Delivered online or on-site, our instructor-led live Machine Learning (ML) training programmes offer hands-on practice in applying machine learning techniques and tools to solve real-world problems across various industries. NobleProg’s ML courses cover a range of programming languages and frameworks, including Python, the R language, and Matlab. These courses are tailored for specific industry applications, such as Finance, Banking, and Insurance, and encompass both the fundamentals of Machine Learning and advanced approaches like Deep Learning.
Machine Learning training is available as either “online live training” or “onsite live training”. Online live training (also known as “remote live training”) is conducted through an interactive remote desktop. Onsite live training can be delivered locally at your customer premises in Durban or at NobleProg’s corporate training centers in Durban.
NobleProg – Your Local Training Provider
Garden Court South Beach
Durban, South Africa
Way to get there
From Durban city centre: follow the beachfront/Marine Parade south toward South Beach.
By car/taxi: give the driver the address 73 O.R. Tambo Parade, South Beach, Durban 4001.
From King Shaka International Airport: the hotel is roughly 30 km ;from the airport.
This instructor-led, live training in Durban (online or onsite) is designed for beginner-level professionals who wish to grasp the concept of pre-trained models and learn how to apply them to solve real-world problems without having to build models from scratch.
By the end of this training, participants will be able to:
Comprehend the concept and advantages of pre-trained models.
Explore various pre-trained model architectures and their use cases.
Fine-tune a pre-trained model for specific tasks.
Implement pre-trained models in simple machine learning projects.
This instructor-led live training in Durban (online or onsite) targets participants with varying levels of expertise who wish to utilise Google's AutoML platform to construct tailored chatbots for diverse applications.
By the conclusion of this training, participants will be able to:
Grasp the fundamentals of chatbot development.
Navigate the Google Cloud Platform and access AutoML.
Prepare data for training chatbot models.
Train and evaluate custom chatbot models using AutoML.
Deploy and integrate chatbots into various platforms and channels.
Monitor and optimise chatbot performance over time.
This instructor-led, live training in Durban (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimise AI models for edge deployment.
By the end of this training, participants will be able to:
Understand the challenges and requirements of deploying AI models on edge devices.
Apply model compression techniques to reduce the size and complexity of AI models.
Utilise quantisation methods to enhance model efficiency on edge hardware.
Implement pruning and other optimisation techniques to improve model performance.
Deploy optimised AI models on various edge devices.
This live, instructor-led training delivered in Durban (either online or on-site) is designed for intermediate-level developers, data scientists, and tech enthusiasts aiming to acquire practical expertise in deploying AI models on edge devices for a variety of applications.
Upon completion of the training, participants will be able to:
Comprehend the fundamentals of Edge AI and its inherent benefits.
Configure and set up a robust edge computing environment.
Build, train, and optimise AI models specifically for edge deployment.
Implement functional AI solutions on edge hardware.
Monitor and enhance the performance of edge-deployed models.
Manage ethical and security aspects within Edge AI ecosystems.
This instructor-led, live training in Durban (online or on-site) is designed for advanced AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, reduce latency, and efficiently deploy AI solutions using modern MLOps practices.
By the end of this training, participants will be able to:
Optimise DeepSeek models for efficiency, accuracy, and scalability.
Implement best practices for MLOps and model versioning.
Deploy DeepSeek models on both cloud and on-premise infrastructure.
Monitor, maintain, and scale AI solutions effectively.
This live training in Durban empowers intermediate practitioners to construct automated MLOps pipelines on Kubernetes. Participants will learn to design CI/CD workflows, implement GitOps strategies, and deploy ML models using containerized infrastructure to achieve scalable and reproducible machine learning operations.
This hands-on training in Durban provides you with the necessary skills to design, train, and serve machine learning models on Kubernetes using Kubeflow. You will learn to navigate the ecosystem, author scalable pipelines, and manage production-ready workloads by adhering to best practices.
This expert-led session in Durban empowers advanced professionals with the capability to design, optimise, and deploy complete TinyML pipelines. Learners will acquire the skills to gather data, train low-energy models, and test practical applications through hands-on labs.
