Machine Learning with Python – 2 Days Training Course

This instructor-led, live training in Portugal (online or onsite) is designed for data scientists and software engineers who want to use AdaBoost to develop boosting algorithms for machine learning in Python.< /p>

By the end of this training, participants will be able to:< /p>

• 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 enhance machine learning algorithms in Python.<
• Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.<

This instructor-led live training in Portugal (online or onsite) targets data scientists and non-technical professionals seeking 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 process for 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 instructor-led, live training (online or onsite) is aimed at technical participants with a background in machine learning who wish to optimize models for detecting complex patterns in big data using AutoML frameworks.

This instructor-led, live training in Portugal (online or onsite) is designed for participants with varying levels of expertise who want to use Google's AutoML platform to build custom 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 optimise chatbot performance over time.

This instructor-led, live training in Portugal (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 Portugal (online or on-site) is aimed at 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 into DataRobot to analyze, assess, and perform quality checks on data.
• Build and train models to identify key variables and achieve prediction targets.
• Interpret models to generate valuable insights that support business decision-making.
• Monitor and manage models to maintain optimal prediction performance.

This instructor-led live training in Portugal (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 range to implement various services for different data types.
• Prepare and label datasets to build custom ML models.
• Train and manage models to deliver accurate and fair machine learning models.
• Make predictions using trained models to address business objectives and needs.

This instructor-led live training in Portugal (online or on-site) is designed for developers and data scientists who aim 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-premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Construct, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run complete machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Portugal (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.
• Use EKS (Elastic Kubernetes Service) to simplify the process 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, targets data scientists who wish to extend their capabilities beyond building ML models by optimizing the ML model creation, tracking, and deployment processes.

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 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 (online or onsite) targets developers who wish to employ Google’s ML Kit to develop machine learning models optimized for mobile processing.

Upon completion of this training, participants will be capable of:

• Configuring the necessary development environment to initiate the creation of machine learning features for mobile apps.
• Integrating new machine learning technologies into Android and iOS apps using ML Kit APIs.
• Enhancing and optimizing existing apps through the ML Kit SDK for on-device processing and deployment.

Pattern matching is a technique employed to identify specific patterns within an image. It allows you to verify the presence of defined characteristics in a captured image, such as confirming the correct label on a faulty product on a factory line or verifying the precise dimensions of a component. Unlike "Pattern Recognition," which identifies general patterns based on broader collections of related samples, pattern matching specifically defines what you are looking for and confirms whether that expected pattern exists.

Course Format

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

This instructor-led, live training in Portugal (online or onsite) is designed for data scientists and software engineers who wish to use 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 Portugal (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilise 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.
• Utilise RapidMiner to estimate and project values, employing 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 covering data preparation, machine learning, deep learning, text mining, and predictive analytics.

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

By the conclusion of this training, participants will be capable of:

• Installing and configuring RapidMiner
• Preparing and visualizing data using RapidMiner
• Validating machine learning models
• Mashing up data and creating predictive models
• Implementing predictive analytics within business processes
• Troubleshooting and optimizing RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

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

Note

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