10 Best Deep Learning Courses with Hands-On Projects: Learn AI Through Real-World Projects in 2026 New

Watching hours of deep learning tutorials feels productive until you try to build something from scratch and realize you can’t. That’s the trap of passive learning. 

Project-based courses flip the script: you write real code, debug real errors, and walk away with something you actually built. That struggle is where the learning sticks.

So what separates a course worth your money from one that’s just another video library? 

A few things: hands-on coding from day one, projects that mirror real-world problems (not toy datasets), active instructor support or community, and content that stays current with tools like PyTorch and TensorFlow rather than teaching outdated syntax.

This guide is for anyone ready to move past theory – beginners who want a structured path into AI, developers pivoting into machine learning roles, and self-taught learners tired of tutorial hell. 

If you can write basic Python and want to actually build neural networks instead of just watching someone else do it, you’re in the right place.

To pick these courses, we looked at curriculum depth, project quality, instructor expertise, student outcomes, and value for money, not just star ratings. Let’s get into it.

How We Selected These Courses

Most “best courses” lists just reshuffle Udemy bestseller rankings. We didn’t do that. Each course on this list was evaluated against nine specific criteria, and courses that failed even one or two got cut regardless of enrollment numbers or reviews.

Project quality mattered most. We rejected courses where “projects” meant filling in three lines of pre-written code. A real project means you architect the model, choose hyperparameters, and debug your own mistakes.

Industry relevance filtered out courses teaching techniques nobody uses in production anymore, like a heavy focus on plain feedforward networks without touching transformers or CNNs.

Framework coverage was non-negotiable. If a course still centers entirely on Keras with no PyTorch exposure, it’s teaching yesterday’s job market.

Beginner friendliness was judged by whether math prerequisites were actually explained, not just assumed.

Instructor reputation came from checking actual publication history, industry experience, or production ML work, not just subscriber counts.

Community support meant checking if forums or Discord servers had active recent activity, not a graveyard of unanswered 2021 posts.

Certificate value was tested by seeing whether recruiters and LinkedIn actually recognize the credential, or if it’s just a PDF for your desktop folder.

Regular updates ruled out courses last touched before transformer architectures became mainstream.

Career applicability meant checking if graduates land interviews citing these specific skills, not vague “success story” testimonials.

Quick Comparison Table – Best Deep Learning Courses with Hands-On Projects

Best Deep Learning Courses with Hands-On Projects

Deep Learning Specialization (DeepLearning.AI)

If you want the course that basically defines how the internet teaches deep learning, this is it. 

Created by Andrew Ng, alongside Kian Katanforoosh and Younes Bensouda Mourri, the Deep Learning Specialization is a foundational program built to help you understand the capabilities, challenges, and consequences of deep learning while preparing you to work on real AI systems. 

It’s a five-course series, and as of its most recent update, every assignment and autograder across Courses 1, 2, 4, and 5 has been refactored for TensorFlow 2, with newer additions like MobileNet, U-Net, and Transformer architectures folded into the curriculum.

Skills you’ll learn

You’ll go from building your first neural network to architecting Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers, plus techniques like Dropout, BatchNorm, and Xavier/He initialization.

Hands-on projects

Course 4 has you building CNNs (including residual networks), applying them to visual detection and recognition tasks, and running neural style transfer to generate art. 

Course 5 covers building RNNs and their variants like GRUs and LSTMs, applying them to character-level language modeling, and using HuggingFace tokenizers and transformer models for tasks like Named Entity Recognition and Question Answering.

Tools covered

TensorFlow and Hugging Face, taught through practical, applied coding rather than pure theory.

Time commitment

Roughly 3 months at 10 hours a week, spread across five courses (25–37 hours each individually).

Best for

Early-career software engineers or technical professionals who want to master fundamental deep learning concepts and gain practical, applicable skills. You’ll need basic Python and preferably some familiarity with linear algebra going in.

IBM Deep Learning with PyTorch, Keras and TensorFlow

If the DeepLearning.AI Specialization is the academic route, this IBM Professional Certificate is the framework-agnostic, job-market-focused alternative. 

It’s a five-course series built around one core idea: employers don’t care which framework you know theoretically, and they want you fluent in all three major ones. 

The program takes learners through Keras, PyTorch, and TensorFlow in sequence, ending in a capstone project you can actually put in a portfolio.

Skills you’ll learn

You’ll start with foundational neural network concepts and Keras basics, then move into building custom layers, integrating Keras with TensorFlow 2.x, and developing transformer models for sequential and time-series data. 

On the PyTorch side, you’ll work with tensors, autograd, and DataLoader, building up from linear regression to full CNN architectures with dropout, weight initialization, and batch normalization.

Hands-on projects

This program leans hard into portfolio-building. You’ll construct deep learning models for regression and classification, build advanced CNNs and vision transformers, and in the final capstone, design a complete deep learning pipeline – data loading, augmentation, model validation – applied to real image classification problems, including geospatial land classification.

Tools covered

PyTorch, Keras, and TensorFlow, plus exposure to autoencoders and GANs.

Time commitment

Roughly 2 months at 10 hours a week, self-paced, included with Coursera Plus.

Best for

Professionals already working in adjacent roles, data scientists, software engineers, ML engineers, Python developers, who want to formally transition into an AI or deep learning engineering role and need multi-framework fluency to compete for those jobs.

