Deep learning courses can help you learn neural networks, convolutional networks, and recurrent networks, along with their applications in image recognition and natural language processing. You can build skills in model training, hyperparameter tuning, and performance evaluation, which are crucial for developing effective AI solutions. Many courses introduce tools like TensorFlow and PyTorch, allowing you to implement algorithms and optimize models, making your learning experience hands-on and relevant to current industry practices.
University of Illinois Urbana-Champaign
Skills you'll gain: Deep Learning, Health Informatics, Image Analysis, Generative Model Architectures, Machine Learning, Applied Machine Learning, Health Care, Artificial Neural Networks, Supervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Graph Theory, Computer Vision, Tensorflow, PyTorch (Machine Learning Library), Predictive Modeling, Medical Science and Research, Unsupervised Learning, Program Development, Big Data
Advanced · Specialization · 1 - 3 Months

Skills you'll gain: Keras (Neural Network Library), Deep Learning, PyTorch (Machine Learning Library), Machine Learning Methods, Computer Vision, Geospatial Information and Technology, Machine Learning, Data Pipelines, Python Programming
Advanced · Course · 1 - 4 Weeks

Skills you'll gain: Apache Spark, Keras (Neural Network Library), Deep Learning, Tensorflow, A/B Testing, Big Data, Data Ethics, Applied Machine Learning, Data Processing, Machine Learning Software, Artificial Neural Networks, Machine Learning Algorithms, Data Cleansing, Machine Learning, MLOps (Machine Learning Operations), Artificial Intelligence, Supervised Learning, Statistical Hypothesis Testing, Dimensionality Reduction, Reinforcement Learning
Advanced · Course · 1 - 3 Months

Google Cloud
Skills you'll gain: Google Cloud Platform, Natural Language Processing, Tensorflow, MLOps (Machine Learning Operations), Large Language Modeling, Reinforcement Learning, Computer Vision, Keras (Neural Network Library), Systems Design, Applied Machine Learning, Image Analysis, AI Personalization, Hybrid Cloud Computing, Systems Architecture, Performance Tuning, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Artificial Neural Networks, Machine Learning, Pandas (Python Package)
Advanced · Specialization · 3 - 6 Months

University of Illinois Urbana-Champaign
Skills you'll gain: Deep Learning, Image Analysis, Applied Machine Learning, Artificial Neural Networks, Computer Vision, Predictive Modeling, Health Informatics, Machine Learning, Unsupervised Learning, Feature Engineering, Medical Imaging, Python Programming, Dimensionality Reduction, Network Architecture
Advanced · Course · 1 - 4 Weeks

University of Illinois Urbana-Champaign
Skills you'll gain: Deep Learning, Generative Model Architectures, Applied Machine Learning, Machine Learning Methods, Image Analysis, Graph Theory, Tensorflow, Artificial Neural Networks, PyTorch (Machine Learning Library), Health Informatics, Predictive Modeling, Unsupervised Learning, Natural Language Processing, Data Synthesis, Python Programming
Advanced · Course · 1 - 4 Weeks

Skills you'll gain: PyTorch (Machine Learning Library), Artificial Neural Networks, Image Analysis, Deep Learning, Computer Vision
Advanced · Guided Project · Less Than 2 Hours

Imperial College London
Skills you'll gain: Tensorflow, Generative Model Architectures, Deep Learning, Image Analysis, Bayesian Statistics, Artificial Neural Networks, Machine Learning, Unsupervised Learning, Probability & Statistics, Dimensionality Reduction
Advanced · Course · 1 - 3 Months

Skills you'll gain: AWS SageMaker, AWS Identity and Access Management (IAM), Image Analysis, Amazon Elastic Compute Cloud, Amazon S3, Applied Machine Learning, Application Deployment, Machine Learning Algorithms, Computer Vision, Deep Learning, Machine Learning
Advanced · Guided Project · Less Than 2 Hours

