Introduction to Pre-trained Models Training Course
Pre-trained models form the foundation of contemporary artificial intelligence, providing ready-made capabilities that can be customized for diverse applications. This course guides participants through the core principles, architecture, and practical scenarios involving pre-trained models. Attendees will discover how to utilize these models for tasks such as text classification, image recognition, and other AI-driven operations.
This instructor-led, live training (available online or onsite) targets beginner-level professionals seeking 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 scratch.
Upon completion of this training, participants will be capable of:
- Understanding the concept and advantages of pre-trained models.
- Examining various pre-trained model architectures and their specific use cases.
- Fine-tuning a pre-trained model for designated tasks.
- Implementing pre-trained models within simple machine learning projects.
Course Format
- Interactive lectures and discussions.
- Ample exercises and practical sessions.
- Hands-on implementation in a live lab environment.
Course Customization Options
- To request customized training for this course, please contact us to arrange.
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 Norway (available online or on-site) is designed for data scientists and software engineers who wish to employ AdaBoost to create boosting algorithms for machine learning using Python.
By the conclusion of this training, participants will be able to:
- Establish the necessary development environment to initiate the creation of machine learning models with AdaBoost.
- Comprehend the ensemble learning approach and how to execute adaptive boosting.
- Learn the process of constructing AdaBoost models to enhance machine learning algorithms in Python.
- Utilize hyperparameter tuning to improve the accuracy and performance of AdaBoost models.
Anaconda Ecosystem for Data Scientists
14 HoursThis instructor-led live training in Norway (online or onsite) is aimed at data scientists who wish to use the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in a single platform.
By the end 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.
- Get to know some practical use cases and techniques for managing multiple data environments.
AutoML with Auto-Keras
14 HoursThis instructor-led live training in Norway (online or on-site) is intended for data scientists and those with less technical expertise who wish to employ 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 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.
AutoML Essentials
14 HoursThis 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.
Creating Custom Chatbots with Google AutoML
14 HoursThis instructor-led, live training in Norway (online or onsite) is designed for participants with varying levels of expertise who wish to utilize Google's AutoML platform to construct customized chatbots for a range of applications.
Upon completion 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 Norway (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 Norway (online or onsite) is designed for 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:
- Import datasets into DataRobot to analyze, evaluate, and perform quality checks.
- 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 optimized prediction performance.
Google Cloud AutoML
7 HoursThis instructor-led, live training in Norway (online or onsite) is designed for data scientists, data analysts, and developers interested in exploring 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 various services for different 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 Norway (online or onsite) is designed for data scientists and developers who wish to develop 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 principles.
- Explore data analytics techniques.
- Understand Kaggle’s features and operational mechanisms.
Machine Learning for Mobile Apps using Google’s ML Kit
14 HoursThis instructor-led, live training (available online or onsite) is intended for developers who wish 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:
- Set up 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 Norway (online or onsite) is aimed at data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.
By the end of this training, participants will be able to:
- Set up the necessary environment to start developing Pandas workflows at scale with Modin.
- Understand the features, architecture, and advantages of Modin.
- Know the differences between Modin, Dask, and Ray.
- Perform Pandas operations faster with Modin.
- Implement the entire Pandas API and functions.
Machine Learning with Random Forest
14 HoursThis instructor-led, live training in Norway (online or onsite) is aimed at 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.
Advanced Analytics with RapidMiner
14 HoursThis instructor-led live training in Norway (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 for Machine Learning and Predictive Analytics
14 HoursRapidMiner is an open-source data science platform designed for rapid application prototyping and development. It features an integrated environment supporting data preparation, machine learning, deep learning, text mining, and predictive analytics.
In this instructor-led live training, participants will learn to utilize RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.
Upon completion of this training, participants will be able to:
- Install and configure RapidMiner
- Prepare and visualize data using RapidMiner
- Validate machine learning models
- Combine data sources and build predictive models
- Implement predictive analytics within business processes
- Troubleshoot and optimize RapidMiner
Audience
- Data scientists
- Engineers
- Developers
Course Format
- A blend 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 Norway (online or onsite) is aimed at 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, cuML, etc.
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.
