Are you interested in diving into the world of deep learning and mastering the cutting-edge techniques used in machine learning?
Look no further!
This comprehensive blog will explore the best deep learning courses available to help you embark on your journey to becoming a skilled deep learning practitioner.
Deep learning has gained immense importance recently due to its ability to analyze and understand complex patterns in large datasets.
The demand for deep learning experts has been skyrocketing, expected to continue in the coming years.
The machine learning market has shown steady growth globally, with a value of $15.44 billion in 2021. It is projected to reach $209.91 billion by 2029, growing at a compound annual growth rate (CAGR) of 38.8% due to the rising adoption of technological advancements.
These statistics highlight the significant opportunities and career prospects that await those skilled in deep learning.
When it comes to choosing the top deep learning course, it can be overwhelming, given the multitude of options available. That’s where this review comes in handy.
We have carefully curated a list of the best deep learning courses that offer comprehensive training, practical exercises, and expert guidance.
Whether you are a beginner looking to grasp the fundamentals or an experienced professional aiming to advance your skills, our review of the best deep learning courses will help you make an informed decision.
So, get ready to embark on an exciting learning journey and unlock the potential of deep learning to revolutionize your career.
List of Top-Rated Deep Learning Courses You Should Enroll in 2023
Are you ready to dive into the world of deep learning? Check out this list of top-rated deep learning courses that you should enroll in 2023.
- IBM Professional Certificate in Deep Learning by edX
- Nanodegree in Deep Learning by Udacity
- PyTorch for Deep Learning & Machine Learning by FreeCodeCamp
- Building Advanced Deep Learning and NLP Projects by Educative
- Machine Learning with Python: From Linear Models to Deep Learning by edX
- Getting Started with Deep Learning by NVIDIA
- Yann LeCun’s Deep Learning Course at CDS by NYU
Don’t miss out on the opportunity to enhance your skills and explore the limitless possibilities of this exciting field. Enroll now and take your deep learning journey to the next level.
Our Criteria to Choose the Best Deep Learning Courses to Learn Online
When selecting the best deep learning courses to learn online, we consider several key criteria to ensure you have a rewarding learning experience. These criteria include:
- Eligibility criteria: We assess the prerequisites and requirements of each course to determine if it is suitable for beginners or requires prior knowledge in deep learning or related fields.
- Course curriculum: We evaluate the content and structure of the course curriculum to ensure it covers essential topics in deep learning, such as neural networks, convolutional networks, recurrent networks, and advanced algorithms.
- Teaching methodologies: We consider the teaching methods employed by the course, such as video lectures, interactive tutorials, and hands-on projects. Effective teaching methodologies enhance understanding and practical application of deep learning concepts.
- Reputation of the program: We review the reputation and credibility of the program by analyzing feedback and reviews from previous learners. This helps us gauge the quality of the course and its effectiveness in imparting knowledge.
- Instructors rating: We assess the expertise and experience of the instructors delivering the course. Their qualifications, industry experience, and teaching style are important factors in providing a valuable learning experience.
- Cost: We consider the pricing structure of the course to determine its affordability and value for money. We aim to recommend courses that offer reasonable pricing without compromising on quality.
- Job placement: We examine if the course provides job placement assistance or connects learners with industry opportunities. This can be crucial for those seeking career advancement or transitioning into deep learning roles.
- Career support: We evaluate the availability of career support services, such as resume building, interview preparation, and networking opportunities. These resources can significantly enhance your career prospects in deep learning.
By considering these criteria, we aim to guide you toward the best deep learning courses that align with your learning goals and provide a comprehensive and rewarding educational experience.
7 Best Courses To Learn Deep Learning| Detailed Analysis
This detailed analysis presents the 7 best courses to learn deep learning. Each course offers comprehensive content, practical exercises, and expert instructors, making them excellent choices for mastering deep learning techniques.
The Professional Certificate in Deep Learning offered by edX in collaboration with IBM is designed for beginners and intermediate learners seeking to gain expertise in deep learning.
This online course format allows flexibility in learning at your own pace and accessing course materials anytime, anywhere.
The course covers a comprehensive range of topics, providing a deep understanding of deep learning concepts and their practical applications.
