What is the main objective of this course?

The course aims to help you build a strong foundation in machine learning and deep learning while teaching you how to leverage pretrained models and transfer learning for practical AI applications.

Which Python libraries will I learn in this course?

You will work with widely used libraries such as NumPy, Pandas, Matplotlib, and Seaborn for data preparation, analysis, and visualization before building machine learning models.

Will I learn how transfer learning works?

Yes. You will learn when to use transfer learning, how pretrained models are adapted to new tasks, and how to decide between fine-tuning a model or training one from scratch.

Will this course cover transformer models?

Yes. The course introduces the core concepts behind transformer architectures, including self-attention, multi-head attention, encoder-decoder models, and modern transformer families.

What career opportunities can this course support?

This course helps prepare you for roles such as AI Engineer, Machine Learning Engineer, Data Scientist, Computer Vision Engineer, and AI Developer by building essential deep learning foundations.

Will I receive a certificate upon completion?

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

What software do I need for this course?

You will use Python, VS Code, Jupyter Notebook, and commonly used Python libraries. The course guides you through setting up the required development environment.

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

Unlike courses that focus only on machine learning theory, this course combines practical Python programming with neural networks, transfer learning, pretrained models, and transformer fundamentals to prepare you for modern AI development.

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.

Transfer Learning Foundations for AI Models

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Transfer Learning Foundations 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 Anfänger

Empfohlene Erfahrung

6 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 Anfänger

Empfohlene Erfahrung

6 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Explain the fundamental concepts of machine learning, neural networks, transfer learning, and transformer architectures.
Apply Python, NumPy, Pandas, and visualization libraries to prepare data, build, and evaluate machine learning models.
Analyze neural networks, CNNs, and transfer learning methods to select suitable approaches for different AI tasks.
Evaluate pretrained and transformer-based models to choose suitable solutions for real-world AI applications.

Kompetenzen, die Sie erwerben

Kategorie: Data Manipulation
Kategorie: Natural Language Processing
Kategorie: Data Visualization
Kategorie: Computer Vision
Kategorie: Artificial Intelligence and Machine Learning (AI/ML)
Kategorie: Data Analysis
Kategorie: Artificial Neural Networks
Kategorie: Feature Engineering
Kategorie: Predictive Modeling
Kategorie: Artificial Intelligence
Kategorie: Machine Learning Methods
Kategorie: Supervised Learning
Kategorie: Applied Machine Learning
Kategorie: Data Science
Kategorie: Deep Learning
Kategorie: Machine Learning

Werkzeuge, die Sie lernen werden

Kategorie: Pandas (Python Package)
Kategorie: NumPy
Kategorie: Python Programming

Wichtige Details

Zertifikat zur Vorlage

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Kürzlich aktualisiert!

Juli 2026

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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

Transfer learning has transformed modern artificial intelligence by making it possible to build powerful AI solutions without training models from scratch. This course provides a practical introduction to machine learning, neural networks, transfer learning, and transformer architectures while helping you develop hands-on skills using Python and widely used data science libraries.

You will begin by working with NumPy, Pandas, Matplotlib, and Seaborn to prepare, analyze, and visualize data for machine learning. You will then build and evaluate your first machine learning models before exploring how neural networks learn, how CNNs extract features, and how pretrained models can be adapted through transfer learning. The course concludes with transformer fundamentals, including self-attention, multi-head attention, encoder-decoder architectures, and the evolution of modern transformer families. You will also learn how to choose between transfer learning and training from scratch and select the right pretrained model for different AI applications. By the End of This Course, You Will Be Able To: - Apply Python and data science libraries to prepare and analyze machine learning data. - Build, train, and evaluate fundamental machine learning models. - Explain how neural networks and convolutional neural networks learn. - Apply transfer learning techniques to adapt pretrained models. - Select suitable pretrained models for different AI use cases. - Explain self-attention, multi-head attention, and transformer architectures. Designed for aspiring AI engineers, machine learning practitioners, software developers, data professionals, students, and technology enthusiasts, this course provides a practical foundation for understanding and applying transfer learning and pretrained AI models.

This module introduces Python-based machine learning fundamentals, including environment setup, data handling with NumPy and Pandas, data visualisation, model building, and evaluation. Learners gain the practical foundation needed to begin working with machine learning workflows.

