Machine Learning Training in Adelaide

This instructor-led, live training in Adelaide (online or onsite) is designed for professional beginners who aim to grasp the concept of pre-trained models and learn how to apply them to solve real-world problems without constructing models from the ground up.

Upon completion 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 specific use cases.
• Fine-tune a pre-trained model for particular tasks.
• Implement pre-trained models in straightforward machine learning projects.

This instructor-led live training in Adelaide (online or onsite) is aimed at participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customised chatbots for various applications.

By the end of this training, participants will be able to:

• Understand 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 Adelaide (online or onsite) is designed for intermediate-level AI developers, machine learning engineers, and system architects who wish to optimise AI models for edge deployment.

Upon completion of this training, participants will be able to:

• Grasp the challenges and requirements associated with deploying AI models on edge devices.
• Apply model compression techniques to decrease the size and complexity of AI models.
• Utilise quantisation methods to improve model efficiency on edge hardware.
• Implement pruning and other optimisation techniques to enhance model performance.
• Deploy optimised AI models across various edge devices.

This instructor-led live training in Adelaide (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

By the end of this training, participants will be able to:

• Understand the principles of Edge AI and its benefits.
• Set up and configure the edge computing environment.
• Develop, train, and optimise AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

This instructor-led, live training in Adelaide (online or onsite) is designed for advanced AI engineers and data scientists with intermediate to extensive experience who wish to enhance DeepSeek model performance, minimise latency, and deploy AI solutions efficiently using contemporary MLOps practices.

Upon completion of this training, participants will be equipped to:

• Optimise DeepSeek models to enhance efficiency, accuracy, and scalability.
• Implement industry best practices for MLOps and model versioning.
• Deploy DeepSeek models across both cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

MLOps on Kubernetes provides a framework for automating the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led live training (available online or onsite) is designed for intermediate-level practitioners aiming to construct automated, scalable MLOps pipelines on Kubernetes.

Upon completion of this training, participants will be able to:

• Design end-to-end CI/CD pipelines tailored for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises focused on building real-world CI/CD workflows.
• Live-lab practice deploying ML workloads to Kubernetes.

Course Customization Options

• Organizations may request tailored content aligned with their internal MLOps tools and infrastructure.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customised versions of this training can be arranged to align with your team’s technology stack and project requirements.

TinyML involves the deployment of optimised machine learning models onto edge devices with limited resources.

This instructor-led, live training (available online or onsite) is designed for advanced technical professionals keen on designing, optimising, and deploying complete TinyML pipelines.

Upon completing this training, participants will gain the ability to:

• Gather, prepare, and manage datasets for TinyML applications.
• Train and optimise models for low-power microcontrollers.
• Convert models into lightweight formats suitable for edge devices.
• Deploy, test, and monitor TinyML applications in real hardware environments.

Course Format

• Instructor-guided lectures and technical discussion.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Customisation Options

• To tailor the training to specific toolchains, hardware boards, or internal workflows, please contact us to arrange.

This instructor-led, live training in Adelaide (available online or onsite) is geared towards intermediate-level developers, data scientists, and AI practitioners who aim to utilise TensorFlow Lite for Edge AI applications.

By the conclusion of this training, participants will be able to:

• Comprehend the fundamentals of TensorFlow Lite and its function in Edge AI.
• Construct and enhance AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models across various edge devices.
• Apply tools and techniques for model conversion and optimisation.
• Develop practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Adelaide (online or onsite) is designed for advanced-level professionals looking to master the technologies underpinning autonomous systems.

Upon completion of this training, participants will be able to:

• Design and deploy AI models for autonomous decision-making.
• Develop control algorithms for autonomous navigation and obstacle avoidance.
• Ensure safety and reliability in AI-driven autonomous systems.
• Integrate autonomous systems with existing robotics and AI frameworks.

This instructor-led, live training in Adelaide (online or on-site) is tailored for advanced-level professionals seeking to deepen their grasp of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models via Google Colab.

Upon completion of this training, participants will be capable of:

• Constructing and training convolutional neural networks (CNNs) using TensorFlow.
• Leveraging Google Colab for scalable and efficient cloud-based model development.
• Implementing image preprocessing techniques for computer vision tasks.
• Deploying computer vision models for real-world applications.
• Employing transfer learning to enhance the performance of CNN models.
• Visualising and interpreting the results of image classification models.

TinyML involves the deployment of machine learning models onto low-power, resource-constrained devices that operate at the network edge.