Through practical exercises and projects, students can apply their knowledge and gain valuable hands-on experience in deep learning.
Upon completing the program, learners receive a certification to showcase their skills and knowledge to potential employers.
The course can be completed in 28 weeks, with an estimated commitment of 4 hours per week. The course costs around $39/month.
Considering the comprehensive content, experienced instructors, and practical exercises, the program provides value for money and is well-regarded in the field.
Taught by experienced data scientists from IBM, including Aije Egwaikhide, Romeo Kienzler, and others, the program offers valuable insights and hands-on experience with popular deep learning libraries such as Keras, PyTorch, and TensorFlow.
The course instructors, including Aije Egwaikhide, Romeo Kienzler, and others, bring a wealth of experience and expertise in data science and deep learning. Their involvement in teaching this course adds credibility and ensures high-quality instruction.
As for student reviews and ratings, no specific information is available to evaluate this course. However, the reputation of edX and IBM in the field of online education and data science lends credibility to the program.
What you will learn?
- Fundamental Concepts of Deep Learning
- Build, train, and deploy different types of Deep Architectures
- Application of Deep Learning to real-world scenarios
- Master Deep Learning at scale with accelerated hardware and GPUs
- Use of popular Deep Learning libraries
- Deep Learning Fundamentals with Keras
- Computer Vision and Image Processing Fundamentals
- PyTorch Basics for Machine Learning
- Deep Learning with Python and PyTorch
- Deep Learning with TensorFlow
- Applied Deep Learning Capstone Project
Overall, the Professional Certificate in Deep Learning by edX and IBM offers a comprehensive learning experience, practical exercises, and valuable certification, making it a recommended choice for individuals interested in advancing their knowledge and skills in deep learning.
Take the next step in your deep learning journey and enroll in this program today.
The Nanodegree in Deep Learning offered by Udacity is suitable for beginners and intermediate learners who want to understand deep learning comprehensively.
This online program allows flexibility in learning at your own pace and accessing course materials online.
The top notch deep learning course covers a wide range of topics in deep learning, including neural networks and adversarial networks. Through a series of project-based courses, learners get hands-on experience using tools like PyTorch to build various deep learning models.
Projects include creating a handwritten digits classifier, designing CNNs for image-based landmark classification, building an LSTM Seq2Seq chatbot, and training custom GAN architectures for face generation.
The program emphasizes practical exercises and provides real-time support to ensure learners gain hands-on experience and complete the projects.
Additionally, career services such as Github portfolio review and LinkedIn profile optimization are included to assist learners in their professional development.
The duration of the program is four months, with an estimated commitment of 10 hours per week.
While the program is a paid course, it offers certification upon completion, which adds value and recognition to the learner’s skillset. You can pay $399 monthly or $1356 for a 4-month course subscription.
The course instructors, including Erick Galinkin, Giacomo Vianello, Nathan Klarer, and Thomas Hossler, bring their expertise in deep learning to deliver high-quality instruction. Their experience in the field adds credibility to the course content.
The course has received positive student reviews, with a rating of 4.7/5. This indicates the satisfaction and positive learning experiences of past students. The students have positive remarks about the course on these lines.
“This is probably the most approachable way to get into deep learning I have found thus far. The course covers a lot of interesting subjects, with (usually) good explanatory videos and walkthroughs.”
What you will learn?
- Introduction to Deep Learning
- Convolutional Neural Networks
- RNNs & Transformers
- Building Generative Adversarial Networks
Enroll now and take the next step in your deep learning journey.
The PyTorch for Deep Learning & Machine Learning course offered by FreeCodeCamp is suitable for both beginners and intermediate-level students.
It is a free online course on YouTube, making it easily accessible to anyone interested in deep learning.
This course, led by Daniel Bourke, a Machine Learning Engineer, provides a comprehensive introduction to deep learning using PyTorch.
The curriculum covers many topics, including PyTorch fundamentals, workflows, neural network classification, computer vision, custom datasets, modular coding, transfer learning, experiment tracking, research replication, and model deployment.
The course takes a hands-on approach, with extensive real-world projects and exercises to reinforce learning. By completing a massive three-part project, students gain practical experience and the skills necessary for a career in deep learning.
As a free course, it does not offer a certification upon completion. However, the value lies in the knowledge and practical skills acquired throughout the course.
