Supervised Learning Training in Tashkent

This instructor-led, live training Tashkent (online or in-person) 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 Tashkent (online or on-site) is designed for intermediate-level professionals who wish to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to improve production efficiency and reduce unexpected equipment failures.

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

• Implement AI models to predict equipment failures in semiconductor manufacturing.
• Analyse maintenance data to identify patterns and trends that indicate potential issues.
• Integrate AI-driven predictive maintenance into existing manufacturing workflows.
• Reduce downtime and maintenance costs through proactive equipment management.

This guided, live training in Tashkent (online or on-site) is designed for 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 fundamental principles of AI and their application in 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 (online or onsite) is designed for technical participants with a background in machine learning who aim to optimise models for detecting complex patterns in big data using AutoML frameworks.

This instructor-led, live training in Tashkent (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 Tashkent (available online or on-site) is tailored for data scientists as well as less technical professionals who wish to leverage Auto-Keras to automate the selection and optimisation 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 optimal parameters for deep learning models.
• Construct highly accurate machine learning models.
• Harness the power of machine learning to address real-world business challenges.

This instructor-led, live training in Tashkent (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 Tashkent (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 analyse, assess, and perform quality checks on data.
• Build and train models to identify key variables and meet prediction targets.
• Interpret models to generate valuable insights that support business decision-making.
• Monitor and manage models to maintain optimised prediction performance.

This instructor-led, live training in Tashkent (online or on-site) is designed for 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 (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 designed for rapid application prototyping and development. It provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led, live training session, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and deploying 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
• Mashup data and build predictive models
• Operationalize predictive analytics within business processes
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Course Format

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

Note

• To request a customized training session for this course, please contact us to make arrangements.