PyTorch for Deep Learning

This is a three-course DeepLearning.AI series taught by Laurence Moroney, built entirely around the framework most research labs and production AI teams actually use. 

Where the IBM certificate spreads across three frameworks, this one goes deep on a single tool, taking you from tensors and training loops all the way to deployment-ready model optimization.

Skills you’ll learn

You’ll master PyTorch’s core building blocks, tensors, autograd, and training pipelines, then move into transfer learning and fine-tuning for computer vision and NLP tasks. 

The final course pushes into advanced territory: Siamese networks, ResNet, DenseNet, Transformers, and diffusion models, alongside model compression techniques like pruning and quantization.

Hands-on projects

You’ll build neural networks from scratch and construct a complete training pipeline in Course 1. 

Course 2 has you working with TorchVision and Hugging Face to process image and text data, optimizing models through hyperparameter tuning. 

By Course 3, you’re tackling portfolio-grade projects like text classification and image segmentation, then preparing trained models for real deployment using ONNX and MLflow.

Tools covered

PyTorch, TorchVision, Hugging Face, ONNX, and MLflow. A genuinely production-oriented toolkit rather than just classroom libraries.

Time commitment

About 2 months at 10 hours a week across three courses (24, 33, and 31 hours respectively).

Best for

Learners who already know they want to specialize in PyTorch specifically, whether that’s because their target job listings demand it or because they want deployment skills (not just model-building) baked into their learning path. Intermediate-level background recommended.

Deep Learning with Python: CNN, ANN & RNN (EDUCBA)

This EDUCBA specialization is built for one specific purpose: getting beginners comfortable with the three foundational neural network architectures fast. 

It’s a compact, three-course series that trades the depth of programs like DeepLearning.AI’s Specialization for speed and directness, walking through ANNs, CNNs, and RNNs using bite-sized, project-anchored courses.

Skills you’ll learn

You’ll design, build, and evaluate CNN, ANN, and RNN models in Python using TensorFlow and Keras, along with preprocessing, feature engineering, and optimization techniques applied to real datasets rather than toy examples.

Hands-on projects

Each course centers on one concrete business or financial problem. 

Course 1 has you preprocessing and augmenting image datasets to build and evaluate a CNN for image classification. 

Course 2 shifts to structured data, building and optimizing an ANN for customer churn prediction, including handling imbalanced datasets. 

Course 3 closes with an RNN using LSTM layers for stock price prediction, covering feature scaling, exploratory data analysis, and prediction visualization on real market data.

Tools covered

Python, TensorFlow, and Keras.

Time commitment

Listed as roughly 4 weeks at 10 hours a week, though Coursera’s FAQ also cites a lighter 5 – 6 weeks at 3 – 4 hours weekly, so pace is genuinely flexible depending on how deep you go into each project.

Best for

True beginners who want a fast, structured introduction to the three core neural network types without wading through a long, multi-month specialization first. 

It’s a solid on-ramp before tackling something denser like the DeepLearning.AI or IBM programs.

Learning Deep Learning (Pearson)

This one stands out for its scope: it’s the only course on this list that takes you all the way from basic perceptrons to large language models and multimodal AI in a single, tight three-course arc. 

Built by Pearson and taught alongside author Magnus Ekman (based on the “Learning Deep Learning” book), this specialization moves fast, roughly 19 hours total, but doesn’t skip the modern stuff most beginner courses leave out.

Skills you’ll learn

You’ll cover neural network fundamentals and training algorithms first, then move into CNNs and RNNs with LSTMs, encoder-decoder architectures, and Transformers. 

The final course goes further than most beginner-friendly programs by tackling large language models, multimodal networks that combine text and images, and multitask learning, plus the ethical considerations of deploying these models in the real world.

Hands-on projects

Guided by real programming examples in both TensorFlow and PyTorch, you’ll build and evaluate neural networks, apply CNNs to advanced image classification, use RNNs for time-series forecasting and text autocompletion, develop neural language models with word embeddings, and design Transformer-based systems for machine translation. 

Course 3 pushes into object detection, segmentation, and building multimodal networks that integrate multiple data types.

Tools covered

TensorFlow, PyTorch, and hands-on prompt engineering as part of the LLM-focused final unit.

Time commitment

About 4 weeks at 5 hours a week. Noticeably lighter than most specializations on this list, making it a good fit if you want breadth without a multi-month commitment.

Best for

Developers, data scientists, and analysts with some technical background who want one course that connects classic deep learning fundamentals directly to today’s LLM and multimodal AI landscape, rather than treating those as a separate, later topic.

A Deep Understanding of Deep Learning (with Python Intro)

Created by Mike X. Cohen, a neuroscientist and computational science instructor, this Udemy course takes a completely different approach from the university-backed specializations above. 

It’s not built to check certification boxes; it’s built to make you actually understand why deep learning works, not just how to copy-paste working code. 

The course is explicit about who it isn’t for: if you want a quick overview with a handful of solved examples, look elsewhere. 

This is for people who want to understand metaparameter selection, model evaluation, and how to adapt existing architectures to new problems.

Skills you’ll learn

You’ll build a genuine mathematical and conceptual foundation – gradient descent, calculus as it applies to neural networks, and the architectural differences between feedforward, convolutional, and recurrent networks. 

