Introduction to Machine Learning Training Course

This instructor-led live training in Nigeria (offered online or onsite) is tailored for data scientists and software engineers who wish to utilize AdaBoost to build boosting algorithms for machine learning with Python.

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

• Set up the required development environment to begin building machine learning models with AdaBoost.
• Comprehend the ensemble learning approach and learn how to implement adaptive boosting.
• Learn the process of building AdaBoost models to enhance machine learning algorithms in Python.
• Use hyperparameter tuning to improve the accuracy and performance of AdaBoost models.

This course explores the application of AI, with a particular focus on Machine Learning and Deep Learning, within the automotive industry. It guides participants in identifying technologies that can be (potentially) applied across various scenarios in a vehicle, ranging from basic automation and image recognition to autonomous decision-making processes.

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 Nigeria (online or onsite) is designed for 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 perform "intelligent" tasks. Neural Networks are frequently utilized in Machine Learning (ML) applications, which represent one implementation of AI. Deep Learning constitutes a specific subset of Machine Learning.

This instructor-led, live training in Nigeria (online or onsite) is designed for participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications.

By the end 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 optimize chatbot performance over time.

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

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

• Load datasets in DataRobot to analyze, assess, and quality check data.
• Build and train models to identify important variables and meet prediction targets.
• Interpret models to create valuable insights that are useful in making business decisions.
• Monitor and manage models to maintain an optimized prediction performance.

This instructor-led, live training in Nigeria (online or onsite) is tailored for 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 Nigeria (online or onsite) is aimed at data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.

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

• Explore the AutoML product line to implement different services for various 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 professionals eager to establish robust ML workflows via Kubeflow.

After completing this training, participants will acquire the competencies to:

• Traverse the Kubeflow ecosystem and its fundamental components.
• Create reproducible workflows utilizing Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Efficiently deploy machine learning models using Kubeflow Serving.

Course Format

• Structured presentations alongside collaborative discussions.
• Practical labs involving real Kubeflow components.
• Functional exercises designed to build comprehensive ML workflows.

Course Customization Options

• Tailored versions of this training can be arranged to suit your team’s specific technology stack and project needs.

This instructor-led, live training in Nigeria (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.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training (online or onsite) is designed for developers who wish to utilize Google’s ML Kit to construct machine learning models optimized for mobile device processing.

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

• Configure the necessary development environment to begin creating machine learning features for mobile apps.
• Integrate new machine learning technologies into Android and iOS applications using ML Kit APIs.
• Enhance and optimize existing applications by employing the ML Kit SDK for on-device processing and deployment.

This instructor-led, live training in Nigeria (online or onsite) is designed for 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 optimize machine learning model performance by tuning the hyperparameters.

This instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize 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.