This instructor-led live training (available online or on-site) is designed for advanced professionals seeking to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

Upon completion of this course, participants will be able to:

• Identify security risks specific to on-device TinyML inference.
• Implement privacy-preserving mechanisms for edge AI deployments.
• Harden TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in constrained environments.

Format of the Course

• Engaging lectures supported by expert-led discussions.
• Practical exercises emphasizing real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tooling.

Course Customization Options

• Organizations may request a tailored version of this training to align with their specific security and compliance needs.

This instructor-led, live training in Adelaide (online or onsite) is designed for advanced-level professionals who wish to deepen their knowledge of machine learning models, improve their hyperparameter tuning skills, and learn how to effectively deploy models 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.
• Optimise 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 Adelaide (online or onsite) targets intermediate-level professionals keen on leveraging AI techniques to optimise yield management in semiconductor manufacturing.

Upon completion of this training, participants will be equipped to:

• Analyse production data to pinpoint factors influencing yield rates.
• Deploy AI algorithms to enhance yield management processes.
• Optimise production parameters to curtail defects and improve yields.
• Integrate AI-driven yield management solutions into current production workflows.

This instructor-led, live training in Adelaide (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:

• Understand how machine learning fits within AI and business strategy.
• Apply supervised and unsupervised learning techniques to structured business problems.
• Preprocess and transform data for modelling.
• Use neural networks for classification and prediction tasks.
• Perform 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 Adelaide (online or onsite) is aimed at intermediate-level professionals who wish to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to enhance production efficiency and reduce unexpected equipment failures.

By the end of this training, participants will be able to:

• Implement AI models for predicting equipment failures in semiconductor manufacturing.
• Analyse maintenance data to identify patterns and trends indicative of potential issues.
• Integrate AI-driven predictive maintenance into existing manufacturing workflows.
• Reduce downtime and maintenance costs through proactive equipment management.

This instructor-led, live training in Adelaide (online or onsite) is aimed at 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 Adelaide (online or onsite) is designed for intermediate-level data scientists and developers who wish to comprehend and apply deep learning techniques within the Google Colab environment.

Upon completion 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 in Adelaide (online or in-person) targets intermediate-level professionals keen on understanding and deploying AI techniques to optimise semiconductor fabrication processes.

Upon completion of this training, participants will be able to:

• Comprehend AI methodologies for process optimisation in chip fabrication.
• Deploy AI models to improve yield and minimise defects.
• Analyse process data to pinpoint critical parameters for optimisation.
• Apply machine learning techniques to fine-tune semiconductor manufacturing operations.

This instructor-led, live training in Adelaide (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 Adelaide (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.
• Optimize and evaluate machine learning models effectively.

TinyML involves the deployment of machine learning models onto hardware with severely limited resources.

This guided, live training session (available online or in-person) is designed for advanced practitioners seeking to optimise TinyML models for low-latency, memory-efficient deployment on embedded devices.

Upon completing this training, participants will be able to:

• Apply quantisation, pruning, and compression techniques to shrink model size while maintaining accuracy.
• Benchmark TinyML models for latency, memory usage, and energy efficiency.
• Implement optimised inference pipelines on microcontrollers and edge devices.
• Evaluate the trade-offs between performance, accuracy, and hardware constraints.

Format of the Course

• Instructor-led presentations supported by technical demonstrations.
• Practical optimisation exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines in a controlled lab environment.

Course Customisation Options

• For tailored training aligned with specific hardware platforms or internal workflows, please contact us to customise the programme.

This instructor-led, live training in Adelaide (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.

By the end of this training, participants will be able to:

• Understand the challenges of 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 Adelaide (online or onsite) is aimed at beginner-level professionals who wish to understand and apply AI technologies within the semiconductor manufacturing industry.

By the end of this training, participants will be able to:

• Understand the basic principles of AI and how they apply to semiconductor manufacturing.
• Identify areas within semiconductor manufacturing where AI can be effectively implemented.
• Utilize AI tools and techniques to enhance production efficiency and quality control.
• Implement basic AI models to optimize manufacturing processes.

Docker is a containerisation platform utilised to construct reproducible, portable, and scalable environments for machine learning systems.

This instructor-led, live training (available online or onsite) targets intermediate to advanced-level technical professionals looking to containerise and operationalise complete ML pipelines using Docker.

Upon completion of this training, participants will be able to:

• Containerise ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines using Docker and supporting tools.
• Implement versioning, reproducibility, and CI/CD for ML components.
• Deploy, monitor, and scale ML services within containerised environments.