This instructor-led live training in Durban (online or onsite) targets intermediate-level developers, data scientists, and AI practitioners who want to utilise TensorFlow Lite for Edge AI applications.
By the end of this training, participants will be able to:
Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
Develop and optimise AI models using TensorFlow Lite.
Deploy TensorFlow Lite models on various edge devices.
Utilise tools and techniques for model conversion and optimisation.
Implement practical Edge AI applications using TensorFlow Lite.
This instructor-led, live training in Durban (online or onsite) is aimed at advanced-level professionals who wish to master the technologies behind autonomous systems.
By the end of this training, participants will be able to:
Design and implement AI models for autonomous decision-making.
Develop control algorithms for autonomous navigation and obstacle avoidance.
Ensure safety and reliability in AI-powered autonomous systems.
Integrate autonomous systems with existing robotics and AI frameworks.
This instructor-led, live training in Durban (delivered online or onsite) is tailored for advanced-level professionals looking to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.
By the end of this training, participants will be able to:
Build and train convolutional neural networks (CNNs) using TensorFlow.
Leverage Google Colab for scalable and efficient cloud-based model development.
Implement image preprocessing techniques for computer vision tasks.
Deploy computer vision models for real-world applications.
Use transfer learning to enhance the performance of CNN models.
Visualize and interpret the results of image classification models.
This instructor-led training in Durban enables advanced professionals to secure TinyML pipelines on edge devices. You will learn to implement privacy-preserving techniques, harden models against adversarial threats, and apply best practices for secure data handling in constrained environments.
This instructor-led, live training in Durban (online or onsite) is designed for advanced professionals wishing to strengthen their expertise in machine learning models, hone their hyperparameter tuning capabilities, and learn effective model deployment using Google Colab.
By the end of this training, participants will be able to:
Implement advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
Optimize model performance through hyperparameter tuning.
Deploy machine learning models in real-world applications using Google Colab.
Collaborate and manage large-scale machine learning projects in Google Colab.
This instructor-led live training in Durban (online or onsite) is aimed at intermediate-level professionals who wish to apply AI techniques to optimise yield management in semiconductor manufacturing.
By the end of this training, participants will be able to:
Analyse production data to identify factors affecting yield rates.
Implement AI algorithms to enhance yield management processes.
Optimise production parameters to reduce defects and improve yields.
Integrate AI-driven yield management into existing production workflows.
This instructor-led, live training in Durban (online or onsite) is aimed at intermediate-level business and AI professionals who wish to apply machine learning in business, forecasting, and AI-driven systems using real case studies and Python-based tools.
By the end of this training, participants will be able to:
Grasp how machine learning integrates with AI and business strategy.
Apply supervised and unsupervised learning techniques to structured business problems.
Preprocess and transform data for modelling purposes.
Utilise neural networks for classification and prediction tasks.
Conduct sales forecasting using statistical and ML-based methods.
Implement clustering and association rule mining for customer segmentation and pattern discovery.
This instructor-led, live training in Durban (online or onsite) is designed for intermediate-level professionals seeking to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to enhance production efficiency and reduce unexpected equipment failures.
Upon completion of this training, participants will be able to:
Deploy AI models to predict equipment failures in semiconductor manufacturing settings.
Analyse maintenance data to uncover patterns and trends that signal potential issues.
Integrate AI-driven predictive maintenance protocols into current manufacturing workflows.
Minimise downtime and maintenance costs through proactive equipment management.
This instructor-led, live training in Durban (online or onsite) targets advanced professionals who wish to apply cutting-edge AI techniques to semiconductor design automation, improving efficiency, accuracy, and innovation in chip design and verification.
By the end of this training, participants will be able to:
Apply advanced AI techniques to optimise semiconductor design processes.
Integrate machine learning models into EDA tools for enhanced design verification.
Develop AI-driven solutions for complex design challenges in chip fabrication.
Leverage neural networks for improving the accuracy and speed of design automation.
