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 program explores the application of AI—with a specific focus on Machine Learning and Deep Learning—within the automotive industry. It aims to identify technologies that can be (potentially) utilized across various automotive scenarios, ranging from basic automation and image recognition to autonomous decision-making processes.

Delving into the fundamentals of artificial intelligence uncovers how intelligent technologies are transforming digital strategy, automation, and decision-making processes within enterprise operations. The course explores core concepts such as the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside topics like communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This instructor-led live training in Italy (available online or on-site) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

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

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

An Artificial Neural Network serves as a computational data model utilized in the creation of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one implementation of AI. Deep Learning constitutes a subset of Machine Learning.

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 intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

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.

Kubeflow is an open-source platform engineered to simplify the process of building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led live training (available online or onsite) is designed for professionals at beginner to intermediate levels who aim to establish robust ML workflows using Kubeflow.

After completing this training, participants will acquire the ability to:

• Explore the Kubeflow ecosystem and its fundamental components.
• Develop reproducible workflows utilizing Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Deploy machine learning models efficiently with Kubeflow Serving.

Course Format

• Structured presentations coupled with collaborative discussions.
• Practical labs involving real-world Kubeflow components.
• Hands-on exercises to construct end-to-end ML workflows.

Course Customization Options

• Tailored versions of this training can be configured to match your team’s technology stack and specific project 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.
• 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 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.

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.