Course Format

• Interactive lectures supported by practical demonstrations.
• Hands-on exercises focused on building real ML pipeline components.
• Live-lab implementation for end-to-end containerised workflows.

Course Customisation Options

• For customised training aligned with specific ML infrastructure needs, please contact us to discuss options.

This instructor-led, live training in Adelaide (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 Adelaide (online or onsite) is designed for intermediate-level data professionals looking to apply machine learning techniques to data-driven business challenges, such as sales forecasting and predictive modelling using neural networks.

Upon completion of this training, participants will be able to:

• Grasp the fundamental concepts and types of machine learning.
• Apply essential algorithms for classification, regression, clustering, and association analysis.
• Conduct exploratory data analysis and data preparation using Python.
• Utilise 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 Adelaide (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimise performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalisation.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training in Adelaide (online or onsite) is designed for intermediate to advanced cybersecurity professionals seeking to elevate their skills in AI-driven threat detection and incident response.

Upon completion of this training, participants will be able to:

• Implement advanced AI algorithms for real-time threat detection.
• Customise AI models to address specific cybersecurity challenges.
• Develop automation workflows for threat response.
• Protect AI-driven security tools against adversarial attacks.

This instructor-led, live training in Adelaide (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers looking to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Configure a development environment suitable for TinyML projects.
• Train, optimise, and deploy AI models on low-power microcontrollers.
• Utilise TensorFlow Lite and Edge Impulse to build real-world TinyML applications.
• Optimise AI models for power efficiency and memory limitations.

This instructor-led, live training in Adelaide (online or onsite) targets beginner-level cybersecurity professionals keen on leveraging AI to enhance their threat detection and response capabilities.

By the end of this training, participants will be able to:

• Grasp AI applications in cybersecurity.
• Deploy AI algorithms for threat detection.
• Automate incident response using AI tools.
• Integrate AI into existing cybersecurity infrastructure.

This instructor-led, live training in Adelaide (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 Adelaide (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 utilise 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.

This instructor-led, live training in Adelaide (online or in-person) is designed for beginner-level engineers and data scientists keen on grasping the core principles of TinyML, investigating its use cases, and deploying AI models onto microcontrollers.

Upon completion of this training, participants will be capable of:

• Comprehending the fundamental concepts of TinyML and its importance.
• Implementing lightweight AI models on microcontrollers and edge devices.
• Optimising and fine-tuning machine learning models to minimise power usage.
• Applying TinyML to practical scenarios such as gesture recognition, anomaly detection, and audio processing.

This course aims to build practical proficiency in applying Machine Learning techniques. Utilising the Python programming language and its extensive library ecosystem, alongside numerous hands-on examples, the programme teaches how to leverage the essential components of Machine Learning, make informed data modelling decisions, interpret algorithm outputs, and validate results.

Our objective is to equip you with the confidence to use the most fundamental tools from the Machine Learning toolbox and help you avoid common pitfalls associated with Data Science applications.

Applied AI from Scratch in Python provides programmers and data analysts with the essential techniques required to construct machine learning solutions from the ground up using Python. It covers the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network architectures. The course examines effective strategies for utilising scikit-learn, Apache Spark MLlib, and Jupyter notebooks for practical AI development. It assists professionals in implementing functional ML models, assessing algorithmic limitations, and completing applied projects to solve real-world problems.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions through direct interaction with their surroundings. This technology drives numerous contemporary AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn strategies, optimise policies, and make autonomous choices based on trial-and-error mechanisms utilising reward-based learning.

This instructor-led, live training (available online or onsite) is designed for intermediate-level developers and data scientists keen on learning and applying Deep Reinforcement Learning techniques to construct intelligent agents capable of autonomous decision-making within complex environments.

Upon completion of this training, participants will be able to:

• Grasp the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement core RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to practical applications such as gaming, robotics, and decision optimisation.
• Troubleshoot, visualise, and enhance training performance using contemporary tools.

Course Format

• Interactive lectures and guided discussions.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Course Customisation Options

• To request a customised version of this course (e.g., using PyTorch instead of TensorFlow), please contact us to make arrangements.

An exploration of artificial intelligence fundamentals reveals how intelligent technology reshapes digital strategy, automation, and decision making across enterprise operations. Examines core concepts spanning AI history, problem-solving frameworks, knowledge representation, uncertain reasoning, and machine learning paradigms alongside communication, perception, and autonomous action. Guides executives and architects to evaluate AI-driven transformation opportunities, assess emerging technology trends, and integrate practical intelligent solutions to accelerate business agility.

