What careers can I pursue after learning deep learning basics?

This course helps you prepare for entry-level and intermediate roles such as AI Engineer, Machine Learning Engineer, Data Scientist, and AI Product positions. It also lays solid groundwork before diving into niche topics such as Generative AI.

Does this course cover Generative AI?

This course details the deep learning mechanisms that underlie generativeAI and introduces the underlying concepts such as neural networks and sequence learning. It is not specifically about GenAI tools, but rather a great foundation for anyone who wants to take on a Generative AI course.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, 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.

Become AI-Ready: Deep Learning Fundamentals

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

Nous sommes actuellement en train de le traduire dans plus de langues.

Become AI-Ready: Deep Learning Fundamentals

Instructeurs : LearnKartS

Inclus avec

3 modules

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

niveau Débutant

Expérience recommandée

8 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

8 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Build Neural Network Foundations to unlock AI,ML,and Generative AI career opportunities
Master Forward Pass & Backpropagationto debug, train, and optimizereal-world AI models
Decide ML vs Deep Learning Strategically to design scalable, production-ready AI systems
Understand RNNs, LSTMs & Sequence Models behind chatbots, forecasting, and GenAI apps

Compétences que vous acquerrez

Catégorie : Machine Learning
Catégorie : Recurrent Neural Networks (RNNs)
Catégorie : Model Optimization
Catégorie : Artificial Neural Networks
Catégorie : Deep Learning
Catégorie : Artificial Intelligence and Machine Learning (AI/ML)
Catégorie : Model Training
Catégorie : AI literacy

Outils que vous découvrirez

Catégorie : Generative AI
Catégorie : Tensorflow

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Récemment mis à jour !

février 2026

Évaluations

15 devoirs

Enseigné en Anglais

Découvrez comment les employés des entreprises prestigieuses maîtrisent des compétences recherchées

En savoir plus sur Coursera pour les affaires

logos de Petrobras, TATA, Danone, Capgemini, P&G et L'Oreal

Il y a 3 modules dans ce cours

Want to learn how AI systems work behind tools such as ChatGPT and develop a solid foundation for careers in AI & Generative AI?

Many professionals study AI theory but struggle to apply it to real-world systems. This course fills that gap by introducing you to how deep learning models are built, trained, and ultimately fail and get optimized in practice. Why This Course Works? 1) Real-World AI Focus – Learn where deep learning is used across GenAI, healthcare, and finance. 2) Industry Evolution Clarity – From Neural Networks → ImageNet → Generative AI shift. 3) Hands-On Understanding – Visual training using TensorFlow Playground and simulators. What You’ll Be Able To Do? -Build neural network foundations -Understand forward pass and backpropagation -Identify when to use ML vs Deep Learning -Understand RNN, LSTM, and sequence modeling This course is intended for software engineers, application developers, data analysts, professionals transitioning into AI & deep learning, as well as ML beginners who want to build job-ready AI skills. Build strong AI foundations, prepare for advanced deep learning and Generative AI roles. Enroll and start building future-ready AI skills today.

This module builds the foundation of modern AI by explaining how deep learning evolved and why it powers today’s intelligent systems. You’ll understand how AI models learn from data and how neural networks were inspired by the human brain.

Inclus

16 vidéos5 lectures7 devoirs

16 vidéosTotal 62 minutes

Why Deep Learning Is Powering Today’s AI Revolution1 minute
What You’ll Be Able to Do After This Deep Learning Course2 minutes
How Deep Learning Powers ChatGPT, Heathcare, and Finance Domain4 minutes
Why Data Type Matters More Than Algorithm in AI4 minutes
Learning Journey of Deep Learning till Gen AI3 minutes
AI vs Machine Learning vs Deep Learning — Finally Explained Clearly4 minutes
Types of Artifical Intelligence3 minutes
History of Deep Learning4 minutes
Imagenet Challenge - The Competition That Changed AI Forever5 minutes
Transfer learning3 minutes
Second Wave - The Moment Deep Learning Became Mainstream5 minutes
When to Use Machine Learning vs Deep Learning6 minutes
How AI Models Actually Learn From Data6 minutes
How the Human Brain Inspired Neural Networks4 minutes
How Activation Functions and Layers Power Neural Networks4 minutes
Artificial Neural Networks5 minutes

5 lecturesTotal 45 minutes

Real-World Deep Learning Applications - How a Hospital Reduced Cancer Diagnosis Errors Using Deep Learning5 minutes
From Neural Networks to Generative AI: The Industry Shift10 minutes
Why Deep Learning Failed in the 1990s and Returned Stronger10 minutes
From ImageNet to Generative AI: Why Deep Learning Took Off10 minutes
How Biological Neurons Inspired Artificial Neural Networks10 minutes

7 devoirsTotal 112 minutes

Deep Learning Foundations and Real-World Applications8 minutes
Foundations of AI, Machine Learning, and Deep Learning16 minutes
Evolution of Deep Learning - The Breakthrough Moments That Made Deep Learning Powerful16 minutes
How Machines Learn: From Brain Logic to Neural Networks12 minutes
How AI, Machine Learning, and Deep Learning Actually Connect25 minutes
Evolution of Deep Learning - The Breakthrough Moments That Made Deep Learning Powerful20 minutes
How Machines Learn: From Brain Logic to Neural Networks15 minutes

This module takes you inside neural networks to show how deep learning models actually learn using forward pass, parameters, and backpropagation. You’ll visually explore how models improve through training, errors, and optimization.