The instructor, Daniel Bourke, is a Machine Learning Engineer with expertise in the field. His guidance and instruction provide learners with a valuable learning experience.
As the course is available on YouTube, no specific enrollment count is available. However, it has received positive reviews from students who have found it valuable in their deep learning journey. One of the students appreciated the course material as follows:
“Amazing tutorial! Really explores the experimental methods of ML and ways to improve your model. And it starts from the basics, assumes nothing, and takes you to a level where you can further progress by yourself.”
Considering the depth of the content, practical exercises, and instructor expertise, this free course offers excellent value for learners interested in PyTorch and deep learning.
What you will learn?
- PyTorch Fundamentals
- PyTorch Workflow
- Neural Network Classification
- Computer Vision
- Custom Datasets
Start the course today and enhance your deep learning and machine learning skills.
The Building Advanced Deep Learning and NLP Projects course by Educative is designed for intermediate-level students with a basic understanding of Python, Numpy, Pandas, and artificial neural networks.
It is an online course format that offers a hands-on learning experience. This course delves into advanced Deep Learning and Natural Language Processing (NLP) concepts through a project-based approach.
With 12 industry-oriented projects, learners gain practical experience leveraging tools like NumPy, Matplotlib, Scikit-Learn, and TensorFlow.
Educative’s interactive approach allows learners to engage with the course material actively, enhancing the learning process.
The content covers various applications, including building a COVID-19 detection system using X-rays and developing an emoji predictor using NLP techniques. Students acquire valuable skills in deep learning and NLP by completing these projects.
The course provides practical exercises that allow learners to gain hands-on experience and reinforce their understanding of the concepts taught. Upon course completion, a certification is awarded, validating the acquired skills.
With a duration of 5 hours, this course offers a concise yet comprehensive learning experience. Educative offers three subscription plans: a monthly plan for $59/month, an annual plan for $16.66/month, and a 2-year plan for $14.99 per month, providing options to suit different preferences and budgets.
Considering the depth of the content, hands-on exercises, and certification offered, the Building Advanced Deep Learning and NLP Projects course by Educative provides excellent value for money.
The instructor for this course is Harsh Jain, who brings expertise in the field of deep learning and NLP. Learners can expect quality instruction and guidance throughout the course.
We cannot provide specific student reviews because the enrollment count is unavailable. However, Educative courses are generally well-regarded for their interactive learning approach and practical project-based content.
Based on the depth of content, 53 Playgrounds, 10 Quizzes, and 75 Illustrations, this course offers value for learners seeking to advance their deep learning and NLP skills.
The students’ comments are positive, indicating the course helped them improve their deep learning skills. One of the students remarked:
“Just finished my first full #ML course: Machine learning for Software Engineers from Educative, Inc. … Highly recommend!”
What you will learn?
- Build a COVID-19 Detection System Using X-Rays (10 Lessons)
- Building a Pokemon Classifier Using Transfer Learning (11 Lessons)
- Text Generation Using Markov Chains (7 Lessons)
- Two Mini Projects (8 Lessons)
- IMDB Reviews Sentiment Analysis (11 Lessons)
- Deciphering Text Using Character-Level RNNs (3 Lessons)
- Final Exam (1 Lesson)
Start building advanced projects today and take your understanding of these domains to the next level.
Machine Learning with Python: From Linear Models to Deep Learning is an online course offered by edX, with instruction from esteemed professors Regina Barzilay, Tommi Jaakkola, and Karene Chu. The course is designed for both beginners and intermediate-level students.
This course is delivered online, allowing learners to access the content and participate in learning activities at their convenience.
It offers an in-depth exploration of machine learning, covering topics from linear models to deep learning and reinforcement learning. Through hands-on Python projects, learners gain practical experience in applying the principles and algorithms of machine learning.
The course dives into various essential topics, including clustering, classification, recommender problems, probabilistic modeling, and neural networks/deep learning.
By completing the course, learners will acquire a solid understanding of these concepts and the skills necessary to develop effective automated predictions.
Practical exercises are integral to the course, enabling learners to gain hands-on experience and apply their knowledge to real-world scenarios. A certificate of completion is available for learners who choose the paid version of the course.
