Introduction to Machine Learning Training Course

This live, instructor-led training in Italy (offered online or on-site) is designed for data scientists and software engineers who aim to use AdaBoost to develop boosting algorithms for machine learning projects using 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 Italy (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

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 in-person) is designed for technical professionals with a machine learning foundation who aim to enhance model performance for identifying complex patterns within big data through AutoML frameworks.

This instructor-led, live training in Italy (online or onsite) is designed for participants with diverse expertise levels who want to utilize Google's AutoML platform to develop customized chatbots for various applications.

Upon completing this training, participants will be able to:

• Grasp 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 Italy (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.

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

• Apply fundamental statistical methods to pattern recognition.
• Utilize essential models such as neural networks and kernel methods for data analysis.
• Implement advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

This instructor-led, live training in Italy (online or onsite) is designed for data scientists and data analysts who want to automate, evaluate, 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 the data.
• Construct and train models to identify key variables and achieve prediction objectives.
• Interpret models to generate valuable insights that support business decision-making.
• Monitor and manage models to ensure optimal prediction performance.

This instructor-led, live training in Italy (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 Italy (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 Italy (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 targeted at data scientists who wish to advance beyond basic ML model building and optimize the workflows for model creation, tracking, and deployment.

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 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 session (available online or on-site) is intended for developers looking to utilize 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 initiate the creation of machine learning features for mobile applications.
• Integrate new machine learning technologies into Android and iOS apps via ML Kit APIs.
• Enhance and optimize existing apps by 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 in Italy (online or onsite) targets data scientists and software engineers looking to utilize Random Forest for building machine learning algorithms on large datasets.

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

• Configure 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 Italy (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 encompassing 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 completing 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

Course Format

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

Note

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