This instructor-led, live training in Durban (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.
By the end of this training, participants will be able to:
Set up and navigate Google Colab for deep learning projects.
Understand the fundamentals of neural networks.
Implement deep learning models using TensorFlow.
Train and evaluate deep learning models.
Utilize advanced features of TensorFlow for deep learning.
This instructor-led, live training Durban (online or onsite) is designed for intermediate-level professionals who wish to understand and apply AI techniques for optimising semiconductor fabrication processes.
Upon completion of this training, participants will be capable of:
Grasping AI methodologies for process optimisation in chip fabrication.
Deploying AI models to enhance yield rates and minimise defects.
Analysing process data to pinpoint critical parameters for optimisation.
Applying machine learning techniques to refine semiconductor manufacturing processes.
This instructor-led live training in Durban (online or onsite) is aimed at intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.
By the end of this training, participants will be able to:
Set up Apache Airflow for machine learning workflow orchestration.
Automate data preprocessing, model training, and validation tasks.
Integrate Airflow with machine learning frameworks and tools.
Deploy machine learning models using automated pipelines.
Monitor and optimize machine learning workflows in production.
This instructor-led, live training in Durban (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently using the Google Colab environment.
By the end of this training, participants will be able to:
Set up and navigate Google Colab for machine learning projects.
Understand and apply various machine learning algorithms.
Use libraries like Scikit-learn to analyze and predict data.
Implement supervised and unsupervised learning models.
Optimise and evaluate machine learning models effectively.
This live, instructor-led training in Durban empowers advanced practitioners to optimise TinyML models for embedded devices with limited resources. Learners will apply quantisation and pruning, build low-latency inference pipelines, and measure performance against memory and energy constraints.
Delivered as instructor-led live training in Durban (available online or on-site), this programme is designed for advanced professionals keen to explore state-of-the-art XAI techniques for deep learning models, with a strong emphasis on building interpretable AI systems.
Upon completion of this training, participants will be able to:
Grasp the challenges associated with explainability in deep learning.
Implement advanced XAI techniques for neural networks.
Interpret decisions made by deep learning models.
Evaluate the trade-offs between performance and transparency.
This instructor-led, live training in Durban (online or onsite) is designed for beginner-level professionals who wish to grasp and apply AI technologies within the semiconductor manufacturing sector.
Upon completion of this training, participants will be able to:
Comprehend the fundamental principles of AI and their application in semiconductor manufacturing.
Pinpoint areas within semiconductor manufacturing where AI can be effectively deployed.
Leverage AI tools and methodologies to improve production efficiency and quality control.
Deploy basic AI models to optimize manufacturing processes.
This live, instructor-led course in Durban walks professionals through the process of containerizing complete ML pipelines with Docker. Learners will gain proficiency in creating reproducible environments, managing training and inference workloads, and establishing CI/CD frameworks for scalable MLOps deployments.
This instructor-led, live training in Durban (online or onsite) is aimed at data scientists and developers who wish to use ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.
By the end of this training, participants will be able to:
Install ML.NET and integrate it into the application development environment.
Understand the machine learning principles behind ML.NET tools and algorithms.
Build and train machine learning models to perform predictions with the provided data smartly.
Evaluate the performance of a machine learning model using the ML.NET metrics.
Optimize the accuracy of the existing machine learning models based on the ML.NET framework.
Apply the machine learning concepts of ML.NET to other data science applications.
This instructor-led, live training in Durban (online or onsite) is aimed at intermediate-level data professionals who wish to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modelling using neural networks.
By the end of this training, participants will be able to:
Understand the core concepts and types of machine learning.
Apply key algorithms for classification, regression, clustering, and association analysis.
Perform exploratory data analysis and data preparation using Python.
Use neural networks for nonlinear modelling tasks.
Implement predictive analytics for business forecasting, including sales data.
Evaluate and optimise model performance using visual and statistical techniques.
This instructor-led live training in Durban (online or on-site) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision specialists who aim to expand their skills in deep learning for text-to-image generation.