Skills you'll gain: Large Language Modeling, Generative Model Architectures, Prompt Engineering, Generative AI, Deep Learning, Natural Language Processing, Responsible AI, Application Deployment, Scalability, Operational Efficiency, Machine Learning, Performance Tuning, Systems Integration
Advanced · Course · 3 - 6 Months

University of Toronto
Skills you'll gain: Computer Vision, Image Analysis, Control Systems, Automation, Deep Learning, Simulation and Simulation Software, Software Architecture, Safety Assurance, Artificial Neural Networks, Global Positioning Systems, Hardware Architecture, Systems Architecture, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, Estimation, Algorithms, Machine Learning Methods, Simulations, Scenario Testing, Data Structures
Advanced · Specialization · 3 - 6 Months

Skills you'll gain: Shiny (R Package), Deep Learning, Image Analysis, PyTorch (Machine Learning Library), Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Tensorflow, Classification And Regression Tree (CART), Unsupervised Learning, Predictive Modeling, Regression Analysis, Dimensionality Reduction, Network Architecture, Interactive Data Visualization, Time Series Analysis and Forecasting, Data Processing
Advanced · Course · 1 - 3 Months
Deep learning is a powerful application of machine learning (ML) algorithms modeled after biological systems of information processing called artificial neural networks (ANN). Machine learning is an artificial intelligence (AI) technique that allows computers to automatically learn from data without explicit programming, and deep learning harnesses multiple layers of interconnected neural networks to generate more sophisticated insights.
While this field of computer science is quite new, it is already being used in a growing range of important applications. Deep learning excels at automated image recognition, also known as computer vision, which is used for creating accurate facial recognition systems and safely driving autonomous vehicles. This approach is also used for speech recognition and natural language processing (NLP) applications, which allow for computers to interact with human users via voice commands.
Machine learning algorithms such as logistic regression are key to creating deep learning applications, along with commonly used programming languages such as Tensorflow and Python. These programming languages are generally preferred for teaching and learning in this field due to their flexibility and relative accessibility - an important priority given the relevance of deep learning to a wide range of professionals without a computer science background.‎
Yes. You can start learning deep learning on Coursera for free in two ways:
If you want to keep learning, earn a certificate in deep learning, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎
Certainly - in fact, Coursera is one of the best places to learn about deep learning. Through partnerships with deeplearning.ai and Stanford University, Coursera offers courses as well as Specializations taught by some of the pioneering thinkers and educators in this field. You can also learn via courses and Specializations from industry leaders such as Google Cloud and Intel, or get a professional certificate from IBM. Guided Projects also offer an opportunity to build skills in deep learning through hands-on tutorials led by experienced instructors, allowing you to learn with confidence.‎
The skills or experience you may need to have before studying deep learning, and which can help you better understand an advanced concept such as deep learning, can include sign language reading, music generation, and natural language processing (NLP), in addition to many others. If you have knowledge of Python 3 and understand the basic concepts of general machine-learning algorithms and deep learning, you may have the necessary skills to learn this specialization. You may also want to know about probability and statistics to study deep learning concepts. Basic math, such as algebra and calculus, is also an important prerequisite to deep learning because it relates to machine learning and data science. Also, if you have worked in the tech or artificial intelligence (AI) fields, you may have the necessary experience to study deep learning.‎
The type of person who is best suited to study deep learning is someone comfortable working with statistics, programming, advanced calculus, advanced algebra, and engineering. Deep learning benefits someone passionate about working in the AI fields which can create types of deep learning networks that help machines perform human functions. A person best suited to learn about deep learning has a vested interest in understanding how the intelligence is built to run everything from driverless cars, mobile devices, stock trading systems, and robotic surgery equipment, for example. Deep learning benefits someone with a goal of working with systems such as computer vision, speech recognition, NLP, audio recognition bioinformatics systems, and medical image analysis.‎