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.

DeepLearning.AI
Skills you'll gain: Computer Vision, Deep Learning, Image Analysis, Natural Language Processing, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Tensorflow, Generative AI, Supervised Learning, Large Language Modeling, Keras (Neural Network Library), Artificial Intelligence, Applied Machine Learning, PyTorch (Machine Learning Library), Machine Learning, Debugging, Performance Tuning, Python Programming, Data-Driven Decision-Making, Feature Engineering
Build toward a degree
Intermediate · Specialization · 3 - 6 Months

DeepLearning.AI
Skills you'll gain: Deep Learning, Artificial Neural Networks, Supervised Learning, Artificial Intelligence, Machine Learning, Python Programming, Linear Algebra, Calculus
Intermediate · Course · 1 - 4 Weeks

Skills you'll gain: PyTorch (Machine Learning Library), Keras (Neural Network Library), Deep Learning, Reinforcement Learning, Unsupervised Learning, Artificial Neural Networks, Machine Learning Methods, Generative AI, Tensorflow, Artificial Intelligence and Machine Learning (AI/ML), Image Analysis, Computer Vision, Statistical Modeling, Artificial Intelligence, Geospatial Information and Technology, Machine Learning, Regression Analysis, Data Pipelines, Network Architecture, Network Model
Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Keras (Neural Network Library), Deep Learning, Artificial Neural Networks, Tensorflow, Machine Learning Methods, Image Analysis, Computer Vision, Regression Analysis, Network Architecture, Network Model, Natural Language Processing, Machine Learning
Intermediate · Course · 1 - 3 Months

Skills you'll gain: PyTorch (Machine Learning Library), Deep Learning, Artificial Neural Networks, Computer Vision, Supervised Learning, Machine Learning, Network Architecture
Intermediate · Course · 1 - 3 Months

Skills you'll gain: Keras (Neural Network Library), Image Analysis, Deep Learning, Artificial Neural Networks, Tensorflow, Data Processing, Computer Vision, Data Transformation, Financial Forecasting, Applied Machine Learning, Feature Engineering, Artificial Intelligence and Machine Learning (AI/ML), Data Visualization, Time Series Analysis and Forecasting, Exploratory Data Analysis, Python Programming, Customer Analysis, Predictive Modeling, Google Cloud Platform, Development Environment
Beginner · Specialization · 1 - 3 Months

Multiple educators
Skills you'll gain: Unsupervised Learning, Supervised Learning, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Responsible AI, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Python Programming
Beginner · Specialization · 1 - 3 Months

DeepLearning.AI
Skills you'll gain: Computer Vision, Image Analysis, Deep Learning, Artificial Neural Networks, Keras (Neural Network Library), Tensorflow, Applied Machine Learning, PyTorch (Machine Learning Library), Artificial Intelligence and Machine Learning (AI/ML), Feature Engineering, Algorithms
Intermediate · Course · 1 - 4 Weeks

DeepLearning.AI
Skills you'll gain: PyTorch (Machine Learning Library), Data Quality, Generative AI, Deep Learning, MLOps (Machine Learning Operations), Data Pipelines, Application Deployment, Artificial Neural Networks, Software Visualization, Computer Vision, Dimensionality Reduction, Natural Language Processing, Machine Learning
Intermediate · Professional Certificate · 1 - 3 Months

Imperial College London
Skills you'll gain: Tensorflow, Generative Model Architectures, Data Pipelines, Keras (Neural Network Library), Deep Learning, Image Analysis, Computer Programming, Program Development, Data Validation, Applied Machine Learning, Bayesian Statistics, Supervised Learning, Natural Language Processing, Data Processing, Predictive Modeling, Computer Vision, Machine Learning Methods, Artificial Neural Networks, Machine Learning, Unsupervised Learning
Intermediate · Specialization · 3 - 6 Months

Pearson
Skills you'll gain: Large Language Modeling, Deep Learning, Prompt Engineering, Image Analysis, PyTorch (Machine Learning Library), Tensorflow, LLM Application, Computer Vision, Responsible AI, Natural Language Processing, Generative AI, Artificial Neural Networks, Data Ethics, Multimodal Prompts, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning Methods, Artificial Intelligence, Application Deployment, Time Series Analysis and Forecasting
Intermediate · Specialization · 1 - 4 Weeks

Skills you'll gain: Natural Language Processing, Keras (Neural Network Library), Generative AI, Generative Model Architectures, Image Analysis, Artificial Neural Networks, Text Mining, Computer Vision, Tensorflow, Deep Learning, Feature Engineering, Performance Testing, Machine Learning Methods, Applied Machine Learning, Google Cloud Platform, Application Development, Data Processing, Systems Development, Python Programming, Data Transformation
Beginner · Specialization · 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.‎