Introduction to Machine Learning Training Course

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

Upon completing this training, participants will be able to:

• Configure the necessary development environment to begin building machine learning models with AdaBoost.
• Grasp the concept of ensemble learning and learn how to implement adaptive boosting.
• Acquire the skills to construct AdaBoost models that enhance machine learning algorithms in Python.
• Apply hyperparameter tuning to improve the accuracy and overall performance of AdaBoost models.

This instructor-led live training in Taiwan (online or onsite) is tailored for data scientists and non-technical professionals who wish to utilize Auto-Keras to automate the process of selecting and optimizing a machine learning model.

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

• Automate the training of highly efficient machine learning models.
• Automatically identify the optimal parameters for deep learning models.
• Construct highly accurate machine learning models.
• Leverage machine learning capabilities to address real-world business challenges.

This instructor-led live training (available online or onsite) targets technical professionals with a background in machine learning who aim to optimize models for identifying complex patterns in big data using AutoML frameworks.

This instructor-led, live training in Taiwan (online or onsite) targets participants with varying levels of expertise who aim to utilize Google's AutoML platform to develop customized chatbots for diverse applications.

Upon completing 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 Taiwan (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 Taiwan (online or on-site) is designed for data scientists and data 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:

• Import datasets into DataRobot to analyze, evaluate, and perform quality checks.
• Develop and train models to identify key variables and achieve prediction objectives.
• Interpret models to generate valuable insights that support business decision-making.
• Monitor and manage models to ensure optimal prediction performance.

This instructor-led, live training in Taiwan (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.

This instructor-led, live training in Taiwan (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 using AWS EKS (Elastic Kubernetes Service).
• 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 in Taiwan (online or onsite) is aimed at 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 needed software on AWS.
• Use EKS (Elastic Kubernetes Service) to simplify the work 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 designed for 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 (available online or onsite) is designed for developers who wish to employ Google’s ML Kit to develop 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 commence creating machine learning features for mobile applications.
• Integrate new machine learning technologies into Android and iOS applications utilizing ML Kit APIs.
• Enhance and optimize existing applications using the ML Kit SDK for on-device processing and deployment.

Pattern matching is a technique employed to identify specific patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.

Format of the Course

• This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.

This instructor-led, live training in Taiwan (online or onsite) is designed for data scientists and software engineers who wish to utilize Random Forest for developing machine learning algorithms tailored for large 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 apply it to solve classification and regression problems.
• Learn techniques for handling large datasets and interpreting the multiple decision trees inherent in Random Forest.
• Evaluate and optimize machine learning model performance by fine-tuning hyperparameters.

This instructor-led, live training in Taiwan (online or on-site) 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 an integrated environment encompassing data preparation, machine learning, deep learning, text mining, and predictive analytics.

In 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 with RapidMiner
• Validate machine learning models
• Mashup data and create predictive models
• Operationalize predictive analytics within a business process
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

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