Machine Learning Concepts for Entrepreneurs and Managers Training Course

This instructor-led, live training in Serbia (online or onsite) is aimed at 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 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 Serbia (online or onsite) is designed for data scientists and less technical individuals who wish to utilize Auto-Keras to automate the selection and optimization 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 best parameters for deep learning models.
• Build highly accurate machine learning models.
• Use the power of machine learning to solve real-world business problems.

This guided, live training session (available online or on-site) targets technical professionals with a background in machine learning who aim to enhance model performance for identifying complex patterns within big data using AutoML frameworks.

This instructor-led, live training in Serbia (online or onsite) targets 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 Serbia (online or onsite) offers an introduction to the fields of pattern recognition and machine learning. It covers 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 Serbia (online or onsite) is designed for data scientists and 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 Serbia (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 Serbia (online or onsite) is designed for developers and data scientists aiming to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on-premises and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run complete machine learning pipelines across diverse architectures and cloud environments.
• Utilize Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Serbia (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 onsite, is aimed at data scientists looking to extend their skills beyond building ML models by optimizing the processes for creating, tracking, and deploying them.

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 instructor-led, live training session (available online or onsite) is designed for developers aiming to utilize Google’s ML Kit to construct machine learning models optimized for mobile device processing.

Upon completing this training, participants will be able to:

• Establish the necessary development environment to commence the creation of machine learning features for mobile applications.
• Integrate new machine learning capabilities into Android and iOS apps using the ML Kit APIs. 
• Refine and optimize existing applications by leveraging 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 in Serbia (online or onsite) is designed for data scientists and software engineers who wish 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 necessary 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 Serbia (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 supporting data preparation, machine learning, deep learning, text mining, and predictive analytics.

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

Upon completing this training, participants will be able to:

• Install and configure RapidMiner
• Prepare and visualize data using RapidMiner
• Validate machine learning models
• Integrate data sources and create predictive models
• Implement predictive analytics within business processes
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

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

Note

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