Machine Learning with Python – 4 Days Training Course

This instructor-led live training in Bulgaria (online or onsite) is designed for data scientists and software engineers who aim 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 Bulgaria (online or onsite) is designed for data scientists as well as less technical participants 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 instructor-led, live training session (available online or on-site) is designed for technical professionals with a background in machine learning who seek to optimize models for detecting complex patterns in big data utilizing AutoML frameworks.

This instructor-led, live training in Bulgaria (online or onsite) is aimed at 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 Bulgaria (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 Bulgaria (online or onsite) is designed for data scientists and analysts seeking to automate, evaluate, and manage predictive models leveraging DataRobot's machine learning capabilities.

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

• Import datasets into DataRobot to analyze, assess, and perform quality checks on data.
• Develop and train models to pinpoint critical variables and achieve prediction goals.
• Interpret model outputs to generate actionable insights that support business decision-making.
• Monitor and oversee models to ensure optimal prediction performance is maintained.

This instructor-led, live training in Bulgaria (online or on-site) targets 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 Bulgaria (online or onsite) is designed for 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.
• 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 Bulgaria (online or on-site) 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 aiming to optimize the ML model creation, tracking, and deployment processes beyond simply building models.

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

• Install and configure MLflow along with related ML libraries and frameworks.
• Understand the critical importance of trackability, reproducibility, and deployability in ML models.
• Deploy ML models across various public clouds, platforms, or on-premise servers.
• Scale the ML deployment process to support multiple users collaborating on a single project.
• Establish a central registry to experiment with, reproduce, and deploy ML models.

This live, instructor-led training (available online or on-site) targets developers who aim to utilize Google’s ML Kit to construct 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 start creating 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 using 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 for the determination of whether certain defined characteristics exist in a captured image, such as verifying the presence of the correct label on a faulty product on a factory assembly line or checking if a component meets specified dimensions. This process differs from "Pattern Recognition," which identifies general patterns by analyzing larger collections of related samples. In pattern matching, the goal is to define exactly what is being sought and then confirm whether that specific expected pattern is present.

Course Format

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

This instructor-led, live training in Bulgaria (online or onsite) is tailored for data scientists and software engineers who aim to utilize Random Forest for building machine learning algorithms on 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 Bulgaria (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 offers an integrated environment covering 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.

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

• Install and configure RapidMiner
• Prepare and visualize data using RapidMiner
• Validate machine learning models
• Combine data and build predictive models
• Implement 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.