Introduction to Pre-trained Models Training Course
Pre-trained models represent a fundamental pillar of contemporary artificial intelligence, providing ready-made capabilities that can be tailored for numerous applications. This course introduces participants to the core principles of pre-trained models, their structural design, and practical implementation scenarios. Participants will gain insights into leveraging these models for tasks such as text classification, image recognition, and more.
This instructor-led, live training (available online or on-site) is designed for beginner-level professionals who aim to grasp the concept of pre-trained models and learn how to apply them to solve real-world problems without the need to build models from the ground up.
Upon completion of this training, participants will be able to:
- Comprehend the concept and advantages of pre-trained models.
- Examine various pre-trained model architectures and their specific use cases.
- Fine-tune a pre-trained model for designated tasks.
- Implement pre-trained models in straightforward machine learning projects.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live laboratory environment.
Course Customization Options
- To request customized training for this course, please contact us to arrange details.
Course Outline
Introduction to Pre-trained Models
- What are pre-trained models?
- Benefits of using pre-trained models
- Overview of popular pre-trained models (e.g., BERT, ResNet)
Understanding Pre-trained Model Architectures
- Model architecture basics
- Transfer learning and fine-tuning concepts
- How pre-trained models are built and trained
Setting Up the Environment
- Installing and configuring Python and relevant libraries
- Exploring pre-trained model repositories (e.g., Hugging Face)
- Loading and testing pre-trained models
Hands-On with Pre-trained Models
- Using pre-trained models for text classification
- Applying pre-trained models to image recognition tasks
- Fine-tuning pre-trained models for custom datasets
Deploying Pre-trained Models
- Exporting and saving fine-tuned models
- Integrating models into applications
- Basics of deploying models in production
Challenges and Best Practices
- Understanding model limitations
- Avoiding overfitting during fine-tuning
- Ensuring ethical use of AI models
Future Trends in Pre-trained Models
- Emerging architectures and their applications
- Advances in transfer learning
- Exploring large language models and multimodal models
Summary and Next Steps
Requirements
- Basic understanding of machine learning concepts
- Familiarity with Python programming
- Basic knowledge of data handling using libraries like Pandas
Audience
- Data scientists
- AI enthusiasts
Open Training Courses require 5+ participants.
Introduction to Pre-trained Models Training Course - Booking
Introduction to Pre-trained Models Training Course - Enquiry
Upcoming Courses
Related Courses
AdaBoost Python for Machine Learning
14 HoursThis instructor-led, live training in Czech Republic (online or onsite) is designed for data scientists and software engineers who want to utilize AdaBoost to build boosting algorithms for machine learning using Python.
Upon completion 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.
Anaconda Ecosystem for Data Scientists
14 HoursThis instructor-led, live training Czech Republic (offered online or onsite) is intended for data scientists who want to utilize the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows on a single platform.
By the conclusion of this training, participants will be able to:
- Install and configure Anaconda components and libraries.
- Understand the core concepts, features, and benefits of Anaconda.
- Manage packages, environments, and channels using Anaconda Navigator.
- Use Conda, R, and Python packages for data science and machine learning.
- Learn about practical use cases and techniques for managing multiple data environments.
AutoML with Auto-Keras
14 HoursThis instructor-led, live training in Czech Republic (online or on-site) is designed for data scientists and less technical professionals who wish to utilize Auto-Keras to automate the process of selecting and optimizing a machine learning model.
By the conclusion 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.
- Apply the power of machine learning to solve real-world business problems.
AutoML Essentials
14 HoursThis guided, live training session (available online or on-site) is designed for technical professionals with a foundation in machine learning who aim to enhance models for identifying complex patterns in big data through AutoML frameworks.
Creating Custom Chatbots with Google AutoML
14 HoursThis instructor-led live training in Czech Republic (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:
- 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.
Pattern Recognition
21 HoursThis instructor-led, live training in Czech Republic (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.
DataRobot
7 HoursThis instructor-led live training in Czech Republic (online or onsite) is designed for data scientists and analysts who want 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 in DataRobot to analyze, assess, and quality check data.
- Build and train models to identify important variables and meet prediction targets.
- Interpret models to create valuable insights that are useful in making business decisions.
- Monitor and manage models to maintain an optimized prediction performance.
Google Cloud AutoML
7 HoursThis instructor-led, live training in Czech Republic (online or onsite) is designed for data scientists, data analysts, and developers seeking 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.
Kaggle
14 HoursThis instructor-led live training in Czech Republic (online or onsite) is tailored for data scientists and developers who wish to learn and build their careers in Data Science using Kaggle.
By the end of this training, participants will be able to:
- Gain insights into data science and machine learning concepts.
- Explore the field of data analytics.
- Understand the Kaggle platform and its operational mechanics.
Machine Learning for Mobile Apps using Google’s ML Kit
14 HoursThis instructor-led live training (available online or onsite) is designed for developers who aim to use Google’s ML Kit to build machine learning models optimized for mobile device processing.
Upon completing this training, participants will be able to:
- Configure the necessary development environment to begin developing 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 apps using the ML Kit SDK for on-device processing and deployment.
Accelerating Python Pandas Workflows with Modin
14 HoursThis instructor-led, live training in Czech Republic (online or onsite) is designed for data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.
Upon completing this training, participants will be able to:
- Set up the necessary environment to begin developing Pandas workflows at scale with Modin.
- Grasp the features, architecture, and advantages of Modin.
- Distinguish between Modin, Dask, and Ray.
- Execute Pandas operations more rapidly with Modin.
- Implement the full Pandas API and its functions.
Machine Learning with Random Forest
14 HoursThis instructor-led live training in Czech Republic (online or onsite) is designed for data scientists and software engineers who wish to apply 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.
Advanced Analytics with RapidMiner
14 HoursThis instructor-led live training in Czech Republic (online or onsite) targets intermediate-level data analysts who want to learn how to use RapidMiner for value estimation and projection, and to utilize analytical tools for time series forecasting.
By the end of this training, participants will be able 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 for Machine Learning and Predictive Analytics
14 HoursRapidMiner is an open-source data science software platform designed for rapid application prototyping and development. It features 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 utilize RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.
By the end 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 create predictive models
- Implement predictive analytics within business processes
- Troubleshoot and optimize RapidMiner
Audience
- Data scientists
- Engineers
- Developers
Format of the Course
- A mix of lectures, discussions, exercises, and extensive hands-on practice
Note
- To request customized training for this course, please contact us to make arrangements.
GPU Data Science with NVIDIA RAPIDS
14 HoursThis instructor-led live training in Czech Republic (online or onsite) is designed for data scientists and developers who wish to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms such as XGBoost and cuML.
By the end of this training, participants will be able to:
- Set up the necessary development environment to build data models with NVIDIA RAPIDS.
- Understand the features, components, and advantages of RAPIDS.
- Leverage GPUs to accelerate end-to-end data and analytics pipelines.
- Implement GPU-accelerated data preparation and ETL with cuDF and Apache Arrow.
- Learn how to perform machine learning tasks with XGBoost and cuML algorithms.
- Build data visualizations and execute graph analysis with cuXfilter and cuGraph.
