Introduction to Machine Learning Training Course

This instructor-led, live training in the UAE (online or onsite) is aimed at data scientists and software engineers who wish to use AdaBoost to build boosting algorithms for machine learning with 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 instructor-led live training in the UAE (available online or onsite) targets data scientists and non-technical professionals interested in using Auto-Keras to automate the selection and optimization of machine learning models.

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

• Automate the process of training highly efficient machine learning models.
• Automatically search for the best parameters for deep learning models.
• Build highly accurate machine learning models.
• Utilize the power of machine learning to solve real-world business problems.

This instructor-led live training, available online or onsite, is designed for technical professionals with a background in machine learning who wish to optimize models for detecting complex patterns in big data using AutoML frameworks.

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

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

• Grasp the fundamental concepts of chatbot development.
• Navigate the Google Cloud Platform and access AutoML.
• Prepare data for training chatbot models.
• Train and assess custom chatbot models using AutoML.
• Deploy and integrate chatbots across various platforms and channels.
• Monitor and optimize chatbot performance over time.

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

Upon completing this training, participants will be able to:

• Load datasets into DataRobot to analyze, assess, and ensure data quality.
• Construct and train models to identify key variables and achieve prediction goals.
• Interpret models to derive valuable insights that support business decision-making.
• Monitor and manage models to ensure optimal prediction performance.

This instructor-led live training in the UAE (online or onsite) is designed for data scientists, data analysts, and developers who aim to explore AutoML products and features to create and deploy custom ML training models with minimal effort.

Upon completing this training, participants will be equipped to:

• Navigate the AutoML product suite to implement appropriate services for diverse data types.
• Prepare and label datasets to generate custom ML models.
• Train and manage models to ensure the production of accurate and fair machine learning outcomes.
• Utilize trained models for predictions that address business goals and requirements.

This instructor-led live training in the UAE (online or onsite) is designed for developers and data scientists who aim to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on-premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Construct, 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 in the UAE (online or onsite) is designed for engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.

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

• Install and configure Kubernetes, Kubeflow, and other necessary software on AWS.
• Use EKS (Elastic Kubernetes Service) to simplify the process of initializing a Kubernetes cluster on AWS.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led live training (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.

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

• Install and configure MLflow and related ML libraries and frameworks.
• Appreciate the importance of trackability, reproducibility, and deployability of an ML model.
• Deploy ML models to different public clouds, platforms, or on-premise servers.
• Scale the ML deployment process to accommodate multiple users collaborating on a project.
• Set up a central registry to experiment with, reproduce, and deploy ML models.

This instructor-led, live training (online or on-site) targets developers who aim to utilize Google’s ML Kit to develop machine learning models optimized for mobile processing.

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

• Prepare the necessary development environment to start creating machine learning features for mobile apps.
• Integrate advanced machine learning technologies into Android and iOS apps via ML Kit APIs.
• Enhance and optimize existing applications using the ML Kit SDK for efficient on-device processing and deployment.

Pattern Matching is a methodology employed to identify specific patterns within an image. This technique allows for the verification of particular characteristics in captured images, such as confirming the presence of an expected label on a defective item on a production line or verifying the precise dimensions of a component. It differs from "Pattern Recognition," which identifies broad patterns derived from large sets of related samples, by strictly defining what is being sought and reporting whether that specific pattern exists.

Course Format

• This course explores the approaches, technologies, and algorithms utilized in pattern matching within the context of Machine Vision.

This instructor-led, live training in the UAE (online or onsite) is tailored for data scientists and software engineers who wish to employ Random Forest to construct machine learning algorithms for large datasets.

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

• Set up the required development environment to start creating machine learning models with Random Forest.
• Understand the benefits of Random Forest and its implementation for addressing classification and regression issues.
• Learn techniques for handling large datasets and interpreting the multiple decision trees utilized in Random Forest.
• Evaluate and optimize the performance of machine learning models by adjusting hyperparameters.

This instructor-led live training in the UAE (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.

RapidMiner is an open-source data science software platform designed for rapid application prototyping and development. It provides a comprehensive, integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

During this instructor-led live training, participants will learn how to utilize RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.

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

• Install and configure RapidMiner
• Prepare and visualize data using RapidMiner
• Validate machine learning models
• Combine data and create predictive models
• Implement predictive analytics within a business process
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• To request customized training for this course, please contact us to arrange.