Das ist alles enthalten

12 Videos4 Lektüren2 Aufgaben

12 VideosInsgesamt 65 Minuten

Specialization Overview5 Minuten
Course Introduction3 Minuten
Python: Backbone of Machine Learning6 Minuten
Hands-On: Setting Up VS Code4 Minuten
Hands-On: Python Fundamentals for Machine Learning6 Minuten
Preparing Data for Machine Learning6 Minuten
Hands On: NumPy Arrays and Vectorized Operations6 Minuten
Hands-On: Pandas for ML Data Handling5 Minuten
Hands-On: Data Visualization with Matplotlib and Seaborn5 Minuten
Decoding Machine Learning: Core Concepts and Insights6 Minuten
Hands-On: Building Your First Machine Learning Model7 Minuten
Hands-On Evaluation of Machine Learning Models6 Minuten

4 LektürenInsgesamt 40 Minuten

Course Syllabus10 Minuten
Python ML Environment Setup Guide: Conda, pip, and Jupyter Best Practices10 Minuten
NumPy and Pandas Reference Guide for Machine Learning Practitioners10 Minuten
Core Machine Learning Concepts: A Practitioner's Reference10 Minuten

2 AufgabenInsgesamt 21 Minuten

Knowledge Check: Python and Machine Learning Fundamentals15 Minuten
Practice Knowledge Check: Python and Machine Learning Fundamentals6 Minuten

This module covers the basics of deep learning, including neural networks, training pipelines, CNNs, and transfer learning. Learners explore how models learn from data and how pretrained models can be adapted for new AI tasks.

Das ist alles enthalten

8 Videos4 Lektüren2 Aufgaben

8 VideosInsgesamt 41 Minuten

How Neural Networks Learn3 Minuten
Neural Network Training Pipelines3 Minuten
Hands-On: Building a Neural Network using NumPy5 Minuten
Convolutional Neural Networks Architecture Basics4 Minuten
Hands-On: CNN Feature Visualization Lab7 Minuten
Transfer Learning: The Foundation of Modern AI5 Minuten
Hands-On: Transfer vs Training from Scratch7 Minuten
Choosing the Right Transfer Strategy5 Minuten

4 LektürenInsgesamt 40 Minuten

Neural Networks: From Perceptrons to Deep Learning10 Minuten
Deep Learning and Feature Hierarchies Reference10 Minuten
Choosing the Correct Model in Transfer Learning10 Minuten
Transfer Learning Strategies and Use Cases: A Practitioner's Framework10 Minuten

2 AufgabenInsgesamt 21 Minuten

Knowledge Check: Neural Networks and Transfer Learning Principles15 Minuten
Practice Knowledge Check: Neural Networks and Transfer Learning Principles6 Minuten

This module introduces transformer architecture, including self-attention, multi-head attention, and encoder-decoder structures. Learners understand how transformers power modern AI models such as BERT, GPT, and other foundation models.

Das ist alles enthalten

4 Videos2 Lektüren1 Aufgabe

4 VideosInsgesamt 16 Minuten

Self-Attention and Multi-Head Attention Explained4 Minuten
Hands-On: Self-Attention from Scratch6 Minuten
Encoder and Decoder Architecture4 Minuten
Course Summary2 Minuten

2 LektürenInsgesamt 40 Minuten

Modern Transformer Families and Their Evolution10 Minuten
Practice Project: Building an Intelligent Image Classification System 30 Minuten

1 AufgabeInsgesamt 30 Minuten

End Course Knowledge Check: Transfer Learning and Pretrained AI Models Essentials30 Minuten

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

Dozent

Edureka

269 Kurse246.734 Lernende

von

Edureka

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Warum entscheiden sich Menschen für Coursera für ihre Karriere?

Felipe M.

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„Bei einem spannenden neuen Projekt konnte ich die neuen Kenntnisse und Kompetenzen aus den Kursen direkt bei der Arbeit anwenden.“

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Häufig gestellte Fragen

You will gain practical skills in Python for machine learning, data preparation, neural networks, transfer learning, transformer architectures, and model evaluation using popular data science libraries.

The course includes hands-on exercises such as setting up a Python environment, working with NumPy and Pandas, building machine learning models, implementing neural networks from scratch, exploring CNNs, and applying transfer learning techniques.

No. Basic Python programming knowledge is recommended, but the course introduces machine learning, neural networks, and transfer learning concepts from the ground up.

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.