Skills you'll gain: Computer Programming, Python Programming, Algorithms, Machine Learning, Theoretical Computer Science, Applied Machine Learning, Computer Science, Data Science, General Statistics, Probability & Statistics, Probability Distribution, Artificial Neural Networks, Linear Algebra, Mathematics, Application Development, Applied Mathematics, Calculus, Computational Logic, Differential Equations, Geometry, Machine Learning Algorithms, Software Engineering, Communication, Computer Graphic Techniques, Computer Graphics, Computer Networking, Computer Vision, Decision Making, Deep Learning, Entrepreneurship, Estimation, Feature Engineering, Graph Theory, Leadership and Management, Machine Learning Software, Mathematical Theory & Analysis, Network Model, Planning, Statistical Programming, Supply Chain and Logistics
Advanced · Specialization · 3-6 Months
Skills you'll gain: Machine Learning, Machine Learning Algorithms, Data Science, Statistical Machine Learning, Linear Algebra, Statistical Analysis, Data Mining, Regression, Applied Machine Learning, Feature Engineering, General Statistics, Natural Language Processing, Python Programming, Machine Learning Software, Statistical Tests, Data Analysis, Dimensionality Reduction, Statistical Programming, Deep Learning, Basic Descriptive Statistics, Probability & Statistics, Computer Vision, Statistical Visualization, Estimation, Probability Distribution, Correlation And Dependence, Forecasting, Big Data, Data Management, Algorithms, Bayesian Statistics, Business Analysis, Business Psychology, Computational Logic, Computational Thinking, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Structures, Distributed Computing Architecture, Entrepreneurship, Exploratory Data Analysis, Markov Model, Mathematical Theory & Analysis, Mathematics, Theoretical Computer Science
Intermediate · Specialization · 3-6 Months
Skills you'll gain: Machine Learning, Statistical Machine Learning, Machine Learning Algorithms, Probability & Statistics, Python Programming, Statistical Programming, Regression, Deep Learning, Data Analysis, Artificial Neural Networks, Applied Machine Learning, Correlation And Dependence, Statistical Analysis, Statistical Tests, Exploratory Data Analysis, Algorithms, Reinforcement Learning, Theoretical Computer Science, Basic Descriptive Statistics, Data Mining, Feature Engineering, General Statistics, Natural Language Processing, Computer Programming, Data Management, Data Structures, Dimensionality Reduction
Intermediate · Specialization · 3-6 Months
Skills you'll gain: Mathematics, Algebra, Linear Algebra, Machine Learning, Python Programming, Probability & Statistics, General Statistics, Calculus, Computer Programming, Applied Mathematics, Mathematical Theory & Analysis, Statistical Programming, Algorithms, Dimensionality Reduction, Regression, Theoretical Computer Science, Basic Descriptive Statistics, Data Analysis, Probability Distribution, Artificial Neural Networks, Computer Graphic Techniques, Computer Graphics, Computer Networking, Deep Learning, Differential Equations, Experiment, Machine Learning Algorithms, Network Model
Beginner · Specialization · 3-6 Months
Skills you'll gain: Machine Learning, Algorithms, Data Analysis, Deep Learning, Machine Learning Algorithms, Probability & Statistics, Regression, Reinforcement Learning, Theoretical Computer Science
Beginner · Course · 1-4 Weeks
Skills you'll gain: Applied Machine Learning, Artificial Neural Networks, Computer Vision, Deep Learning, Machine Learning, Python Programming, Statistical Machine Learning, Statistical Programming, Computer Programming, HTML and CSS, Web Development
Intermediate · Guided Project · Less Than 2 Hours
Skills you'll gain: Statistical Programming, Data Analysis, Computer Programming, Python Programming, Data Management, Databases, SQL, Machine Learning, Probability & Statistics, Big Data, Computer Programming Tools, Data Visualization, Regression, General Statistics, Mathematics, Algorithms, Machine Learning Algorithms, Theoretical Computer Science, R Programming, Econometrics, Data Model, Data Mining, Web Development
Beginner · Professional Certificate · 3-6 Months
Skills you'll gain: Machine Learning, Cloud Computing, Google Cloud Platform, Computer Programming, Cloud Platforms, Statistical Programming, Python Programming, Data Management, Applied Machine Learning, Feature Engineering, Tensorflow, Deep Learning, DevOps, Entrepreneurship, Probability & Statistics, Data Analysis, Big Data, Artificial Neural Networks, Business Psychology, Data Visualization, Exploratory Data Analysis, Regression, SQL, Statistical Visualization, Theoretical Computer Science, Data Science, Kubernetes, Apache, Basic Descriptive Statistics, Bayesian Statistics, Computational Thinking, Computer Architecture, Computer Networking, Data Model, Data Structures, Extract, Transform, Load, General Statistics, Hardware Design, Machine Learning Algorithms, Machine Learning Software, Network Security, Performance Management, Security Engineering, Security Strategy, Statistical Machine Learning, Strategy and Operations, Algorithms, Business Analysis, Cloud Applications, Cloud Infrastructure, Cloud Storage, Data Analysis Software, Data Architecture, Data Warehousing, Database Application, Databases, Dimensionality Reduction, Distributed Computing Architecture, Full-Stack Web Development, Information Technology, Natural Language Processing, Statistical Analysis, Web Development
Intermediate · Professional Certificate · 3-6 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.
A familiarity with the capabilities and development process for deep learning applications can be an asset in a growing number of careers. For example, the use of deep learning is being explored in healthcare for automatic reading of radiology images, as well as searching for patterns in genes and pharmaceutical interactions that can aid in the discovery of new types of medicines. In many fields, even a basic understanding of deep learning can help professionals identify new potential applications of this powerful technology.
Those with a deeper expertise in deep learning may become computer research scientists in this field, responsible for inventing new algorithms and finding new applications for these techniques. Given the wide range of uses for deep learning, computer scientists in this field are in high demand for jobs at private companies as well as government agencies and research universities. According to the Bureau of Labor Statistics, computer research scientists earned a median annual salary of $122,840 as of 2019, and these jobs are expected to grow much faster than average.
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.
Deep learning may be right for you if you want to break into AI. The specialization may benefit you if you are a machine learning researcher or practitioner who is seeking to learn the next generation of machine learning, and you want to develop practical skills in the popular deep learning framework TensorFlow. Deep learning is one of the most highly sought-after skills in tech, and mastering it may lead you to many opportunities in the field of AI. It may also benefit you if you want to learn how to build neural networks and how to lead successful machine learning projects, and if you have a passion for learning about convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and how to master concepts in Python and TensorFlow.