What is the main objective of this course?

The course helps you learn how to customize pretrained AI models for specific tasks by preparing quality datasets, fine-tuning models, and evaluating their performance.

Which tools and frameworks will I use?

You will work with Python, Hugging Face Transformers, and commonly used machine learning libraries to build practical fine-tuning workflows.

Will I learn parameter-efficient fine-tuning techniques?

Yes. The course introduces LoRA and PEFT, explaining how they reduce training costs while maintaining strong model performance.

How will I evaluate fine-tuned AI models?

You will use evaluation metrics such as Accuracy, F1 Score, BLEU, ROUGE, and Perplexity, along with error analysis techniques to assess model quality.

Will I learn how to improve model performance?

Yes. You will explore hyperparameter tuning, learning rate scheduling, gradient accumulation, and other optimization techniques that improve fine-tuning results.

Is this course suitable for working with large language models?

Yes. The concepts and techniques covered in this course are directly applicable to fine-tuning modern large language models for domain-specific applications.

Will I receive a certificate upon completion?

Yes. After successfully completing all course requirements, you will receive a certificate that you can share with employers and on professional platforms.

How is this course different from a general machine learning course?

Unlike traditional machine learning courses, this course focuses specifically on adapting pretrained AI models using modern fine-tuning techniques, efficient training methods, and production-ready evaluation workflows.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Fine-Tuning Techniques for AI Models

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Fine-Tuning Techniques for AI Models

Dieser Kurs ist Teil von Spezialisierung „Transfer Learning and Fine-Tuning for AI Models“

Dozent: Edureka

Bei Mehr erfahren enthalten

3 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

5 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

3 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

5 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Prepare high-quality datasets for fine-tuning using data cleaning, tokenization, label engineering, and task formulation.
Apply supervised fine-tuning techniques to optimize pretrained AI models for domain-specific tasks and applications.
Analyze model performance using evaluation metrics, error analysis, and validation techniques to improve model quality.
Evaluate fine-tuning strategies, including LoRA and PEFT, to select efficient approaches for different AI use cases.

Kompetenzen, die Sie erwerben

Kategorie: Machine Learning
Kategorie: Natural Language Processing
Kategorie: Deep Learning
Kategorie: Data Cleansing
Kategorie: Supervised Learning
Kategorie: Data Quality
Kategorie: Model Training
Kategorie: Large Language Modeling
Kategorie: Artificial Intelligence

Werkzeuge, die Sie lernen werden

Kategorie: Prompt Engineering
Kategorie: Hugging Face
Kategorie: Generative AI
Kategorie: Python Programming

Wichtige Details

Zertifikat zur Vorlage

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Kürzlich aktualisiert!

Juli 2026

Bewertungen

5 Aufgaben

Unterrichtet in Englisch

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung „Transfer Learning and Fine-Tuning for AI Models“

Wenn Sie sich für diesen Kurs anmelden, werden Sie auch für diese Spezialisierung angemeldet.

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In diesem Kurs gibt es 3 Module

Pretrained AI models provide an excellent starting point, but real-world AI applications often require them to be adapted for specific tasks and domains. In this course, you will learn the practical techniques used to prepare high-quality datasets, fine-tune large language models, optimize training workflows, and evaluate model performance using industry-standard practices.

You will begin by exploring data preparation techniques, including data cleaning, tokenization, dataset splitting, label engineering, and task formulation. Next, you will learn supervised fine-tuning concepts such as training dynamics, learning rate scheduling, gradient accumulation, hyperparameter tuning, and instruction fine-tuning through practical demonstrations. Finally, you will evaluate fine-tuned models using metrics such as Accuracy, F1 Score, BLEU, ROUGE, and Perplexity, perform error analysis, and explore parameter-efficient fine-tuning techniques including LoRA and PEFT. By the End of This Course, You Will Be Able To: - Prepare high-quality datasets for fine-tuning AI models. - Apply supervised fine-tuning techniques to optimize model performance. - Analyze model quality using standard evaluation metrics and error analysis. - Evaluate fine-tuning strategies for different AI applications. Designed for AI engineers, machine learning practitioners, software developers, and data scientists, this course provides the practical skills needed to customize pretrained AI models for real-world applications.