Cohen calls this an “experimental scientific approach,” meaning you’re encouraged to test and probe models rather than just accept how they behave.

Hands-on projects

The course includes an 8+ hour Python tutorial built in, so true beginners aren’t locked out. From there, you’re building artificial neural networks and full models in PyTorch, working through practice problems, and using GPUs to speed up training.

Tools covered: Python, PyTorch, NumPy.

Time commitment

Self-paced, with substantial total video content (approx 58+ hours); no fixed weekly schedule since it’s a single lifetime-access purchase rather than a subscription model.

Best for

Learners who’ve already dabbled in deep learning tutorials and felt like something was missing. It suits people willing to slow down and treat deep learning as applied math rather than a copy-paste skill, and who want an active Q&A community for troubleshooting along the way.

Deep Learning A-Z 2026: Neural Networks, AI, AWS & LLM Prize

This is a six-part curriculum from Kirill Eremenko, Hadelin de Ponteves, and the SuperDataScience Team that covers nearly every major deep learning architecture in one package, from classic ANNs all the way to Boltzmann Machines and AutoEncoders. 

It’s built around a clear philosophy: pair intuition-first explanations with immediate hands-on implementation, so you understand the “why” before touching code.

Skills you’ll learn

The course walks through Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Self-Organizing Maps, Boltzmann Machines, and AutoEncoders – each introduced with an “intuition” module before the implementation module. 

That structure means you’re not just memorizing syntax; you’re learning why each architecture exists and what problem it was built to solve.

Hands-on projects

This is where the course earns its reputation. You’ll build image and text classifiers (including a cats-vs-dogs image classifier you can test on your own photos), work with recurrent networks that use long-term memory via LSTMs for tasks like stock price prediction, build a Self-Organizing Map, construct a Boltzmann Machine in PyTorch, and finish by building two full recommender systems, one for e-commerce and one for movie recommendations, deployed with AWS integration across several modules.

Tools covered: Python, TensorFlow, PyTorch, and AWS for select deployment-focused sections.

Time commitment

Around 22 – 23 hours of on-demand video, self-paced with lifetime access, so no fixed weekly schedule, though the six-part structure lends itself well to working through it in stages.

Best for

Beginners and intermediate learners who want one broad, affordable course covering the full landscape of classic deep learning architectures, rather than a narrow deep-dive into a single framework. 

It’s a strong pick if you want breadth and a large body of hands-on projects to reference later, though it leans more toward classic architectures than cutting-edge transformer/LLM-first coursework.

PyTorch for Deep Learning Bootcamp

Created by Andrei Neagoie and Daniel Bourke, this is one of Udemy’s most popular deep learning bootcamps, and for good reason. 

It’s built entirely around “learning by building,” with almost every module ending in a project you can point to. Bourke also maintains the course’s companion materials as a free online book at learnpytorch.io, so if you want to sample the content before enrolling, you actually can.

Skills you’ll learn

The course moves through PyTorch fundamentals (tensors, the core data structure behind everything in deep learning), building a full PyTorch workflow from scratch, neural network classification, computer vision, and working with custom datasets. 

It also covers “going modular,” turning notebook code into reusable Python scripts, a skill most beginner courses skip entirely but that matters a lot in real engineering teams.

Hands-on projects

This is a heavily project-based course rather than a lecture-and-quiz format. 

You’ll apply transfer learning to real image classification tasks, run experiment tracking to compare model performance, and even replicate a research paper’s architecture from scratch. 

By the end, you’ll build and deploy your own custom-trained PyTorch model, so it’s publicly accessible, not just running locally in a notebook.

Tools covered: PyTorch, including PyTorch 2.0 and torch.compile, covered in a dedicated module.

Time commitment

Self-paced with lifetime access; it’s a lengthy, comprehensive bootcamp (the instructors describe it as intentionally thorough rather than a quick primer), so expect real weeks of study rather than a weekend project.

Best for

Learners who specifically want to become job-ready in PyTorch and care about deployment and production skills, not just model-building. It suits people comfortable with a “fair warning: this is comprehensive” pace, and who want a portfolio of real, deployable projects by the end rather than isolated notebook exercises.

TensorFlow 2.0: Deep Learning and Artificial Intelligence (Lazy Programmer)

This is a broad and unique course from Lazy Programmer Inc., a well-known instructor duo in the Udemy AI space. 

Where courses like the Complete TensorFlow 2 and Keras Bootcamp focus on depth in a few core skills, this one is explicitly built for breadth: it touches nearly every major deep learning application area in one package, from computer vision to reinforcement learning to GANs. 

The instructor is upfront about the tradeoff: this course prioritizes building lots of real projects over deep theoretical dives, so if you want heavy math, this isn’t the one.

Skills you’ll learn

You’ll progress from basic machine learning models up through the major deep learning architectures like Deep Neural Networks, Convolutional Neural Networks for image processing, and Recurrent Neural Networks for sequence data. 

Optional “in-depth” sections cover the theory side, like loss functions and different gradient descent approaches, for anyone who wants to go beyond just running the code.

Hands-on projects

This course is stacked with distinct, varied projects rather than one long build. You’ll work through Natural Language Processing tasks, build recommender systems, apply transfer learning to computer vision problems, construct Generative Adversarial Networks (GANs) that generate realistic images, and build a deep reinforcement learning stock trading bot, plus time series forecasting and stock price prediction projects that are new additions specific to this TensorFlow 2 version.

