Introduction to Machine Learning Training Course

This instructor-led live training in Kenya (online or onsite) is intended for data scientists and software engineers who wish to employ AdaBoost to develop boosting algorithms for machine learning using Python.

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

• Set up the necessary development environment to start building machine learning models with AdaBoost.
• Understand the ensemble learning approach and how to implement adaptive boosting.
• Learn how to build AdaBoost models to boost machine learning algorithms in Python.
• Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.

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

A deep dive into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making frameworks within enterprise operations. This course examines core concepts including the history of AI, problem-solving methodologies, knowledge representation techniques, reasoning under uncertainty, and machine learning paradigms, alongside modules on 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 implement practical intelligent solutions to enhance business agility.

This instructor-led live training in Kenya (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 utilized in the creation of Artificial Intelligence (AI) systems capable of executing 'intelligent' tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a specialized subset of Machine Learning.

This instructor-led live training in Kenya (online or on-site) is designed for participants with varying levels of expertise who aim to leverage Google's AutoML platform to construct customized chatbots for diverse applications.

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

• Understand 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 Kenya (online or onsite) offers an introduction to the fields of pattern recognition and machine learning. It covers 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 Kenya (online or onsite) targets data scientists and analysts who want 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 Kenya (online or onsite) is designed for intermediate developers, data scientists, and AI practitioners aiming to harness TensorFlow Lite for Edge AI solutions.

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

• Comprehend the basics of TensorFlow Lite and its significance in Edge AI.
• Construct and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on diverse edge devices.
• Make use of tools and techniques for converting and optimizing models.
• Execute practical Edge AI applications with TensorFlow Lite.

This instructor-led live training in Kenya (online or onsite) is designed for data scientists, data analysts, and developers who want 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:

• Navigate the AutoML product suite to implement diverse services for various data types.
• Prepare and annotate datasets to generate custom ML models.
• Train and oversee models to ensure they yield accurate and fair machine learning outcomes.
• Leverage trained models for making predictions that address business goals and needs.

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

This instructor-led, live training (available online or on-site) is tailored for beginner to intermediate-level professionals aiming to establish reliable ML workflows using Kubeflow.

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

• Navigating the Kubeflow ecosystem and its core components.
• Creating reproducible workflows via Kubeflow Pipelines.
• Executing scalable training jobs on Kubernetes.
• Efficiently serving machine learning models using Kubeflow Serving.

Course Format

• Guided presentations and collaborative discussions.
• Hands-on labs involving real Kubeflow components.
• Practical exercises to construct end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be organized to align with your team’s technology stack and project requirements.

This instructor-led live training in Kenya (online or onsite) targets 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-premises and in the cloud.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run complete machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to create and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led live training (online or on-site) is designed for developers who intend to utilize Google’s ML Kit to build machine learning models optimized for mobile device processing.

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

• Establish the necessary development environment to initiate the creation of machine learning features for mobile apps.
• Incorporate new machine learning technologies into Android and iOS applications using ML Kit APIs.
• Enhance and optimize existing apps by leveraging the ML Kit SDK for on-device processing and deployment.

This instructor-led live training in Kenya (available online or onsite) is tailored for data scientists and software engineers who aim to utilize Random Forest to build machine learning algorithms for extensive datasets.

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

• Establish the necessary development environment to commence building machine learning models with Random Forest.
• Comprehend the advantages of Random Forest and learn how to implement it to address classification and regression issues.
• Master the techniques for handling large datasets and interpreting multiple decision trees within Random Forest.
• Evaluate and optimize machine learning model performance through hyperparameter tuning.

This instructor-led live training in Kenya (online or onsite) targets intermediate-level data analysts who wish to learn how to use RapidMiner for estimating and projecting values and utilizing analytical tools for time series forecasting.

By the end of this training, participants will be able 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.