Introduction to Pre-trained Models Training Course

This instructor-led, live training in Uzbekistan (available online or on-site) is designed for 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 using AdaBoost.
• Understand the ensemble learning approach and learn how to implement adaptive boosting.
• Learn how to build AdaBoost models to enhance machine learning algorithms in Python.
• Apply hyperparameter tuning to improve the accuracy and performance of AdaBoost models.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at data scientists who wish to use the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in a single platform.

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

• Install and configure Anaconda components and libraries.
• Understand the core concepts, features, and benefits of Anaconda.
• Manage packages, environments, and channels using Anaconda Navigator.
• Use Conda, R, and Python packages for data science and machine learning.
• Get to know some practical use cases and techniques for managing multiple data environments.

This instructor-led, live training in Uzbekistan (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 (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 Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 in Uzbekistan (available online or on-site) is tailored for data scientists and developers who aim to learn and advance their careers in Data Science using Kaggle.

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

• Gain a solid understanding of data science and machine learning.
• Explore key concepts in data analytics.
• Learn about Kaggle and how it operates.

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.

This instructor-led, live training in Uzbekistan (available online or on-site) is tailored for data scientists and developers who wish to utilize Modin to build and implement parallel computations with Pandas, thereby achieving faster data analysis.

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

• Set up the necessary environment to begin developing scalable Pandas workflows with Modin.
• Understand the features, architecture, and advantages of Modin.
• Distinguish between Modin, Dask, and Ray.
• Execute Pandas operations more rapidly using Modin.
• Implement the full Pandas API and its functions.

This instructor-led, live training in Uzbekistan (available online or on-site) is designed for data scientists and software engineers who wish to use Random Forest to develop machine learning algorithms for large-scale datasets.

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

• Set up the necessary development environment to begin building machine learning models using Random Forest.
• Understand the advantages of Random Forest and learn how to apply it to solve classification and regression problems.
• Acquire skills in handling large datasets and interpreting multiple decision trees within the Random Forest framework.
• Evaluate and optimize machine learning model performance by fine-tuning hyperparameters.

This instructor-led, live training in Uzbekistan (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 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.

This instructor-led, live training in Uzbekistan (available online or on-site) is intended for data scientists and developers who wish to leverage RAPIDS to build GPU-accelerated data pipelines, workflows, and visualisations, applying machine learning algorithms such as XGBoost, cuML, and others.

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

• Set up the required development environment to build data models using NVIDIA RAPIDS.
• Understand the key features, components, and advantages of the RAPIDS framework.
• Harness the power of GPUs to accelerate end-to-end data and analytics pipelines.
• Implement GPU-accelerated data preparation and ETL processes using cuDF and Apache Arrow.
• Learn how to carry out machine learning tasks using XGBoost and cuML algorithms.
• Create data visualisations and perform graph analysis using cuXfilter and cuGraph.