Introduction to Pre-trained Models Training Course
Pre-trained models stand as a foundational element of contemporary artificial intelligence, providing ready-made capabilities that can be tailored for diverse applications. This course provides participants with an introduction to the core principles, structural design, and practical scenarios involving pre-trained models. Attendees will discover how to utilize these models for activities such as text classification, image recognition, and additional tasks.
Delivered as an instructor-led, live training session (available online or onsite), this program is designed for professionals at the beginner level who aim to grasp the concept of pre-trained models and learn how to apply them to resolve real-world issues without constructing models from the ground up.
Upon completion of this training, participants will be capable of:
- Comprehending the concept and advantages of pre-trained models.
- Examining different pre-trained model architectures and their respective use cases.
- Performing fine-tuning of a pre-trained model for specific objectives.
- Integrating pre-trained models into straightforward machine learning projects.
Course Structure
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Practical implementation within a live-lab environment.
Customization Options
- To arrange a customized training session for this course, please reach out to us to organize the 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
- Fundamental understanding of machine learning concepts
- Proficiency with Python programming
- Basic knowledge of data manipulation using libraries such as Pandas
Target Audience
- Data scientists
- AI enthusiasts
Need help picking the right course?
uae@nobleprog.com or +971 4871 6715
Introduction to Pre-trained Models Training Course - Enquiry
Upcoming Courses
Related Courses
AdaBoost Python for Machine Learning
14 HoursThis instructor-led, live training in the UAE (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.
Artificial Intelligence (AI) in Automotive
14 HoursThis course explores the application of AI—focusing on Machine Learning and Deep Learning—within the automotive industry. It guides participants in identifying technologies suitable for various in-vehicle scenarios, ranging from basic automation and image recognition to complex autonomous decision-making.
AlphaFold: AI-Driven Protein Structure Prediction and Interpretation
7 HoursThis instructor-led, live training (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.
By the end of this training, participants will be able to:
- Understand the basic principles of AlphaFold.
- Learn how AlphaFold works.
- Learn how to interpret AlphaFold predictions and results.
Anaconda Ecosystem for Data Scientists
14 HoursThis instructor-led, live training in the UAE (online or onsite) is designed for data scientists aiming to leverage the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in a unified platform.
Upon completion of this training, participants will be equipped to:
- Install and configure Anaconda components and libraries.
- Comprehend the core concepts, features, and advantages of Anaconda.
- Manage packages, environments, and channels using Anaconda Navigator.
- Utilize Conda, R, and Python packages for data science and machine learning applications.
- Explore practical use cases and techniques for managing multiple data environments.
Creating Custom Chatbots with Google AutoML
14 HoursThis instructor-led live training in the UAE (online or onsite) targets participants with varying expertise levels who wish to leverage Google's AutoML platform to build customized chatbots for various applications.
Upon completion of this training, participants will be able to:
- Grasp the fundamental concepts of chatbot development.
- Navigate the Google Cloud Platform and access AutoML.
- Prepare data for training chatbot models.
- Train and assess custom chatbot models using AutoML.
- Deploy and integrate chatbots across various platforms and channels.
- Monitor and optimize chatbot performance over time.
Pattern Recognition
21 HoursThis instructor-led, live training in the UAE (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.
Edge AI with TensorFlow Lite
14 HoursThis instructor-led live training offered in the UAE (online or onsite) caters to intermediate-level developers, data scientists, and AI practitioners aiming to harness TensorFlow Lite for Edge AI applications.
By the conclusion of this training, participants will be able to:
- Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
- Develop and optimize AI models using TensorFlow Lite.
- Deploy TensorFlow Lite models on various edge devices.
- Utilize tools and techniques for model conversion and optimization.
- Implement practical Edge AI applications using TensorFlow Lite.
Google Cloud AutoML
7 HoursThis instructor-led live training in the UAE (online or onsite) is designed for data scientists, data analysts, and developers who aim to explore AutoML products and features to create and deploy custom ML training models with minimal effort.
Upon completing this training, participants will be equipped to:
- Navigate the AutoML product suite to implement appropriate services for diverse data types.
- Prepare and label datasets to generate custom ML models.
- Train and manage models to ensure the production of accurate and fair machine learning outcomes.
- Utilize trained models for predictions that address business goals and requirements.
Kaggle
14 HoursThis instructor-led, live training in the UAE (online or onsite) is designed 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 a solid understanding of data science and machine learning principles.
- Analyze data using advanced analytics techniques.
- Familiarize themselves with the Kaggle platform and its operational mechanisms.
Kubeflow Essentials: Build, Train & Serve with Kubernetes
14 HoursKubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.
Upon completion of this training, attendees will gain the skills to:
- Navigate the Kubeflow ecosystem and core components.
- Build reproducible workflows with Kubeflow Pipelines.
- Run scalable training jobs on Kubernetes.
- Serve machine learning models efficiently using Kubeflow Serving.
Format of the Course
- Guided presentations and collaborative discussions.
- Hands-on labs with real Kubeflow components.
- Practical exercises to build end-to-end ML workflows.
Course Customization Options
- Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.
Kubeflow Fundamentals
28 HoursThis instructor-led, live training in the UAE (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.
- Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
- Run entire machine learning pipelines on diverse architectures and cloud environments.
- Using Kubeflow to spawn and manage Jupyter notebooks.
- Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
Machine Learning for Mobile Apps using Google’s ML Kit
14 HoursThis instructor-led, live training (online or on-site) targets developers who aim to utilize Google’s ML Kit to develop machine learning models optimized for mobile processing.
By the conclusion of this training, participants will be able to:
- Prepare the necessary development environment to start creating machine learning features for mobile apps.
- Integrate advanced machine learning technologies into Android and iOS apps via ML Kit APIs.
- Enhance and optimize existing applications using the ML Kit SDK for efficient on-device processing and deployment.
Machine Learning with Random Forest
14 HoursThis instructor-led, live training in the UAE (online or onsite) is tailored for data scientists and software engineers who wish to employ Random Forest to construct machine learning algorithms for large datasets.
By the end of this training, participants will be able to:
- Set up the required development environment to start creating machine learning models with Random Forest.
- Understand the benefits of Random Forest and its implementation for addressing classification and regression issues.
- Learn techniques for handling large datasets and interpreting the multiple decision trees utilized in Random Forest.
- Evaluate and optimize the performance of machine learning models by adjusting hyperparameters.
Advanced Analytics with RapidMiner
14 HoursThis instructor-led live training in the UAE (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.
GPU Data Science with NVIDIA RAPIDS
14 HoursThis instructor-led, live training in the UAE (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.
Upon completion of this training, participants will be able to:
- Configure the required development environment to build data models using NVIDIA RAPIDS.
- Gain a comprehensive understanding of RAPIDS' features, components, and benefits.
- Utilize GPUs to speed up end-to-end data and analytics pipelines.
- Implement GPU-accelerated data preparation and ETL processes using cuDF and Apache Arrow.
- Master the execution of machine learning tasks using XGBoost and cuML algorithms.
- Create data visualizations and perform graph analysis with cuXfilter and cuGraph.
