Online or onsite, instructor-led live Supervised Learning training courses demonstrate through interactive hands-on practice how to use supervised machine learning techniques to train models, make predictions, and analyze data patterns effectively.
Supervised Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Supervised Learning training can be carried out locally on customer premises in 横浜 or in NobleProg corporate training centers in 横浜.
Supervised Learning is also known as Supervised Machine Learning.
This instructor-led, live training in 横浜 (online or onsite) is aimed at 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:
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 横浜 (online or onsite) is aimed at intermediate-level professionals who wish to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to enhance production efficiency and reduce unexpected equipment failures.
By the end of this training, participants will be able to:
Implement AI models for predicting equipment failures in semiconductor manufacturing.
Analyze maintenance data to identify patterns and trends indicative of potential issues.
Integrate AI-driven predictive maintenance into existing manufacturing workflows.
Reduce downtime and maintenance costs through proactive equipment management.
This instructor-led, live training in 横浜 (online or onsite) is aimed at beginner-level professionals who wish to understand and apply AI technologies within the semiconductor manufacturing industry.
By the end of this training, participants will be able to:
Understand the basic principles of AI and how they apply to semiconductor manufacturing.
Identify areas within semiconductor manufacturing where AI can be effectively implemented.
Utilize AI tools and techniques to enhance production efficiency and quality control.
Implement basic AI models to optimize manufacturing processes.
This instructor-led, live training in 横浜 (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.
This instructor-led, live training in 横浜 (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.
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.
Use the power of machine learning to solve real-world business problems.
This instructor-led, live training in 横浜 (online or onsite) is aimed at technical persons with a background in machine learning who wish to optimize the machine learning models used for detecting complex patterns in big data.
By the end of this training, participants will be able to:
Install and evaluate various open source AutoML tools (H2O AutoML, auto-sklearn, TPOT, TensorFlow, PyTorch, Auto-Keras, TPOT, Auto-WEKA, etc.)
Train high quality machine learning models.
Efficiently solve different types of supervised machine learning problems.
Write just the necessary code to initiate the automated machine learning process.
This instructor-led, live training in 横浜 (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 横浜 (online or onsite) is aimed at data scientists and data analysts who wish 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 横浜 (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 (online or onsite) is aimed at developers who wish to use Google’s ML Kit to build machine learning models that are optimized for processing on mobile devices.
By the end of this training, participants will be able to:
Set up the necessary development environment to start developing machine learning features for mobile apps.
Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs.
Enhance and optimize existing apps using the ML Kit SDK for on-device processing and deployment.
Pattern Matching is a technique used to locate specified 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.
RapidMiner is an open source data science software platform for rapid application prototyping and development. It includes an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.
In this instructor-led, live training, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and predictive model deployment.
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.
