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.

Skills you'll gain: PyTorch (Machine Learning Library), Image Analysis, Deep Learning, Computer Vision, Python Programming
Intermediate · Guided Project · Less Than 2 Hours

Skills you'll gain: PyTorch (Machine Learning Library), Generative Model Architectures, Deep Learning, Image Analysis, Python Programming
Intermediate · Guided Project · Less Than 2 Hours

Skills you'll gain: Tensorflow, Python Programming, Natural Language Processing, Artificial Neural Networks, Text Mining, Machine Learning Algorithms, Deep Learning, Machine Learning, Data Processing
Intermediate · Guided Project · Less Than 2 Hours

Skills you'll gain: Exploratory Data Analysis, Regression Analysis, Predictive Modeling, Applied Machine Learning, Data Manipulation, Data Analysis, Random Forest Algorithm, Machine Learning, Decision Tree Learning, Machine Learning Algorithms, Data Visualization Software, Artificial Neural Networks, Deep Learning, Statistical Methods, Python Programming
Beginner · Guided Project · Less Than 2 Hours

Skills you'll gain: Exploratory Data Analysis, Deep Learning, Plot (Graphics), Artificial Neural Networks, Matplotlib, Data Cleansing, Data Analysis, Tensorflow, Natural Language Processing, Data Processing, Data Manipulation, Python Programming, Machine Learning
Beginner · Guided Project · Less Than 2 Hours

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

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: PyTorch (Machine Learning Library), Image Analysis, Computer Vision, Applied Machine Learning, Deep Learning
Intermediate · Guided Project · Less Than 2 Hours

Skills you'll gain: Tensorflow, Applied Machine Learning, Python Programming, Jupyter, Artificial Neural Networks, Deep Learning, Computer Vision, Machine Learning
Intermediate · Guided Project · Less Than 2 Hours

Coursera
Skills you'll gain: Interviewing Skills, Recruitment, Oral Expression, Follow Through, Communication, Business Writing
Beginner · Guided Project · Less Than 2 Hours

Skills you'll gain: Tensorflow, Keras (Neural Network Library), Natural Language Processing, Deep Learning, Data Pipelines
Intermediate · Guided Project · Less Than 2 Hours

Skills you'll gain: Finite Element Methods, Simulation and Simulation Software, Cloud Engineering, Engineering Analysis, Simulations, Mechanical Design, Engineering Design Process, Verification And Validation
Beginner · Guided Project · Less Than 2 Hours
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.‎