Advanced Machine Learning with Python Training Course

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.

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 instructor-led, live training in Australia (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.

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.

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.

This instructor-led live training in Australia (online or onsite) is designed for researchers and developers who want to use Chainer to build and train neural networks in Python, ensuring the code remains easy to debug.

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

• Set up the necessary development environment to begin developing neural network models.
• Define and implement neural network models using clear and understandable source code.
• Run examples and modify existing algorithms to optimise deep learning training models while leveraging GPUs for high performance.

This instructor-led, live training in Australia (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.

This instructor-led, live training in Australia (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.

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.

This instructor-led, live training in Australia (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 Australia (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.

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

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in Australia (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

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

• Set up the necessary development environment to start running deep learning trainings.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to run on multiple GPUs.

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

This instructor-led, live training in Australia (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.