Tools covered: TensorFlow 2.0 and NumPy, with all major deep learning architecture types covered under one roof.

Time commitment

Self-paced with lifetime access (26+ hours); expect a substantial course given the sheer number of topic areas covered; this is not a weekend course.

Best for

Learners who already know Python and NumPy and want a fast, wide-ranging tour of deep learning’s major application areas like computer vision, NLP, GANs, recommender systems, and reinforcement learning. It suits people who prefer learning by seeing many different real projects.

Practical Deep Learning: Master PyTorch in 15 Days (Jannis Seemann)

This is one of the most beginner-friendly, structure-obsessed courses on this list. Where other options ask you to figure out your own pace across dozens of hours of loosely connected content, Jannis Seemann’s course gives you a literal day-by-day roadmap, 15 days from zero PyTorch knowledge to deployed models, with no assumed prior experience required.

Skills you’ll learn

You’ll build up from single-neuron regression through full neural network classifiers and into convolutional networks and transfer learning. 

The course deliberately paces conceptual difficulty against project difficulty, so each new architecture is introduced right when you’re ready to apply it, rather than front-loading theory.

Hands-on projects

This is where the day-by-day structure really shows its value. 

Day 3 – 4 has you predicting used car prices with a single-neuron regression model. 

Day 5 – 6 builds an SMS spam detector using a neuron classifier. 

Day 7 – 8 predicts student exam results with a full neural network, followed by a loan approval classification exercise. 

Day 9 – 10 tackles multi-class classification on handwritten digits, Day 11 – 12 moves to convolutional networks for fashion item classification, and Day 13 – 14 applies transfer learning with ResNet to a tire quality prediction task. 

The course closes on Day 15 by teaching you to deploy your trained models as interactive apps using Gradio.

Tools covered: PyTorch and Gradio for deployment, with light exposure to data preprocessing techniques across images and text.

Time commitment

Structured as a 15-day plan, self-paced but built with a clear daily cadence; each “day” pairs a concept with a matching project, plus interactive quizzes and exams at key checkpoints (Day 6, Day 15) to test retention.

Best for

Absolute beginners who want a clear, low-overwhelm path into PyTorch without wading through a 40+ hour mega-course first. It’s especially strong for anyone who wants deployment experience baked in from day one.

Which Course Should You Choose?

If you’re not sure where to start, use this quick decision guide based on your background and goals.

TensorFlow vs PyTorch: Which Will You Learn?

Want to know the detailed difference between TensorFlow and PyTorch? My TensorFlow and PyTorch comparison article will give you better insights.    

How to Build an AI Portfolio While Learning

Finishing a course is great, but a certificate alone won’t get you hired. Employers want to see proof you can build things. The good news: every project you complete in these courses is raw material for a portfolio. You just need to package it right.

GitHub is your foundation. Push every project, even the small ones, like the spam classifier from a 15-day course, to its own repo with a clear README explaining what the model does, what dataset you used, and what results you got. Recruiters skim READMEs before they skim code, so treat that file like a mini pitch.

Kaggle lets you go beyond course exercises. Join a beginner-friendly competition using skills from your course (image classification and tabular prediction competitions are great starting points) and publish your notebook publicly. It shows you can apply what you learned to a dataset nobody handed you pre-cleaned.

Hugging Face is worth using once you’ve built anything with transformers or NLP. Upload a fine-tuned model to the Hub, even a small one. It’s free, and it puts your work in front of the same ecosystem researchers and engineers actually use.

A personal website ties everything together. A simple portfolio site with project summaries, live demo links (Gradio or Streamlit apps work well here), and links back to your GitHub repos turns scattered work into a coherent narrative.

LinkedIn is where you make it visible. Post short project breakdowns as you finish them; what problem you solved, what you learned, what you’d improve, instead of saving everything for one big “I finished my course!” post. Consistent, smaller posts build more traction over time.

Frequently Asked Questions

Final Verdict

Stop comparing courses. Start one today.

Best overall: Deep Learning Specialization by DeepLearning.AI. It’s the gold standard, trusted by employers, and backed by real credentials.

Best for total beginners: Practical Deep Learning: Master PyTorch in 15 Days. No experience needed, no overwhelm, just 15 focused days.

Best value for breadth: Deep Learning A-Z 2026. One purchase, nearly every major architecture covered.

None of these courses will change your career sitting in your browser tabs. Pick one tonight, enroll, and finish it before you even think about the next one. 

The people landing AI roles aren’t the ones who found the “perfect” course. They’re the ones who actually finished a course and built something with it.

Are you in search of valuable deep learning courses to learn new skills? If so, this article lists the 14 best deep learning courses online for both novice and experienced learners.  

Deep learning is a buzzing technology these days, encouraging students and professionals to learn trending skills to work with data. If you want to build a career in this field, then joining a valuable deep learning course online will help you.   

In short, Deep Learning is a sub-field of artificial intelligence and machine learning that deals with artificial neural networks that learn complex data patterns from big data sets.  

So, by learning the fundamentals of deep learning, you will get opportunities to enhance your skills in machine learning and artificial intelligence.  

In this article, I have listed 14 of the best deep learning courses online, which I have selected from many options available. Whether you are a beginner learner or a professional looking to enhance your skills, you will find a suitable course from this list.

