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.

Multiple educators
Skills you'll gain: Unsupervised Learning, Supervised Learning, Transfer Learning, Machine Learning, Jupyter, Applied Machine Learning, Data Ethics, Decision Tree Learning, Model Evaluation, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Data Preprocessing
Beginner · Specialization · 1 - 3 Months

Illinois Tech
Skills you'll gain: Recurrent Neural Networks (RNNs), Deep Learning, Generative AI, Convolutional Neural Networks, Transfer Learning, Image Analysis, Artificial Neural Networks, Keras (Neural Network Library), Generative Adversarial Networks (GANs), Artificial Intelligence and Machine Learning (AI/ML), PyTorch (Machine Learning Library), Network Architecture, Computer Vision, Natural Language Processing, Model Deployment, Model Evaluation
Build toward a degree
Beginner · Course · 1 - 3 Months

Skills you'll gain: Model Evaluation, Convolutional Neural Networks, Data Preprocessing, Keras (Neural Network Library), Image Analysis, Deep Learning, Tensorflow, Computer Vision, Artificial Neural Networks, Recurrent Neural Networks (RNNs), Data Manipulation, Data Transformation, Financial Forecasting, Data Visualization, Time Series Analysis and Forecasting, Exploratory Data Analysis, Python Programming, Customer Analysis, Applied Machine Learning, Cloud Computing
Beginner · Specialization · 1 - 3 Months

MathWorks
Skills you'll gain: Model Evaluation, Computer Vision, Model Deployment, Anomaly Detection, Convolutional Neural Networks, Image Analysis, Transfer Learning, Matlab, Deep Learning, Artificial Neural Networks, Applied Machine Learning, PyTorch (Machine Learning Library), Data Preprocessing, Classification Algorithms, Data Visualization, Data Synthesis, Performance Tuning, Data Analysis
Beginner · Specialization · 1 - 3 Months

Skills you'll gain: Responsible AI, Generative AI, Natural Language Processing, Robotics, Business Logic, Risk Mitigation
Beginner · Course · 1 - 4 Weeks

Imperial College London
Skills you'll gain: Linear Algebra, Dimensionality Reduction, NumPy, Regression Analysis, Calculus, Applied Mathematics, Data Preprocessing, Unsupervised Learning, Feature Engineering, Machine Learning Algorithms, Jupyter, Advanced Mathematics, Statistics, Artificial Neural Networks, Algorithms, Mathematical Modeling, Python Programming, Derivatives
Beginner · Specialization · 3 - 6 Months

Skills you'll gain: Recurrent Neural Networks (RNNs), Artificial Neural Networks, Deep Learning, Matplotlib, Convolutional Neural Networks, Linear Algebra, Image Analysis, Data Visualization, NumPy, Machine Learning Algorithms, Keras (Neural Network Library), Pandas (Python Package), Seaborn, Artificial Intelligence and Machine Learning (AI/ML), Data Science, Applied Machine Learning, Tensorflow, Data Analysis, Artificial Intelligence, Machine Learning
Beginner · Specialization · 3 - 6 Months

MathWorks
Skills you'll gain: Computer Vision, Model Evaluation, Image Analysis, Convolutional Neural Networks, Deep Learning, Matlab, Data Preprocessing, Data Visualization, Transfer Learning, Applied Machine Learning, Data Analysis
Beginner · Course · 1 - 4 Weeks

DeepLearning.AI
Skills you'll gain: AI Product Strategy, Responsible AI, Data Ethics, AI Enablement, Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Artificial Neural Networks
Beginner · Course · 1 - 4 Weeks

Skills you'll gain: Prompt Engineering, Exploratory Data Analysis, Prompt Patterns, LangChain, Large Language Modeling, Retrieval-Augmented Generation, Model Evaluation, Unsupervised Learning, Generative Model Architectures, PyTorch (Machine Learning Library), ChatGPT, Generative AI, Restful API, LLM Application, Keras (Neural Network Library), Data Transformation, Supervised Learning, Responsible AI, Vector Databases, Data Import/Export
Beginner · Professional Certificate · 3 - 6 Months

Skills you'll gain: Model Evaluation, Computer Vision, Convolutional Neural Networks, Image Analysis, Transfer Learning, Matlab, Artificial Neural Networks, Deep Learning, Applied Machine Learning, Classification Algorithms, Performance Tuning
Beginner · Course · 1 - 4 Weeks

Simplilearn
Skills you'll gain: Reinforcement Learning, Artificial Intelligence, Tensorflow, Artificial Neural Networks, Deep Learning, Machine Learning, Applied Machine Learning, Data Ethics, Supervised Learning, Responsible AI, Unsupervised Learning, Natural Language Processing, Computer Programming
Beginner · Course · 1 - 4 Weeks
Deep learning is a subset of machine learning that utilizes neural networks with many layers (hence the term 'deep') to analyze various forms of data. It is important because it enables computers to perform tasks that typically require human intelligence, such as image recognition, natural language processing, and decision-making. As technology continues to evolve, deep learning is becoming increasingly integral in various industries, driving innovations in automation, healthcare, finance, and more.‎
Pursuing a career in deep learning can open doors to various job opportunities. Some common roles include deep learning engineer, data scientist, machine learning engineer, AI researcher, and computer vision engineer. These positions often involve designing and implementing deep learning models, analyzing data, and developing algorithms that can learn from and make predictions based on data.‎
To succeed in deep learning, you should develop a strong foundation in several key skills. These include programming languages such as Python, understanding of machine learning concepts, proficiency in using deep learning frameworks like TensorFlow and PyTorch, and knowledge of mathematics, particularly linear algebra and calculus. Familiarity with data preprocessing and model evaluation techniques is also beneficial.‎
There are numerous online courses available for those interested in deep learning. Some of the best options include the Deep Learning Specialization and the IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate. These courses provide comprehensive training and hands-on experience in deep learning techniques and applications.‎
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.‎
To learn deep learning effectively, start by building a strong foundation in programming and mathematics. Enroll in introductory courses to understand the basics of machine learning and neural networks. Gradually progress to more advanced topics and practical applications by working on projects. Engaging with online communities and forums can also provide support and enhance your learning experience.‎
Deep learning courses typically cover a range of topics, including neural network architectures, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing, and reinforcement learning. Additionally, courses may explore practical applications in fields such as computer vision, healthcare, and finance, providing learners with a well-rounded understanding of how deep learning can be applied in real-world scenarios.‎
For training and upskilling employees in deep learning, specialized courses such as the AI ML with Deep Learning and Supervised Models Specialization and the Deep Learning for Healthcare Specialization can be particularly beneficial. These programs focus on practical skills and applications, making them suitable for workforce development.‎