Introduction to Machine Learning 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 (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 in Uzbekistan (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 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 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 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 (online or on-site) is designed for 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 both on-premises and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Build, deploy, and manage ML workflows leveraging Docker containers and Kubernetes.
• Execute complete machine learning pipelines across diverse architectures and cloud environments.
• Utilize Kubeflow to spawn and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Uzbekistan (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 (available online or on-site) is designed for data scientists who wish to move 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.
• Understand the importance of trackability, reproducibility, and deployability in ML models.
• Deploy ML models to various 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 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 employed to identify specific patterns within an image. It helps determine whether certain characteristics are present in a captured image, such as the correct label on a defective product on a factory line or the precise dimensions of a component. Unlike "Pattern Recognition," which identifies general patterns based on large collections of related samples, Pattern Matching specifies what we are looking for and confirms whether the expected pattern is present.

Format of the Course

• This course provides an introduction to the approaches, technologies, and algorithms used in the field of pattern matching as it pertains to Machine Vision.

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 quick 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, participants will learn how to use 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
• Mash up 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 extensive hands-on practice

Note

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