Interested in learning Machine Learning techniques? Check out our article on best machine learning courses available online.

List of the Best Deep Learning Courses Online

1. Deep Learning Specialization – Offered by DeepLearning.AI   

The first entry on this list of the best deep learning courses online is the Deep Learning Specialization Course. It covers the fundamentals of deep learning, enabling you to understand what deep learning is and its applications and challenges.  

It discusses how to build and train neural network architectures (CNN, RNN, Transformers, LSTMs) using top-notch strategies such as Dropout, BatchNorm, etc. Also, you will learn the concepts of Python and TensorFlow and how to use them in the field of deep learning.  

By joining this course, you will take your first step toward the field of AI and learn the necessary skills to enhance your career.  

Who is this course for?  

Anyone who is willing to step into the world of AI and understand the concepts of deep learning should join this course. Students should have a basic understanding of Python, linear algebra, and machine learning to be able to understand the topics covered in this course.  

If you are someone who is in a technical role but doesn’t understand neural networks and deep learning, this course will help you advance your career. Apart from that, anyone with programming experience and interests in artificial intelligence and neural networks can consider joining this course.  

Course Highlights   

Institution – DeepLearning.AI    

Platform – Coursera   

Level – Intermediate Level   

Instructor – Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh         

Duration – 3 Months   

Rating – 4.9  

Language Options – 22 Languages Available   

Schedule Type – Flexible Scheduling 

What you will learn  

The curriculum of this deep learning specialization course comprises four courses on the topic of deep learning. This course begins with the fundamentals of neural networks and deep learning. You will learn the ability, importance, and challenges of deep learning and how to use it to build advanced AI technologies.  

Further, the course explains the techniques of deep neural networks, such as hyperparameter tuning, regularization, and optimization. Then, you will learn how to build deep learning projects using your experience in machine learning and the guidance of top instructor Andrew Ng.  

The concepts of convolutional neural networks and sequence models will also be covered in this course.  

2. Introduction To Deep Learning and Neural Networks with Keras – Offered By IBM 

Those who are looking to build a career in the field of deep learning and participate in building excellent technologies with AI can join this course. You will get to know about the fundamentals of deep learning and neural networks in this course.  

In this course, you will learn how to build deep learning models using the Keras library. Throughout this online course, you will learn the basics of deep learning models and neural networks and the differences between them. Also, it discusses the fundamentals of supervised and unsupervised deep learning.  

Who is this course for?  

Any person who is interested in the field of AI and deep learning can consider joining this course. Since this course is of the intermediate level, students should have some experience in programming and related fields to understand these topics better.  

If you want to do a job in this field or learn the deep learning concepts to advance your career, then you can join this course.  

Course Highlights    

Institution – IBM     

Platform – Coursera    

Level – Intermediate Level    

Instructor – Alex Aklson          

Duration – 8 Hours Approx     

Rating – 4.7   

Language Options – 22 Languages Available    

Schedule Type – Flexible Scheduling 

What you will learn  

The first chapter of this course explains what deep learning and neural networks are and why deep learning is becoming so popular. You will resonate deep learning algorithms with the functions of the human brain in this course.  

Then, the course discusses artificial neural networks, including the gradient descent algorithm, backpropagation, and activation functions.  

In the next chapter, you will be introduced to several deep learning libraries such as Keras, TensorFlow, and PyTorch and learn to use the Keras library to build classification and regression models.       

Finally, you will build deep learning models using the Keras library and do a similar learning project in the end. By completing this course, you will have a better understanding of deep learning and neural networks and learn how to use the Keras library.  

3. Introduction To TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning – Offered by DeepLearning.AI  

This introductory course on TensorFlow is a great choice for anyone who wants to learn this open-source framework for machine learning, deep learning, and artificial intelligence. It teaches how to use TensorFlow to build deep learning and machine learning models to solve real-world problems.  

This course doesn’t describe deep learning or machine learning in detail. If you want to learn these concepts, you can join the Deep Learning Specialization Course or the Machine Learning Specialization Course.   

Who is this course for?  

Anyone who is interested in deep learning and wants to learn how to use TensorFlow for the creation of deep learning models can join this course. 

Having knowledge of Python programming, deep learning, and machine learning fundamentals will help you in this course.  

If you are looking for a specific course to learn what TensorFlow is and how to use it with deep learning, then this course is for you.  

Course Highlights    

Institution – DeepLearning.AI     

Platform – Coursera    

Level – Intermediate Level    

Instructor – Laurence Moroney          

Duration – 17 Hours Approx     

Rating – 4.8   

Language Options – 22 Languages Available    

Schedule Type – Flexible Scheduling 

What you will learn  

The first chapter of this course introduces machine learning and deep learning, describing what they are and their capabilities. With basic programming skills, you can understand these concepts better.  

Then, the course discusses what computer vision is and solving problems with your code. The later session of this course discusses convolutional neural networks and how to use them to enhance computer vision. Finally, you will work with real-world images and learn how to improve them using deep neural networks.  

4. Introduction To Deep Learning – Offered by Udacity  

Introduction to Deep Learning is an introductory course that discusses the fundamentals of deep learning and its applications. You will learn both the theory and practical aspects of deep learning in this course.  

The course explains deep learning algorithms, architectures, and goals, along with their fundamentals. Also, it introduces the students to the PyTorch framework and how to use it to deploy deep learning models.  