By the end of this course, participants will be able to:
Understand advanced deep learning architectures and techniques for text-to-image generation.
Implement complex models and optimisations for high-quality image synthesis.
Optimise performance and scalability for large datasets and intricate models.
Tune hyperparameters to enhance model performance and generalisation.
Integrate Stable Diffusion with other deep learning frameworks and tools.
This instructor-led, live training is available online or on-site, designed for intermediate to advanced cybersecurity professionals aiming to elevate their skills in AI-driven threat detection and incident response.
Upon completion of this training, participants will be capable of:
Implementing advanced AI algorithms for real-time threat detection.
Customising AI models to address specific cybersecurity challenges.
Developing automation workflows for threat response.
Securing AI-driven security tools against adversarial attacks.
This instructor-led, live training in Durban (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.
By the end of this training, participants will be able to:
Understand the fundamentals of TinyML and its benefits for edge AI applications.
Set up a development environment for TinyML projects.
Train, optimize, and deploy AI models on low-power microcontrollers.
Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
Optimize AI models for power efficiency and memory constraints.
This instructor-led, live training in Durban (online or onsite) targets entry-level cybersecurity professionals who want to learn how to utilise AI for enhanced threat detection and response capabilities.
Upon completion of this training, participants will be able to:
Gain insight into AI applications within the cybersecurity sector.
Deploy AI algorithms for threat detection.
Automate incident response using AI tools.
Incorporate AI into existing cybersecurity infrastructure.
This instructor-led, live training in Durban (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.
By the end of this training, participants will be able to:
Understand the basic principles of AlphaFold.
Learn how AlphaFold works.
Learn how to interpret AlphaFold predictions and results.
This instructor-led live training in Durban (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilise analytical tools for time series forecasting.
By the end of this training, participants will be able to:
Learn to apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.
This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.
By the end of this training, participants will be able to:
Understand the principles of Stable Diffusion and how it works for image generation.
Build and train Stable Diffusion models for image generation tasks.
Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
Optimize the performance and stability of Stable Diffusion models.
Gain hands-on proficiency in applying Machine Learning methods using Python at this Durban training. The course delves into core algorithms such as regression, classification, and clustering, guiding you through the process of making modeling decisions, interpreting outputs, and validating results using practical, real-world examples.
This practical training in Durban empowers programmers to construct AI models using Python. You will gain proficiency in supervised learning, neural networks, and unsupervised techniques through the use of scikit-learn and Apache Spark. The focus is on hands-on exercises in Jupyter notebooks designed to solve real-world problems.
This instructor-led training in Durban covers the theoretical foundations and practical implementation of Deep Reinforcement Learning using Python. Participants will build and train DRL agents with TensorFlow or PyTorch, applying key algorithms like DQN and PPO to solve complex real-world problems.
A foundational training in Durban that delves into AI fundamentals, ranging from intelligent agents to machine learning. It empowers executives and architects to evaluate emerging AI trends, integrate practical solutions, and boost business agility through automated strategies.
Investigate how Machine Learning and Deep Learning are reshaping the automotive industry. This Durban programme outlines key concepts spanning from basic automation to autonomous decision-making, featuring neural networks and practical TensorFlow examples for real-world implementation.
This intensive 8-day programme guides participants on a comprehensive journey, transforming robust Python engineering skills into the design of advanced AI systems. Learners will cultivate disciplined coding habits, acquire expertise in statistical and deep learning methodologies, and construct generative AI and agent-based systems ready for production environments. The curriculum prioritises reliability, evaluation metrics, safety standards, and real-world deployment strategies over mere experimentation.
This three-day programme focused on Durban delves into both the theoretical underpinnings and practical applications of Artificial Neural Networks, Machine Learning, and Deep Learning. Participants will examine various network architectures, learning mechanisms, and mathematical foundations, progressing from fundamental perceptrons to sophisticated deep learning techniques.
