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

Ce cours n'est pas disponible en Français (France)

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Transfer Learning Foundations for AI Models

Ce cours fait partie de Spécialisation "Transfer Learning and Fine-Tuning for AI Models"

Instructeur : Edureka

Inclus avec En savoir plus

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

6 heures à compléter

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Débutant

Expérience recommandée

6 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

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

Outils que vous découvrirez

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

Détails à connaître

Certificat partageable

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Ce cours fait partie de la Spécialisation "Transfer Learning and Fine-Tuning for AI Models"

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

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Il y a 3 modules dans ce cours

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.

Inclus

12 vidéos4 lectures2 devoirs

12 vidéosTotal 65 minutes

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

4 lecturesTotal 40 minutes

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

2 devoirsTotal 21 minutes

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

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.

Inclus

8 vidéos4 lectures2 devoirs

8 vidéosTotal 41 minutes

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

4 lecturesTotal 40 minutes

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

2 devoirsTotal 21 minutes

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

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.

Inclus

4 vidéos2 lectures1 devoir

4 vidéosTotal 16 minutes

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

2 lecturesTotal 40 minutes

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

1 devoirTotal 30 minutes

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

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Instructeur

Edureka

230 Cours203 740 apprenants

Offert par

Edureka

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Foire Aux Questions

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.