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 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.

This instructor-led live training in Botswana (online or onsite) targets intermediate data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

Upon completing this training, participants will be able to:

• Configure and navigate Google Colab for deep learning projects.
• Grasp the fundamental concepts of neural networks.
• Develop deep learning models using TensorFlow.
• Train and assess deep learning models.
• Leverage advanced TensorFlow features for deep learning.

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 use 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 in Botswana (online or onsite) is aimed at developers and data scientists who wish to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and so on.

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 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.