Overall, this course is a perfect choice to learn the basics of deep learning and use the PyTorch library for deep learning models.  

Who is this course for?  

Anyone who wants to learn deep learning and its uses can consider joining this course. This course describes how to determine the right scenario to use the deep learning tool.  

This course requires students to have skills in linear algebra. Also, having skills in the related field will be helpful in this course. If you have an interest in the latest technologies, such as neural networks, deep learning, and artificial intelligence, and want to explore these fields, this course will fulfill your needs.  

Course Highlights  

Platform – Udacity   

Level – Intermediate Level   

Instructor – Erick Galinkin        

Duration – 4 weeks   

What you will learn  

In the first lesson, you will meet the instructor, Erick Galinkin, and get introduced to the topics covered in this course. Then, you will learn about deep learning and when to use it for your work with some examples.   

The next chapter will teach you different concepts of neural networks, including Backpropagation, Gradient Descent, and Error Functions. Also, you will come to know how neural network algorithms function like the human brain and solve certain problems.  

Then, the course describes how to train neural networks using several techniques such as regularization, early stopping, dropout, local minima, and random restart. After that, you will do a project with PyTorch that classifies using MNIST Dataset.  

5. Intro To Deep Learning with PyTorch – Offered by Udacity  

Intro to Deep Learning is a free course on the fundamentals of deep learning that will teach students how to create deep neural networks with PyTorch. PyTorch is a powerful and open-source machine learning framework that helps deploy deep learning models.  

It has a wide range of applications, including computer vision and natural language processing. You will get to know about this machine learning library and how to use it in this free deep learning course on Udacity.  

Who is this course for?  

Anyone who is interested in learning the concepts of deep learning and neural networks can consider joining this course. It doesn’t require any previous experience of students in similar fields, but having such skills will be good.  

If you are looking for an introductory course on deep learning to understand the basics, you can join this course.  

Course Highlights  

Platform – Udacity   

Level – Beginner Level   

Instructor – Luis Serrano, Alexis Cook, Soumith Chintala, Cezanne Camacho, Mat Leonard 

What you will learn  

In the beginning, you will learn the functioning of neural networks and how to train them using data. Then, the course discusses what PyTorch is and explains how to use it to create and train deep neural networks.  

The course also covers convolutional neural networks, recurrent neural networks, style transfer, and deployment of PyTorch models.  

By the end of this course, you will be able to understand the basics of deep learning and neural networks and use PyTorch Library to build and train deep neural networks.  

6. Deep Learning for Business – Offered by Yonsei University 

Deep learning, machine learning, and artificial intelligence are emerging technologies that are being used in every field nowadays. If you don’t use this in your product or service, you will be far from the trend.  

Therefore, it is crucial to learn these techniques and apply them to your business for better results. With this motto, this deep learning course has been designed by Yonsei University.  

In this course, you will learn what deep learning is and how to use it with TensorFlow for the advantage of your business.  

Who is this course for?  

Deep learning for business is a novice-friendly course, so no previous experience in this field is required to join this course. If you are interested in learning about the latest technologies to use them in your business, then this course might meet your needs.  

This course is also perfect for those who want to learn the concepts of deep learning and apply them to prepare strategies for certain problems.  

Course Highlights    

Institution – Yonsei University     

Platform – Coursera    

Level – Beginner Level    

Instructor – Jong-Mong Chung           

Duration – 8 Hours Approx     

Rating – 4.4   

Language Options – 22 Languages Available    

Schedule Type – Flexible Scheduling 

What you will learn  

The deep learning for the business course will teach you how to use deep learning and machine learning techniques in your products and services for the betterment of your business.  

Out of three parts of this course, the first part discusses how to design future business strategies using deep learning and machine learning technologies. This session also covers building new products and services using deep learning techniques.  

In the second part, you will go a step deeper into these concepts and understand what neural networks are and different types of neural networks, such as convolutional neural networks and recurrent neural networks.  

The last part of this course explains the TensorFlow framework. First, you will learn how to use this library and design neural networks using it. This will be helpful for you in building deep learning models for complex business problems.  

7. Applied AI with Deep Learning – Offered By IBM 

The Applied AI with Deep Learning is an invaluable course by IBM that explains deep learning models, computer vision, natural language processing, time series analysis, etc. This course is a part of the IBM Advanced Data Science Certificate program.  

In this course, you will learn the basics of linear algebra and neural networks and get introduced to deep learning open-source libraries such as TensorFlow, Keras, PyTorch, Apache System ML, and DeepLearning4J.  

A major part of this course discusses the TensorFlow and Keras libraries, educating you about their features and how to use them to design deep learning models.  

Who is this course for?  

If you are in the field of AI or machine learning and want to enhance your skills, you can consider joining this course. Programming in Python is necessary, but having coding skills in any language will also work.  

In addition to that, students should also understand linear algebra, which will be helpful for understanding the topics better.  

If you are in a role that requires an understanding of deep learning concepts and deep learning libraries such as TensorFlow and Keras, then this course will be suitable for you.  

Course Highlights    

Institution – IBM     

Platform – Coursera    

Level – Advanced Level    

Instructor – Romeo Kienzler, Niketan Pansare, Tom Hanlon, Max Pumperla, Ilja Rasin           

Duration – 24 Hours Approx     

Rating – 4.4   

Language Options – 22 Languages Available    

Schedule Type – Flexible Scheduling 

What you will learn  

First, you will understand what deep learning is and its importance in the field of artificial intelligence. You will encounter various deep learning libraries such as TensorFlow, PyTorch, Keras, DeepLearning4J, and Apache SystemML.  