With a duration of 210 hours spread over 15 weeks, this course provides an immersive and comprehensive learning experience.
Though you can access this course for $300, learners can audit the course for free before deciding to pursue the paid version.
The instructors, Regina Barzilay, Tommi Jaakkola, and Karene Chu, are highly respected experts in the field of machine learning. They bring their extensive knowledge and experience to deliver high-quality instruction.
The enrollment count is 204,724, but as student reviews are not listed on the website, we cannot provide detailed feedback from previous learners. However, edX courses, especially those associated with prestigious institutions like MIT, are known for their academic rigor and high standards.
Considering the depth of content, hands-on exercises, reputation of the instructors, and affiliation with MIT, this course offers a valuable learning opportunity for individuals interested in machine learning.
What you will learn?
- Linear classifiers, separability, perceptron algorithm
- Maximum margin hyperplane, loss, regularization
- Stochastic gradient descent, over-fitting, generalization
- Linear regression
- Recommender problems, collaborative filtering
- Non-linear classification, kernels
- Learning features, Neural networks
- Deep learning, backpropagation
- Recurrent neural networks
- Generalization, complexity, VC-dimension
- Unsupervised learning: clustering
- Generative models, mixtures
- Mixtures and the EM algorithm
- Learning to control: Reinforcement learning
- Reinforcement learning continued
- Applications: Natural Language Processing
Expand your knowledge and skills in machine learning with this comprehensive course from edX.
Want to become a machine learning engineer? Check out our pick of the best machine learning courses!
Getting Started with Deep Learning by NVIDIA is a beginner-level course that introduces learners to the fundamental concepts and techniques of deep learning.
NVIDIA, a renowned company in the field of artificial intelligence and deep learning, instructs the course.
Delivered online, the course allows learners to access the content at their own pace. It covers the basics of training deep learning models. It provides hands-on exercises in computer vision and natural language processing.
Through these exercises, learners gain practical experience applying deep learning techniques to real-world problems.
The course explores topics such as data augmentation to enhance datasets, transfer learning for model efficiency, and using pre-trained models.
By completing this course, learners will develop a solid foundation in deep learning and gain the skills needed to manage their own deep learning projects.
Prerequisites for the course include knowledge of Python3, Pandas data structures, and regression lines. It has a duration of 8 hours, making it suitable for individuals who want to grasp the basics of deep learning quickly.
While the course does not offer certification upon completion, it provides valuable knowledge and practical exercises to gain hands-on experience for just $90.
As the course is instructed by NVIDIA, a prominent player in the deep learning industry, learners can benefit from the expertise and experience of the company.
While specific student reviews and ratings are not available, NVIDIA is recognized for its contributions to the field of deep learning and its advanced technologies.
Considering its beginner-level focus, practical exercises, and the reputation of NVIDIA, this course serves as a valuable starting point for individuals interested in exploring deep learning.
What you will learn?
- Fundamental techniques and tools required to train a deep learning model
- Gain experience with common deep-learning data types and model architectures
- Enhance datasets through data augmentation to improve model accuracy
- Leverage transfer learning between models to achieve efficient results with fewer data and computation
- Build confidence to take on your own project with a modern deep learning framework
Gain the foundational knowledge and skills needed to embark on your deep learning journey with this course from NVIDIA.
Yann LeCun’s Deep Learning Course at CDS by NYU is an intermediate-level course that offers a comprehensive exploration of deep learning and representation learning techniques. The course is instructed by Yann LeCun, a renowned AI expert and pioneer in the field.
Delivered online, the course is free to access and spans 15 weeks. It is designed for individuals who have completed a graduate-level machine learning course and have a solid understanding of the fundamentals.
The course curriculum covers many thematic areas, including neural nets, parameter sharing, energy-based models, associative memories, graphs, control, optimization, and more.
The content is applied in contexts such as computer vision, natural language understanding, and speech recognition, providing learners with a comprehensive understanding of the practical applications of deep learning.
Learners engage in interactive learning experiences throughout the course through platforms like Reddit and Discord. They also have assignments and homework to reinforce their understanding and gain practical experience.
While the course does not offer certification upon completion, it offers a unique opportunity to learn directly from Yann LeCun, whose contributions to AI are highly regarded.