Gain command over Machine Learning algorithms, including Naive Bayes, Decision Trees, Neural Networks, SVMs, and Clustering, through this hands-on Durban course. Develop practical competencies in model evaluation, managing bias-variance trade-offs, and deep learning to construct robust predictive solutions.
This instructor-led, live training in Durban (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
By the end of this training, participants will be able to:
Apply core statistical methods to pattern recognition.
Use key models like neural networks and kernel methods for data analysis.
Implement advanced techniques for complex problem-solving.
Improve prediction accuracy by combining different models.
This instructor-led live training in Durban (available online or onsite) is targeted at data scientists who wish to employ TensorFlow to analyse potential fraud data.
By the end of this training, participants will be able to:
Create a fraud detection model in Python and TensorFlow.
Build linear regressions and linear regression models to predict fraud.
Develop an end-to-end AI application for analyzing fraud data.
This instructor-led live training on Durban explores the fundamentals of AI, machine learning, and deep learning. Participants will utilise Python, Keras, and TensorFlow to develop practical telecom models, including credit risk and churn prediction, thereby acquiring hands-on skills applicable to real-world data science scenarios.
This hands-on, instructor-led training serves as a logical progression from the Python for Data Analysis course.
It introduces participants to the fundamental concepts of Machine Learning, demonstrating how these can be directly applied to data analysis tasks such as prediction, classification, and segmentation.
The emphasis is on grasping the practical application of Machine Learning, utilising familiar tools like Python, Pandas, and Jupyter Notebook, without the need for an advanced mathematical background.
This instructor-led, live training in Durban (available online or on-site) is designed for developers and data scientists looking to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this training, participants will be able to:
Install and configure Kubeflow on premise and in the cloud.
Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
Run entire machine learning pipelines on diverse architectures and cloud environments.
Using Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led live training in Durban (online or at your premises) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.
By the end of this training, participants will be able to:
Install and configure various MLOps frameworks and tools.
Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
Prepare, validate and version data for use by ML models.
Understand the components of an ML Pipeline and the tools needed to build one.
Experiment with different machine learning frameworks and servers for deploying to production.
Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
This instructor-led, live training in Durban (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.
By the end of this course, participants will be able to:
Understand and differentiate key machine learning paradigms.
Explore data preprocessing techniques and model evaluation metrics.
Apply machine learning algorithms to solve real-world data problems.
Use Python libraries and Jupyter notebooks for hands-on development.
Build models for prediction, classification, recommendation, and clustering.
This instructor-led live training in Durban (delivered online or on-site) is designed for developers and data scientists seeking to utilise TensorFlow 2.x to build sophisticated predictors, classifiers, generative models, and neural networks.
By the end of this programme, participants will have the capability to:
Install and configure TensorFlow 2.x effectively.
Comprehend the benefits TensorFlow 2.x offers over earlier versions.
Construct deep learning models.
Implement an advanced image classifier.
Deploy deep learning models to cloud environments, mobile applications, and IoT devices.
This 35-hour course in Durban covers deep neural network fundamentals, including CNNs, RNNs, and generative models like GANs. Participants gain hands-on experience with Theano and TensorFlow, learning to build, train, and deploy production-grade deep learning models for real-world applications.
I thoroughly enjoyed the training and appreciated the deeper dive into the subject of Machine Learning. I appreciated the balance between theory and practical applications, especially the hands-on coding sessions. The trainer provided engaging examples and well-designed exercises that enhanced the learning experience. The course covered a wide range of topics, and Abhi demonstrated excellent expertise by answering all questions with clarity and ease.
Valentina
Course - Machine Learning
The training provided an interesting overview of deep learning models and related methods. The topic was quite new to me, but now I feel like I actually have an idea of what AI and ML can involve, what these terms consist of and how they can be used advantageously. In general, I liked the approach of starting with the statistical background and the basic learning models, such as linear regression, especially emphasizing the exercises in between.
Konstantin - REGNOLOGY ROMANIA S.R.L.
Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning
Interesting knowledge
Gabriel - MINDEF
Course - Machine Learning with Python – 4 Days
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
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