Machine Learning with Python – 4 Days Training Course

This instructor-led live training in Slovakia (online or onsite) targets data scientists and software engineers who wish to use AdaBoost for building boosting algorithms in machine learning with Python.

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

• Set up the required development environment to start building machine learning models with AdaBoost.
• Understand the ensemble learning approach and how to implement adaptive boosting.
• Learn how to construct AdaBoost models to enhance machine learning algorithms in Python.
• Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.

This instructor-led live training in Slovakia (online or on-site) is designed for data scientists and less technical individuals who wish to utilize Auto-Keras to automate the process of selecting and optimizing machine learning models.

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

• Automate the training of highly efficient machine learning models.
• Automatically search for the best parameters for deep learning models.
• Build highly accurate machine learning models.
• Apply machine learning power to solve real-world business problems.

This guided, live training session (available online or in-person) is designed for technical professionals with machine learning expertise who want to enhance models capable of identifying complex patterns in large datasets using AutoML frameworks.

This instructor-led, live training in Slovakia (online or onsite) is designed for participants with varying levels of experience who want 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 Slovakia (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 Slovakia (online or onsite) is tailored for data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

Upon completing this training, participants will be able to:

• Load datasets into DataRobot to analyze, assess, and perform quality checks on their data.
• Build and train models to identify key variables and achieve prediction goals.
• Interpret models to generate valuable insights that support business decision-making.
• Monitor and manage models to ensure optimized prediction performance.

This instructor-led, live training in Slovakia (online or onsite) targets data scientists, data analysts, and developers who want to explore AutoML products and features to create and deploy custom ML 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 Slovakia (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 Slovakia (online or onsite) is designed for 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 required software on AWS.
• Use EKS (Elastic Kubernetes Service) to simplify the process 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 onsite) is aimed at 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, reproducability 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, offered online or on-site, is designed for developers who want to use Google’s ML Kit to build machine learning models optimized for mobile device processing.

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

• Set up the required development environment to start creating machine learning features for mobile applications.
• Integrate new machine learning technologies into Android and iOS apps via the ML Kit APIs.
• Enhance and optimize existing applications by leveraging the ML Kit SDK for on-device processing and deployment.

Pattern matching is a method employed to identify specific patterns within an image. This technique is useful for verifying the presence of particular attributes in a captured image, such as confirming the correct label on a defective product on a factory line or checking the specified dimensions of a component. Unlike "Pattern Recognition," which identifies general patterns from large sets of related samples, pattern matching focuses on pinpointing exactly what is being sought and confirms whether that specific pattern is present.

Course Format

• This course covers the approaches, technologies, and algorithms utilized in pattern matching within the context of Machine Vision.

This instructor-led, live training session Slovakia (online or onsite) is intended for data scientists and software engineers who aim to use Random Forest to develop machine learning algorithms for large-scale datasets.

By the conclusion of this training, participants will be capable of:

• Establishing the necessary development environment to initiate the creation of machine learning models with Random Forest.
• Comprehending the advantages of Random Forest and implementing it to address classification and regression issues.
• Learning how to process large datasets and interpret the multiple decision trees utilized in Random Forest.
• Evaluating and optimizing the performance of machine learning models by tuning hyperparameters.

This instructor-led, live training in Slovakia (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:

• Apply the CRISP-DM methodology, select appropriate machine learning algorithms, and improve 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 offers an integrated environment encompassing data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led live training, participants will learn 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 using 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

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

Note

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