This module introduces the foundations of fine-tuning, including task formulation, dataset cleaning, tokenization, label engineering, training example design, and Hugging Face model selection.

Das ist alles enthalten

9 Videos4 Lektüren2 Aufgaben

9 VideosInsgesamt 50 Minuten

Specialization Overview5 Minuten
Course Introduction3 Minuten
Data Engineering for Fine-Tuning6 Minuten
Hands-on: Dataset Cleaning and Preparation6 Minuten
Hands-on: Text Tokenization for Modern NLP Pipelines7 Minuten
Task Formulation in machine learning6 Minuten
Hands-On: Business Problem to ML Task Formulation5 Minuten
Hands-On: Label Engineering for Fine-Tuning7 Minuten
Hands-On: Hugging Face Model Selection5 Minuten

4 LektürenInsgesamt 40 Minuten

Course Syllabus10 Minuten
Characteristics of High-Quality Fine-Tuning Datasets10 Minuten
Designing Effective Training Examples for Fine-Tuning10 Minuten
Fine-Tuning Readiness Checklist10 Minuten

2 AufgabenInsgesamt 21 Minuten

Knowledge Check: Model Selection, Data Preparation, and Baselines15 Minuten
Practice Knowledge Check: Model Selection, Data Preparation, and Baselines 6 Minuten

This module covers the supervised fine-tuning process, including training dynamics, stability techniques, learning rate scheduling, gradient accumulation, hyperparameter trade-offs, and instruction fine-tuning.

Das ist alles enthalten

5 Videos3 Lektüren2 Aufgaben

5 VideosInsgesamt 29 Minuten

The Supervised Fine-Tuning Pipeline6 Minuten
Hands-on: Learning Rate Schedulers5 Minuten
Hands-On: Gradient Accumulation and Training Stability7 Minuten
Understanding Instruction Fine-Tuning4 Minuten
Hands-On: End-to-End Instruction Fine-Tuning7 Minuten

3 LektürenInsgesamt 30 Minuten

Understanding Training Dynamics in Large Language Models10 Minuten
Hyperparameter Trade-offs in Fine-Tuning10 Minuten
Common Fine-Tuning Failures and Troubleshooting10 Minuten

2 AufgabenInsgesamt 21 Minuten

Knowledge Check: Supervised Fine-Tuning Workflows15 Minuten
Practice Knowledge Check: Supervised Fine-Tuning Workflows6 Minuten

This module focuses on evaluating fine-tuned models, analysing errors, designing reliable evaluation workflows, and applying efficient adaptation techniques such as LoRA and PEFT.

Das ist alles enthalten

4 Videos4 Lektüren1 Aufgabe

4 VideosInsgesamt 18 Minuten

Model Evaluation Metrics: F1, BLEU, ROUGE5 Minuten
Hands-On: BERT Metrics and Error Analysis6 Minuten
Introduction to PEFT and LORA4 Minuten
Course Summary3 Minuten

4 LektürenInsgesamt 60 Minuten

Designing Reliable Evaluation Pipelines for Fine-Tuned Models10 Minuten
Full Fine-Tuning vs PEFT: Choosing an Approach10 Minuten
From Fine-Tuning to Production: What Comes Next?10 Minuten
Practice Project: Build an AI Email Assistant30 Minuten

1 AufgabeInsgesamt 30 Minuten

End CourseKnowledge Check: Fine-Tuning Techniques for AI Models30 Minuten

Erwerben Sie ein Karrierezertifikat.

Fügen Sie dieses Zeugnis Ihrem LinkedIn-Profil, Lebenslauf oder CV hinzu. Teilen Sie sie in Social Media und in Ihrer Leistungsbeurteilung.

Dozent

Edureka

250 Kurse223.818 Lernende

von

Edureka

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Häufig gestellte Fragen

You will learn how to prepare datasets, fine-tune pretrained AI models, optimize training workflows, evaluate model performance, and apply parameter-efficient fine-tuning techniques.

The course includes hands-on exercises covering dataset preparation, tokenization, supervised fine-tuning, hyperparameter tuning, model evaluation, error analysis, and LoRA-based fine-tuning using Hugging Face.

Yes. A basic understanding of Python, machine learning, neural networks, and transfer learning is recommended. Completing the previous course in this specialization is ideal.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.