Introduction to Machine Learning Training Course

This instructor-led live training in Sweden (online or onsite) is designed for data scientists and software engineers who aim to use AdaBoost to develop 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 with AdaBoost.
• Understand the ensemble learning approach and how to implement adaptive boosting.
• Learn how to build AdaBoost models to boost machine learning algorithms in Python.
• Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.

This instructor-led live training in Sweden (online or onsite) is designed for data scientists and non-technical professionals who wish to utilize Auto-Keras to automate the process of selecting and optimizing machine learning models.

Upon completing this training, participants will be capable of:

• Automating the training of highly efficient machine learning models.
• Automatically identifying optimal parameters for deep learning models.
• Developing highly accurate machine learning models.
• Leveraging machine learning capabilities to address real-world business challenges.

This instructor-led, live training session (available online or onsite) targets technical professionals with a background in machine learning who aim to enhance model performance for identifying complex patterns in large datasets using AutoML frameworks.

This instructor-led, live training in Sweden (online or on-site) is designed for participants with varying levels of expertise who wish to leverage Google's AutoML platform to create 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 Sweden (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 Sweden (online or onsite) is designed for data scientists and analysts who want to automate, assess, and manage predictive models using DataRobot’s machine learning capabilities.

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

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

This instructor-led, live training in Sweden (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 Sweden (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 Sweden (online or onsite) is designed for engineers aiming to deploy Machine Learning workloads to an AWS EC2 server.

Upon completing this training, participants will be capable of:

• Installing and configuring Kubernetes, Kubeflow, and other necessary software on AWS.
• Utilizing EKS (Elastic Kubernetes Service) to streamline the initialization of a Kubernetes cluster on AWS.
• Creating and deploying a Kubernetes pipeline to automate and manage ML models in production environments.
• Training and deploying TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leveraging 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 along with related ML libraries and frameworks.
• Appreciate the importance of trackability, reproducibility, 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 (available online or onsite) is intended for developers who want to use Google’s ML Kit to build machine learning models optimized for mobile device processing.

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 method employed to identify specific patterns within an image. This technique helps determine whether defined characteristics exist in a captured image, such as verifying the correct label on a defective product on a factory line or confirming the specified dimensions of a component. It differs from "Pattern Recognition," which identifies general patterns based on larger collections of related samples, because it explicitly defines what is being sought and then confirms whether the expected pattern is present.

Course Format

• This course introduces the approaches, technologies, and algorithms used in pattern matching as applied to Machine Vision.

This instructor-led live training in Sweden (available online or onsite) is tailored for data scientists and software engineers who want to utilize Random Forest to build machine learning algorithms for large datasets.

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

• Set up the required development environment to start building machine learning models with Random forest.
• Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
• Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
• Evaluate and optimize machine learning model performance by tuning the hyperparameters.

This instructor-led live training in Sweden (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 features 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 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 blend of lecture, discussion, exercises, and extensive hands-on practice

Note

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