As specific student reviews and ratings are unavailable, the reputation of NYU’s Center for Data Science and Yann LeCun’s expertise in the field indicate this free course’s quality.
What you will learn?
THEME 1: INTRODUCTION
- History and resources 🎥 🖥
- Gradient descent and the backpropagation algorithm 🎥 🖥
- Neural nets inference 🎥 📓
- Modules and architectures 🎥
- Neural nets training 🎥 🖥 📓📓
- Homework 1: backdrop
THEME 2: PARAMETERS SHARING
- Recurrent and convolutional nets 🎥 🖥 📝
- ConvNets in practice 🎥 🖥 📝
- Natural signals properties and the convolution 🎥 🖥 📓
- Recurrent neural networks, vanilla and gated (LSTM) 🎥 🖥 📓📓
- Homework 2: RNN & CNN
THEME 3: ENERGY BASED MODELS, FOUNDATIONS
- Energy based models (I) 🎥 🖥
- Inference for LV-EBMs 🎥 🖥
- What are EBMs good for? 🎥
- Energy based models (II) 🎥 🖥 📝
- Training LV-EBMs 🎥 🖥
- Homework 3: structured prediction
THEME 4: ENERGY BASED MODELS, ADVANCED
- Energy based models (III) 🎥 🖥
- Unsup learning and autoencoders 🎥 🖥
- Energy based models (VI) 🎥 🖥
- From LV-EBM to target prop to (any) autoencoder 🎥 🖥
- Energy based models (V) 🎥 🖥
AEs with PyTorch and GANs 🎥 🖥 📓📓
- THEME 5: ASSOCIATIVE MEMORIES
- Energy based models (V) 🎥 🖥
- Attention & transformer 🎥 🖥 📓
THEME 6: GRAPHS
- Graph transformer nets [A][B] 🎥 🖥
- Graph convolutional nets (I) [from last year] 🎥 🖥
- Graph convolutional nets (II) 🎥 🖥 📓
THEME 7: CONTROL
- Planning and control 🎥 🖥
- The Truck Backer-Upper 🎥 🖥 📓
- Prediction and Planning Under Uncertainty 🎥 🖥
THEME 8: OPTIMISATION
- Optimization (I) [from last year] 🎥 🖥
- Optimisation (II) 🎥 🖥 📝
- SSL for vision [A][B] 🎥 🖥
- Low resource machine translation [A][B] 🎥 🖥
- Lagrangian backdrop, final project, and Q&A 🎥 🖥 📝
Take advantage of this free course to enhance your understanding of deep learning under the guidance of Yann LeCun.
Best Deep Learning Courses – FAQs
What Are the Best Courses for Deep Learning?
The Professional Certificate in Deep Learning offered by edX and Nanodegree in Deep Learning by Udacity is our pick for the best deep learning course title.
Is Deep Learning AI Training Available for Free?
Yes, free resources are available for deep learning AI training, including online tutorials, courses, and open-source libraries, allowing individuals to learn and explore deep learning concepts without cost.
Are deep learning courses worth it?
Deep learning courses are worth it as they provide valuable knowledge and skills in a rapidly growing field, opening up exciting career prospects in artificial intelligence and machine learning.
Also, look at our machine learning vs deep learning comparison to find the field you are interested in!
Why should you study deep learning?
Studying deep learning offers immense career opportunities and allows you to be at the forefront of cutting-edge technology in artificial intelligence.
Finding the best deep learning courses to learn online is a crucial step in developing your skills and expertise in this rapidly growing field.
As organizations recognize the power of leveraging artificial intelligence to extract meaningful insights from complex data, deep learning has become increasingly important in various industries, including healthcare, finance, autonomous vehicles, and more.
The right deep learning course will provide you with a solid foundation in essential concepts, including neural networks, convolutional networks, recurrent networks, and advanced algorithms.
Following this comprehensive review of the best deep learning courses at know it get it, you can make an informed decision and embark on a transformative learning journey.
Remember to assess your goals, prioritize your learning needs, and choose a course that fits your schedule and budget.
Investing in your deep learning education is a wise decision that can open doors to exciting opportunities and enable you to contribute to advancing artificial intelligence.
Embrace the power of deep learning and embark on a path of continuous learning and professional growth.