Advanced Machine Learning with Python Training Course

This programme explores the application of AI—focusing on Machine Learning and Deep Learning—within the automotive industry. It guides learners in identifying technologies that can be applied across various automotive scenarios, ranging from basic automation and image recognition to autonomous decision-making.

An exploration of artificial intelligence fundamentals demonstrates how intelligent technologies are transforming digital strategy, automation, and decision-making processes within enterprise operations. This course examines core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside 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 enhance business agility.

This instructor-led, live training in Botswana (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. Neural Networks are frequently utilised within Machine Learning (ML) applications, which constitute one implementation of AI. Deep Learning represents a subset of Machine Learning.

This course on Practical AI Development from the Ground Up in Python empowers programmers and data analysts with essential techniques for constructing machine learning solutions entirely from scratch using Python. It explores 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 structures. Participants will examine established techniques for utilising scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The programme assists professionals in implementing practical machine learning models, assessing algorithmic constraints, and completing applied projects designed to solve real-world problems.

This instructor-led live training in Botswana (online or on-site) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code 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 Botswana (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:

• 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, live training in Botswana (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 the principles of reinforcement learning with deep learning structures, empowering agents to make decisions by interacting with their surroundings. This approach drives numerous contemporary AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. Through reward-based learning, DRL enables artificial agents to learn strategies, optimise policies, and execute autonomous decisions based on trial and error.

This instructor-led live training (available online or onsite) is designed for intermediate-level developers and data scientists keen to master and apply 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 underpinning 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 real-world scenarios such as gaming, robotics, and decision optimisation.
• Troubleshoot, visualise, and optimise training performance using modern tools.

Course Format

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

Customization Options

• To request a customized version of this course (for instance, using PyTorch instead of TensorFlow), please contact us to make arrangements.

This instructor-led, live training in Botswana (online or onsite) is targeted at intermediate-level developers, data scientists, and AI practitioners who wish to leverage 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 optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Botswana (online or on-site) 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 Botswana (online or onsite) targets developers or data scientists who wish to utilise Horovod for distributed deep learning training and scale it 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 commence deep learning training.
• Install and configure Horovod to train models using TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to operate on multiple GPUs.

This course starts by providing you with a conceptual understanding of neural networks and machine learning algorithms in general, with a specific focus on deep learning (algorithms and applications).

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

Part-2 (20%) of this training introduces Theano, a Python library that simplifies the creation of deep learning models.

Part-3 (40%) of the training is extensively based on TensorFlow, the API of Google's open-source software 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 apply TensorFlow to their Deep Learning projects.

Upon completing this course, delegates will:

• possess a strong understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow's structure and deployment mechanisms
• be capable of carrying out installation, production environment, and architecture tasks as well as configuration
• be able to assess code quality, perform debugging, and monitoring
• be able to implement advanced production tasks such as training models, building graphs, and logging

This instructor-led, live training in Botswana (online or on-site) targets 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.