Advanced Machine Learning with Python Training Course

This course explores the application of AI, with a specific focus on Machine Learning and Deep Learning, within the automotive sector. It assists participants in identifying technologies that can be utilised across various vehicle scenarios, ranging from basic automation and image recognition to autonomous decision-making.

An exploration of artificial intelligence fundamentals demonstrates how intelligent technology transforms digital strategy, automation, and decision-making across enterprise operations. This course examines core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, as well as communication, perception, and autonomous action. It equips executives and architects with the insights needed 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 South Africa (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.

An Artificial Neural Network is a computational data model employed in the creation of Artificial Intelligence (AI) systems that can execute 'intelligent' tasks. These networks are frequently utilized within Machine Learning (ML) applications, which serve as one implementation of AI. Furthermore, Deep Learning represents a specialized subset of Machine Learning.

Applied AI from Scratch in Python provides developers and data analysts with the essential techniques required to construct machine learning solutions from the ground up using Python. The course covers core 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. It explores proven methodologies for utilising scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. This enables professionals to implement practical machine learning models, assess algorithmic limitations, and execute applied projects designed for real-world problem solving.

This instructor-led live training, conducted in South Africa (online or onsite), is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while 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 start developing neural network models.
• Define and implement neural network models using a comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

This instructor-led, live training in South Africa (online or onsite) is aimed at advanced-level professionals who wish 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:

• Construct and train convolutional neural networks (CNNs) using TensorFlow.
• Utilise 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.
• Apply transfer learning to enhance the performance of CNN models.
• Visualise and interpret the results of image classification models.

This instructor-led, live training in South Africa (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) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions through interaction with their surroundings. This technology drives numerous modern AI advancements, including self-driving cars, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimise policies, and make autonomous decisions via trial and error using reward-based learning.

This instructor-led, live training (available online or on-site) is designed for intermediate-level developers and data scientists who wish to learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in complex environments.

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

• Grasp the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement key RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Build and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to real-world applications such as games, robotics, and decision optimization.
• Troubleshoot, visualise, and optimise training performance using modern tools.

Format of the Course

• Interactive lecture and guided discussion.
• 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 arrange.

This instructor-led live training in South Africa (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 South Africa (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 South Africa (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale them up to operate 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 a conceptual foundation in neural networks, machine learning algorithms, and deep learning (including algorithms and applications).

Part 1 (40%) of this training focuses primarily on fundamentals but will assist you in selecting the appropriate technology, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part 2 (20%) of this training introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part 3 (40%) of the training is heavily centred on TensorFlow, the open-source software library for Deep Learning developed by Google. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers looking to utilise TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow’s structure and deployment mechanisms
• be capable of performing 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 model training, graph construction, and logging

Delivered as instructor-led live training in South Africa (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.