empowerment initiative which is why the price is so affordable. Through this we wish to enable people to create an income source for themselves.
      • Classes are held once a week on a weekday (Saturday) 
      • Course is six months part time including examinations (1 week on (live classes and practical sessions and 1 week off (theory assignments and homework practical assignments) 
      • Can't make live lecture classes? No problem. Study at your own pace. You can request to receive weekly recorded video lectures as well as be able to ask your lecturer questions after each lecture via email. 
      • Hands on training and live lectures (you can ask questions and get individual attention and help if required) 
      • Small classes therefore ensuring individual attention to each student.
      • Students will receive homework weekly as practice makes perfect
      • All students will receive a Certificate of Completion at their online graduation ceremony on completion of the course after successfully passing and completing examinations.
      • Please scroll down further below for full course syllabus, timings, dates and fees.

  • Weekend classes: (Saturdays)
    Commences on: 06 June 2026                                
    Time: Central European Time  12:00PM 

    •                                                                                                           
    • Fees: £115

      • (Payment plan available: Deposit before course: £39 + £38 in month one of course + 

      £38 in month two of course)


    ​(Includes e-modules)

    • Course Outline:

    Module 1: 

    Introduction to Artificial Intelligence

    Overview of Artificial Intelligence and Machine Learning

    AI vs Machine Learning vs Deep Learning

    Real-world applications of AI

    Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)

    Ethical considerations in AI


    Module 2: 

    Programming Foundations for AI

    Introduction to Python programming

    Data types, variables, and operators

    Control structures (loops, conditionals)

    Functions and modules

    Working with files


    Module 3: 

    Mathematics for Machine Learning

    Linear Algebra basics (vectors, matrices)

    Probability fundamentals

    Statistics basics (mean, median, variance, distributions)

    Introduction to Linear Regression


    Module 4: 

    Data Handling and Analysis

    Data collection and preprocessing

    Data cleaning techniques

    Handling missing data

    Data visualization basics

    Introduction to libraries:

    NumPy

    Pandas


    Module 5: 

    Exploratory Data Analysis (EDA)

    Understanding datasets

    Identifying patterns and trends

    Feature relationships

    Data transformation


    Module 6: 

    Machine Learning Fundamentals

    Supervised Learning:

    Regression

    Classification

    Unsupervised Learning:

    Clustering

    Model training and testing

    Overfitting and underfitting


    Module 7: 

    Machine Learning Algorithms

    Linear Regression

    Logistic Regression

    Decision Trees

    Random Forest

    K-Nearest Neighbors

    K-Means Clustering


    Module 8: 

    Model Evaluation and Optimization

    Train-test split

    Cross-validation

    Evaluation metrics (Accuracy, Precision, Recall, F1 Score)

    Hyperparameter tuning


    Module 9: 

    Machine Learning with Scikit-learn

    Implementing ML models

    Model pipelines

    Feature scaling

    Model comparison


    Module 10: 

    Introduction to Deep Learning

    Basics of Neural Networks

    Activation functions

    Training process (forward and backward propagation)

    Introduction to frameworks:

    TensorFlow or PyTorch


    Module 11: 

    Natural Language Processing (Intermediate)

    Text preprocessing

    Tokenization

    Sentiment analysis basics

    Working with text datasets

    Module 12: Model Deployment Basics

    Saving and loading models

    Introduction to APIs for ML models

    Deployment concepts (local and cloud overview)


    Module 13: Advanced Topics (Select 1–2)

    Option A: Deep Learning (Advanced Intro)

    Convolutional Neural Networks (CNNs)

    Image classification basics


    Option B: Natural Language Processing (Advanced Intro)

    Text classification

    Introduction to language models


    Option C: AI in Business Applications

    Recommendation systems

    Predictive analytics


    Module 14: 

    Professional Development

    AI/ML career paths

    Resume and portfolio building

    Git and project documentation

    Interview preparation

    Ethics and responsible AI usage


    Core Tools and Technologies Covered

    Python

    NumPy

    Pandas

    Scikit-learn

    TensorFlow or PyTorch