You will learn more details about TensorFlow and Keras libraries and learn how to create deep learning models using them. This course also explains different functions such as image recognition, anomaly detection, time series forecasting, and neural language processing using the Keras library.  

The last session of this course discusses how to use Kubernetes, Apache Spark, and GPUs. To learn the concepts of machine learning and data science, you can join the Advanced Data Science with IBM specialized course.  

8. Deep Learning – Offered By IIT Ropar   

If you are looking for a free-to-join course to learn the fundamental building blocks and algorithms of deep learning, then this course is perfect for you. It is an AICTE-approved course offered by IIT Ropar.  

In this course, you will learn what deep learning and neural networks are and how to train them using different algorithms such as Gradient Descent, Nesterov Accelerated Gradient Descent, Adam, RMSProp, and AdaGrad.  

Also, this deep learning course covers the basics of deep architectures, which are used for solving various problems in computer vision and natural language processing.  

Who is this course for?  

Anyone who is interested in deep learning and neural networks and wants to learn more about them can consider joining this course. Even if you just want to explore the field of deep learning for fun, you can enroll in this course. 

The prerequisites of this course are prior knowledge of linear algebra and probability theory. Students should also have experience in machine learning to take complete benefits of this course.  

Course Highlights    

Institution – IIT Ropar     

Platform – NPTEL 

Level – intermediate/advanced Level    

Instructor – Prof. Sudarshan Iyengar, Prof. Padmavati             

Duration – 12 weeks (about 3 months) approx.     

What you will learn  

The course starts with the fundamentals of deep learning and goes deeper into different concepts of deep learning. It discusses neural networks, autoencoders, and different algorithms of deep learning.  

In this way, you will learn various aspects of deep learning concepts and understand the subject better. The final part of this course discusses how to use deep architectures to solve various problems in natural language processing and computer vision.  

9. Deep Learning with TensorFlow 2.0, Keras and Python – Offered by Codebasics YouTube Channel  

This is a beginner-friendly deep learning course available for free for everyone on YouTube platform. It covers the concepts of deep learning and why this is a buzzing field. You will understand the advantages of deep learning and how to use it for your work.  

In addition to the basics, this course also covers deep learning libraries such as TensorFlow, Keras, and PyTorch. You will also learn the difference between these libraries in this course.  

This course also provides several exercises for each concept so that you can understand them better. You can practice them on your existing system as it doesn’t require any specific system.  

Who is this course for?  

Any person who is interested in learning new technologies and wants to explore the field of deep learning can join this course. By the way, it is available on YouTube, so anyone can access it at any time.  

It teaches deep learning from scratch, but knowing a little about Python, pandas, and machine learning will be helpful for you.  

What you will learn  

In the beginning, you will learn what deep learning is and get a complete overview of it. Then, you will explore open-source frameworks such as TensorFlow, Keras, and PyTorch and understand the differences among these libraries.  

After that, the instructor discusses the concepts of artificial neurons, neural networks, and training of neural networks. In this context, you will learn changing rules, gradient descent, recurrent neural networks, convolutional neural networks, Word2Vec, BERT, etc.  

Apart from these, there are several other topics such as Droupout regularization, computer vision, image classification, handling imbalanced datasets, YOLO algorithm, etc.  

10. Deep Learning Full Course – Offered by Edureka On YouTube  

This is another excellent course on deep learning available on YouTube. It is a 6-hour long video that covers everything related to deep learning and TensorFlow.  

You just have to spare 6 hours a day and watch this video to understand the concepts of deep learning and its algorithms. From the basics of deep learning & AI to deep learning algorithms, applications, and interview questions are covered in this course.  

Who is this course for?  

If you are in search of a complete course on deep learning to understand it and use it in your projects for free, then this course will be perfect for you. It is also suitable for students who are looking to learn deep learning for free.  

If you are not in the technical field but want to learn deep learning just for fun, then this course will be suitable for you. Whether you are a novice or a professional, this course will help you master deep learning.  

What you will learn  

This deep learning course introduces deep learning, machine learning, and artificial intelligence to students. It also explains the applications of deep learning and machine learning.  

Then, the course discusses the concept of artificial neurons and neural networks. After that, the course covers 8 deep learning frameworks – TensorFlow, PyTorch, Keras, DeepLearning4J, Chainer, CNTK, Caffe, and MXNet.  

The concepts of autoencoders and their types will also be covered. By the end of this course, you will know what deep learning is, how it works, its algorithms, and its applications. 

11. Deep Neural Networks with PyTorch – Offered By IBM 

Deep Neural Networks with PyTorch is an intermediate-level course on deep learning concepts that discusses how to build deep learning models using the PyTorch framework.  

In this course, you will learn about deep learning algorithms, machine learning methods, neural networks, and how to build deep learning applications using PyTorch.  

In 10 modules of this course, you will encounter different topics related to deep learning.  

Who is this course for?  

If you are in the field of AI and machine learning and want to learn about deep learning algorithms and the PyTorch library, then this course will be suitable for you. Having some experience in Python and related fields will be helpful in this course.  