This course explores the application of AI—specifically Machine Learning and Deep Learning—within the automotive sector. It assists learners in identifying technologies that can be applied across various automotive scenarios, ranging from basic automation and image recognition to complex autonomous decision-making systems.

This eight-day programme takes participants on a comprehensive journey, building upon solid Python engineering foundations to master advanced AI system design. Attendees will cultivate disciplined coding habits, gain proficiency in statistical and deep learning techniques, and construct generative AI and agent-based systems ready for production. The emphasis is placed on reliability, evaluation, safety, and real-world deployment, moving beyond mere experimentation.

Artificial Neural Networks serve as a computational data model employed in creating Artificial Intelligence (AI) systems capable of executing tasks considered 'intelligent'. Neural Networks are frequently utilised within Machine Learning (ML) applications, which represent one specific implementation of AI. Deep Learning constitutes a specialised subset of Machine Learning.

Enhance your data science skills with this comprehensive Machine Learning training course, which covers fundamental algorithms such as Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques. You will gain practical experience alongside theoretical knowledge, utilising real-world examples. This course is ideal for data analysts, software engineers, AI enthusiasts, and business professionals aiming to implement machine learning solutions. Learn to master classification performance metrics, cross-validation, the bias-variance trade-off, and deep learning fundamentals to construct robust predictive models.

This instructor-led, live training in Adelaide (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 Adelaide (online or on-site) is targeted at data scientists who wish to utilise 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 analysing fraud data.

Machine learning constitutes a branch of Artificial Intelligence where computers possess the capability to learn from data without being explicitly programmed for every task.

Deep learning is a specialized subfield of machine learning that employs techniques based on learning complex data representations and structures, particularly through neural networks.

Python is a high-level programming language widely recognised for its clear syntax and high code readability.

In this instructor-led live training, participants will explore how to implement deep learning models tailored for the telecommunications sector by working through the creation of a deep learning credit risk model.

Upon completing this training, participants will be equipped to:

• Grasp the fundamental concepts underpinning deep learning.
• Identify and understand the applications and uses of deep learning within the telecom industry.
• Utilise Python, Keras, and TensorFlow to construct deep learning models for telecommunications.
• Develop their own deep learning customer churn prediction model using Python.

Course Format

• Interactive lectures and group discussions.
• Numerous exercises and practical practice sessions.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

• To request customised training for this course, please contact us to arrange.

This hands-on, instructor-led course serves as a logical follow-up to the Python for Data Analysis course.

It introduces participants to the fundamental concepts of Machine Learning and demonstrates how to apply these directly to data analysis tasks like prediction, classification, and segmentation.

The emphasis is on grasping how Machine Learning functions in practice, utilising well-known tools such as Python, Pandas, and Jupyter Notebook, without the need for an advanced mathematical background.

This instructor-led, live training in Adelaide (online or onsite) is designed for engineers looking to assess the current approaches and tools available. This enables them to make informed decisions on how to adopt MLOps within their organisation.

Upon completion of this training, participants will be able to:

• Install and configure a range of MLOps frameworks and tools.
• Assemble a team with the appropriate skills to build and support an MLOps system.
• Prepare, validate, and version data for use by ML models.
• Understand the components of an ML Pipeline and identify the tools required to build one.
• Experiment with different machine learning frameworks and servers for production deployment.
• Operationalise the entire Machine Learning process to ensure it is reproducible and maintainable.

This instructor-led, live training in Adelaide (online or onsite) targets intermediate-level data analysts, developers, or aspiring data scientists keen on leveraging machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

Upon completing this course, participants will be able to:

• Grasp and distinguish between key machine learning paradigms.
• Investigate data preprocessing techniques and model evaluation metrics.
• Implement machine learning algorithms to address real-world data challenges.
• Utilise Python libraries and Jupyter notebooks for practical development.
• Construct models for prediction, classification, recommendation, and clustering.

This instructor-led live training in Adelaide (online or onsite) is tailored for developers and data scientists who wish to utilise TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and more.

By the end of this training, participants will be able to:

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This course provides foundational knowledge of neural networks, as well as broader concepts within machine learning algorithms, deep learning frameworks, and their practical applications.

The first part of this training (40%) concentrates on fundamental principles, aiding you in selecting the appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

The second part of the training (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

The final portion of the training (40%) focuses extensively on TensorFlow, which is Google's open-source library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers who wish to utilise TensorFlow for their deep learning projects.

Upon completion of this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand the structure and deployment mechanisms of TensorFlow
• be capable of managing installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production-level tasks such as training models, building graphs, and logging