Introduction to Machine Learning Training Course

This instructor-led, live training in Argentina (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, held in Argentina (online or onsite), targets data scientists and non-technical individuals who want 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 highly efficient machine learning models.
• Automatically find the best parameters for deep learning models.
• Construct highly accurate machine learning models.
• Utilize machine learning to solve real-world business problems.

This instructor-led live training (online or onsite) targets technical professionals with a background in machine learning who seek to optimize models for identifying complex patterns in big data through AutoML frameworks.

This instructor-led live training in Argentina (online or onsite) is designed for participants with varying expertise levels who wish to leverage Google's AutoML platform to build customized chatbots for various applications.

By the conclusion 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 Argentina (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 Argentina (online or onsite) targets data scientists and analysts aiming to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

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

• Import datasets into DataRobot to analyze, assess, and validate data quality.
• Construct and train models to pinpoint key variables and achieve prediction goals.
• Interpret models to generate actionable insights that support business decision-making.
• Monitor and manage models to ensure optimal prediction performance.

This instructor-led, live training in Argentina (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 Argentina (online or onsite) targets 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).
• Construct, deploy, and oversee ML workflows leveraging Docker containers and Kubernetes.
• Execute complete machine learning pipelines across various architectures and cloud environments.
• Utilize Kubeflow to create and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Argentina (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, offered online or onsite, is designed for data scientists who wish to advance beyond building ML models and optimize the processes for creating, tracking, and deploying them.

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 (online or onsite) targets developers who intend to use 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 apps.
• Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs.
• Enhance and optimize existing applications by employing 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 Argentina (online or onsite) targets 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 Argentina (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 for rapid application prototyping and development. It includes an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. 

In this instructor-led, live training, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and predictive model deployment. 

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

• Install and configure RapidMiner
• Prepare and visualize data with RapidMiner
• Validate machine learning models
• Mashup data and create predictive models
• Operationalize predictive analytics within a business process
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

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