AI-Driven Process Optimization in Chip Fabrication Training Course

This instructor-led, live training in South Africa (online or onsite) is designed for data scientists and software engineers who wish to utilise AdaBoost to build boosting algorithms for machine learning with Python.

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

• Establish the necessary development environment to begin constructing machine learning models with AdaBoost.
• Grasp the ensemble learning approach and understand how to implement adaptive boosting.
• Acquire the skills to construct AdaBoost models that enhance machine learning algorithms in Python.
• Employ hyperparameter tuning to boost the accuracy and performance of AdaBoost models.

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.

This instructor-led live training in South Africa (online or onsite) targets participants with varying levels of expertise who wish to utilise Google's AutoML platform to construct tailored chatbots for diverse applications.

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

• Grasp 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 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.

This instructor-led, live training in South Africa (online or onsite) is tailored for data scientists and analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

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

• Load datasets into DataRobot to analyse, assess, and perform quality checks on data.
• Build and train models to identify key variables and achieve prediction objectives.
• Interpret models to derive valuable insights that support strategic business decisions.
• Monitor and manage models to ensure optimised prediction performance.

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 designed for data scientists, data analysts, and developers keen on exploring AutoML products and features to create and deploy custom ML training models with minimal effort.

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

• Explore the AutoML product range to implement various services for different data types.
• Prepare and label datasets to create custom ML models.
• Train and manage models to produce accurate and fair machine learning models.
• Make predictions using trained models to meet business objectives and needs.

Kubeflow is an open-source platform engineered to simplify the creation, training, and deployment of machine learning workloads on Kubernetes.

This instructor-led, live training (available online or onsite) targets beginner to intermediate-level professionals aiming to build robust ML workflows utilizing Kubeflow.

Upon completing this training, participants will acquire the skills to:

• Navigate the Kubeflow ecosystem and its core components.
• Construct reproducible workflows using Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Deploy machine learning models efficiently via Kubeflow Serving.

Course Format

• Guided presentations alongside collaborative discussions.
• Hands-on labs featuring real Kubeflow components.
• Practical exercises to develop end-to-end ML workflows.

Course Customization Options

• Bespoke versions of this training can be arranged to suit your team’s technology stack and project requirements.

This instructor-led live training in South Africa (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on-premise and in the cloud.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led live training, available online or onsite, is tailored for developers who wish to utilise Google’s ML Kit to build machine learning models optimised for mobile device processing.

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

• Set up the necessary development environment to commence developing machine learning features for mobile applications.
• Integrate new machine learning technologies into Android and iOS applications using the ML Kit APIs.
• Enhance and optimise existing applications by employing the ML Kit SDK for on-device processing and deployment.

This instructor-led live training in South Africa (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.

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

• Set up the necessary development environment to start building machine learning models with Random forest.
• Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
• Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
• Evaluate and optimise machine learning model performance by tuning the hyperparameters.

This instructor-led live training in South Africa (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 utilize analytical tools for time series forecasting.