If you are looking for a specific course that covers the concepts of deep learning and the PyTorch library, this course will suit your needs.  

Course Highlights    

Institution – IBM     

Platform – Coursera    

Level – Intermediate Level    

Instructor – Joseph Santarcangelo           

Duration – 30 Hours Approx     

Rating – 4.4   

Language Options – 22 Languages Available    

Schedule Type – Flexible Scheduling 

What you will learn  

The beginning of this course describes the datasets and tensors in PyTorch. Then it explains different topics such as linear regression, logistic regression, softmax regression, neural networks, and different types of neural networks.  

Also, this course explains several other deep learning methods. The Deep Neural Networks with PyTorch is a part of the IBM AI Engineering Professional Certificate. If you are interested in learning deeper concepts of AI, you can consider joining this course.  

12. Deep Learning Nano Degree Program – Offered By Udacity  

Deep Learning Nano Degree Course is a perfect course to learn the fundamentals of deep learning. The course covers both theory and practice related to deep learning and explains how to decide when to use deep learning.  

You will learn about algorithms, architecture, goals, and neural networks in deep learning throughout this course. After completing this course, students can create deep learning models and neural networks using PyTorch.  

Who is this course for?  

Any person who is interested in AI technology and wants to explore the field of deep learning will find this fundamental course on deep learning highly valuable.  

To join this course, you just need to know Python, machine learning basics, matrix manipulation, pandas, and linear algebra.  

If your job requires you to learn deep learning to apply these techniques to your projects, this course will suit your needs.  

Course Highlights       

Platform – Udacity    

Level – Intermediate Level    

Instructor – Giacomo Vianello, Nathan Klarer, Erick Galinkin, Thomas Hossler 

Duration – 4 Months Approx      

Schedule Type – Flexible Scheduling 

What you will learn  

The course will start with an introduction to the instructors and an introductory lesson. It explains the basics of deep learning, fundamental algorithms, and neural networks.  

In the second part, you will learn about different types of neural networks, such as convolutional neural networks, recurrent neural networks, convolutional generative adversarial neural networks, and their functions.  

This course also discusses PyTorch and how to use this platform to create and train deep learning models. The final part of this course will help you shape your career in the field of artificial intelligence by providing appropriate guidance and helping you enhance your LinkedIn Profile.  

13. TensorFlow 2 For Deep Learning Specialization – Offered by Imperial College London  

TensorFlow 2 for deep learning specialization is a course by Imperial College London to help students create, train, and test deep learning models. Also, it discusses how to predict certain situations using deep learning models.  

You will encounter the TensorFlow framework to build deep learning models for several applications. In this context, you will learn how to use TensorFlow APIs and create complex and flexible deep learning models.  

Who is this course for?  

This specialized deep learning course is suitable for anyone who is in the field of artificial intelligence and machine learning and wants to practice the skills of deep learning with the TensorFlow library.  

This course is not for beginners who don’t have experience in any related field. Specifically, students should know Python 3, basic machine learning concepts, basic deep learning concepts, and the concepts of statistics and probability to understand the topics better.  

Course Highlights    

Institution – Imperial College London     

Platform – Coursera    

Level – Intermediate Level    

Instructor – Dr Kevin Webster           

Duration – 3 Months Approx     

Rating – 4.8   

Language Options – 22 Languages Available    

Schedule Type – Flexible Scheduling 

What you will learn  

The TensorFlow 2 deep learning specialization course has three courses, and the first course discusses concepts of using TensorFlow for deep learning. This includes how to build, train, test, and predict using deep learning models in TensorFlow.  

With the tutorials provided in this course, you will get practical experience in using TensorFlow. The next course focuses on modifying deep learning models using the TensorFlow framework.  

In the last course, you will learn the concepts of probabilistic approach in deep learning using TensorFlow. This will help you create probabilistic models using the probability library of TensorFlow.  

14. Multi-Backened Deep Learning with Keras – Offered by Udacity 

This is another valuable course on deep learning for developers or learners who want to use machine learning in their Python projects. While browsing through the course content, I found that this course gives a thorough explanation of the fundamentals of deep learning, which will be really helpful for anyone who wants to start with the basics. 

Then, it describes how to use Keras, TensorFlow, JAX, and PyTorch frameworks to build and train machine learning frameworks. In addition to that, it also covers the concepts of neural networks.  

Who is this course for?  

This is a 3-hour intermediate-level course suitable for any programmer or developer who wants to learn the use of different deep learning frameworks and create multi-backend deep learning abstraction using Keras.  

I would recommend this course to students who are interested in the fields of AI and ML and want to explore the field of deep learning with Keras. To join this course, knowing intermediate Python and NumPy is necessary.  

Course Highlights      

Platform – Udacity    

Level – Intermediate Level    

Instructor – Jesse Chan 

Duration – 3 Hours Approx      

Schedule Type – Flexible Scheduling 

What you will learn  

First, this course will explain the fundamentals of deep learning and multi-backend Keras briefly. Then, it explains how to build and train deep learning models using multi-backend Keras. Also, the course explains how to build image classification models using Keras.  

In addition to this, you will also encounter other deep learning frameworks such as TensorFlow, PyTorch, and Jax. Then, the course covers the fundamentals of neural networks and explains how they function. By completing this course, you will learn how to use multi-backend Keras and other deep learning frameworks.       

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