Machine Learning Concepts for Entrepreneurs and Managers Training Course

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

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

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

This instructor-led live training (available online or in-person) is designed for technical professionals with a background in machine learning who aim to optimize models for identifying complex patterns within big data by leveraging AutoML frameworks.

This instructor-led, live training in Romania (online or onsite) is designed for participants with varying levels of experience who want to utilize Google's AutoML platform to develop customized chatbots for a wide range of applications.

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

• Grasp the core principles of chatbot development.
• Navigate the Google Cloud Platform and access AutoML.
• Prepare data for training chatbot models.
• Train and assess 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 Romania (online or onsite) offers a comprehensive introduction to the fields of pattern recognition and machine learning. It explores practical applications across statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

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

• Apply fundamental statistical methods to pattern recognition tasks.
• Leverage essential models such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to address complex problem-solving scenarios.
• Enhance prediction accuracy by integrating various models.

This instructor-led training, offered online or onsite, is designed for data scientists and analysts who want to automate, evaluate, and manage predictive models using DataRobot’s machine learning capabilities.

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

• Load datasets into DataRobot to analyze, assess, and verify data quality.
• Build and train models to identify critical variables and meet prediction objectives.
• Interpret models to derive valuable insights that aid in business decision-making.
• Monitor and manage models to sustain optimized prediction performance.

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

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

• Install and configure Kubernetes, Kubeflow, and other necessary software on AWS.
• Utilize EKS (Elastic Kubernetes Service) to simplify the process of initializing a Kubernetes cluster on AWS.
• Create and deploy a Kubernetes pipeline to automate and manage 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 (online or onsite) is designed for data scientists who wish to move beyond simply building ML models and instead optimize the processes of model creation, tracking, and deployment.

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, 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 live, instructor-led training (available online or on-site) is designed for developers looking to use Google’s ML Kit to build machine learning models optimized for mobile devices.

Upon completing 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.

This instructor-led live training, offered in Romania (online or onsite), is tailored for data scientists and software engineers who aim to utilize Random Forest to develop machine learning algorithms for large datasets.

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

• Set up the necessary development environment to commence building machine learning models with Random Forest.
• Comprehend the advantages of Random Forest and its implementation for resolving classification and regression issues.
• Learn techniques for handling large datasets and interpreting the multiple decision trees inherent in Random Forest.
• Evaluate and optimize the performance of machine learning models through hyperparameter tuning.

This instructor-led, live training in Romania (online or onsite) is designed for data analysts at an intermediate level who want to learn how to use RapidMiner to estimate and project values, as well as to leverage analytical tools for time series forecasting.

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

• Apply the CRISP-DM methodology, select suitable machine learning algorithms, and improve model construction and performance.
• Use RapidMiner to estimate and project values, as well as 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.

During this instructor-led live training, participants will learn how to utilize RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.

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

• Install and configure RapidMiner
• Prepare and visualize data using RapidMiner
• Validate machine learning models
• Integrate data and create predictive models
• Implement predictive analytics within business processes
• 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 customized training for this course, please contact us to arrange.