Inclus

14 vidéos2 lectures4 devoirs

14 vidéosTotal 73 minutes

Intro to Tensorflow Playground - Train Your First Neural Network Visually6 minutes
Tensorflow playground Intro, Epochs7 minutes
Tensorflow Playground - Watch a Neural Network Learn in Real Time4 minutes
Tensorflow playground learning - Simple, medium complex cases6 minutes
When Neural Networks Fail — And How to Fix Them4 minutes
Parameters in Deep Learning : Tensorflow Playground1 minute
Forward pass - What Happens When Data Enters a Neural Network5 minutes
Learning - Neural networks7 minutes
Back Propagation - How Neural Networks Learn From Mistakes6 minutes
Backpropagation - Gradient descent7 minutes
Backpropagation Step-by-Step With Real Numbers5 minutes
Forward Pass + Backprop in Action using Network Simulator6 minutes
Forward Pass Calculations + Loss Computation in Action using Network Simulator5 minutes
Backpropagation + Weight Updates in Action using Network Simulator6 minutes

2 lecturesTotal 20 minutes

Why Neural Networks Fail on Complex Patterns10 minutes
How Data Flows Through Deep Neural Networks10 minutes

4 devoirsTotal 72 minutes

Train a Neural Network Visually Using TensorFlow Playground16 minutes
Forward Pass and Backpropagation — The Core of Deep Learning Training16 minutes
Train a Neural Network Visually Using TensorFlow Playground20 minutes
Forward Pass and Backpropagation — The Core of Deep Learning Training20 minutes

This module introduces sequence intelligence and explains how AI systems handle time-based and language data using memory models. You’ll learn how RNNs and LSTMs enable AI to understand context, sequences, and long-term dependencies.

Inclus

10 vidéos4 lectures4 devoirs

10 vidéosTotal 42 minutes

RNN - Why Deep Learning Needed Memory Models1 minute
Sequence Modeling - How AI Understands Language, Speech, and Time Data5 minutes
Language Modeling and Need for RNN6 minutes
Recurrent Neural Networks (RNN) Concept and Network3 minutes
Recurrent Neural Networks (RNN) Flow and Working Mechanism5 minutes
Challenges in RNN5 minutes
How LSTM Solves the Memory Problem in Deep Learning4 minutes
Understanding LSTM Gate Operations and Information Flow5 minutes
LSTM Gradient Flow and Backpropagation Through Time3 minutes
Course Summary3 minutes

4 lecturesTotal 32 minutes

Why Traditional Neural Networks Fail on Time-Based Data10 minutes
How RNNs Remember Previous Information10 minutes
Vanishing Gradient Problem Explained Without Math10 minutes
Course Completion2 minutes

4 devoirsTotal 45 minutes

Why Deep Learning Needs Memory: Introduction to Sequence Modeling8 minutes
Recurrent Neural Networks Explained — And Why They Struggle12 minutes
Why Deep Learning Needs Memory: Introduction to Sequence Modeling10 minutes
Recurrent Neural Networks Explained — And Why They Struggle15 minutes

Instructeurs

LearnKartS

160 Cours201 415 apprenants

Offert par

LearnKartS

En savoir plus sur Machine Learning

Statut : Essai gratuit
Simplilearn
AI & Deep Learning Concepts and Applications
Cours
Statut : Essai gratuit
Packt
Deep Learning, NLP, and AI Applications
Cours
Statut : Essai gratuit
Whizlabs
NVIDIA: Fundamentals of Deep Learning
Cours
Statut : Prévisualisation
Whizlabs
Fundamentals of Deep Learning
Cours

Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?

Felipe M.

Étudiant(e) depuis 2018

’Pouvoir suivre des cours à mon rythme à été une expérience extraordinaire. Je peux apprendre chaque fois que mon emploi du temps me le permet et en fonction de mon humeur.’

Jennifer J.

Étudiant(e) depuis 2020

’J'ai directement appliqué les concepts et les compétences que j'ai appris de mes cours à un nouveau projet passionnant au travail.’

Larry W.

Étudiant(e) depuis 2021

’Lorsque j'ai besoin de cours sur des sujets que mon université ne propose pas, Coursera est l'un des meilleurs endroits où se rendre.’

Chaitanya A.

’Apprendre, ce n'est pas seulement s'améliorer dans son travail : c'est bien plus que cela. Coursera me permet d'apprendre sans limites.’

Ouvrez de nouvelles portes avec Coursera Plus

Accès illimité à 10,000+ cours de niveau international, projets pratiques et programmes de certification prêts à l'emploi - tous inclus dans votre abonnement.

Faites progresser votre carrière avec un diplôme en ligne

Obtenez un diplôme auprès d’universités de renommée mondiale - 100 % en ligne

Découvrir les diplômes

Rejoignez plus de 3 400 entreprises mondiales qui ont choisi Coursera pour les affaires

Améliorez les compétences de vos employés pour exceller dans l’économie numérique

Foire Aux Questions

Deep learning fundamentals describe the core concepts that underlie neural networks, how models are trained based on data usinglayers, weights, biases, and activation functions.These ideas underpin contemporary AI systems, such as image recognition,speech processing, and smart decision-making.

Yes. This course is intended for beginners and professionals new to AI. It breaks deep learning down using intuitive explanations and easy-to-understand examples, bridging the gap between theory and practice – no AI expertise required.

Machine learning is a large field dedicated to the study of learning from data, whereas deep learning is one of its subfields that employs multi-layer neural networks. Deep learning powers AI superpowers like vision, language understanding, and pattern recognition.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.