What Is Programming? And How To Get Started
January 28, 2025
Article
Master Neural Networks for AI and Machine Learning. Gain hands-on experience with neural networks, advanced techniques, and ethical AI practices to solve real-world challenges in machine learning and AI applications.
Instructor: Zerotti Woods
Included with
Recommended experience
Intermediate level
A basic understanding of programming in Python, along with foundational knowledge of machine learning and mathematics is recommended.
Recommended experience
Intermediate level
A basic understanding of programming in Python, along with foundational knowledge of machine learning and mathematics is recommended.
Understand the mathematical foundations of neural networks, including deep learning optimization, regularization, and ethical considerations in AI.
Gain hands-on experience in implementing and analyzing various neural network architectures, such as CNNs, RNNs, and GANs, using Python.
Explore topics like probabilistic models, model evaluation, and bias mitigation, preparing for real-world applications in AI and deep learning.
Add to your LinkedIn profile
December 2024
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
This Specialization is intended for post-graduate students seeking to develop advanced skills in neural networks and deep learning. Through three courses, you will cover the mathematical theory behind neural networks, including feed-forward, convolutional, and recurrent architectures, as well as deep learning optimization, regularization techniques, unsupervised learning, and generative adversarial networks. You will also explore the ethical issues associated with neural network applications. By the end of the specialization, you will gain hands-on experience in formulating and implementing algorithms using Python, allowing you to apply theoretical concepts to real-world data. This specialization prepares you to design, analyze, and deploy neural networks for practical applications in fields such as AI, machine learning, and data science, and equips you with the tools to address ethical considerations in AI systems. As you progress, you'll be able to independently implement and evaluate a variety of neural network models, setting a strong foundation for a career in AI research or development.
Applied Learning Project
The hands-on assignments in this specialization integrate theoretical and practical expertise to design, train, and evaluate neural network models for real-world challenges. Using Python and frameworks like TensorFlow or PyTorch, students will implement feed-forward, convolutional, and recurrent networks, alongside advanced techniques such as generative adversarial networks and unsupervised learning. Focus areas include optimization, regularization, and ethical considerations like bias and privacy. Deliverables include a functional model addressing a defined problem, a critical evaluation of ethical impacts, and thorough documentation, preparing participants for roles in AI research and development.
Understand the foundational mathematics and key concepts driving neural networks and machine learning.
Analyze and apply machine learning algorithms, optimization methods, and loss functions to train and evaluate models effectively.
Explore the design and structure of feedforward neural networks, using gradient descent to optimize and train deep models.
Investigate convolutional neural networks, their elements, and how they apply to real-world problems like image processing and computer vision.
Analyze and implement Recurrent Neural Networks (RNNs) to process sequence data and solve tasks like time series prediction and language modeling.
Explore autoencoders for data compression, feature extraction, and anomaly detection, along with their applications in diverse fields.
Develop and evaluate generative models, such as GANs, understanding their mathematical foundations and deployment challenges.
Apply reinforcement learning techniques using Markov Chains and deep neural networks to tackle complex decision-making problems.
Learners will gain hands-on experience training and debugging deep learning models while considering deployment challenges and best practices.
Students will understand and evaluate ethical concerns in AI, including bias, fairness, and the societal impact of deploying neural networks.
Learners will explore how to integrate structured probabilistic models with deep learning, reducing uncertainty and improving model decision-making.
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Earn a degree from world-class universities - 100% online
Upskill your employees to excel in the digital economy
The specialization is designed to be completed at your own pace, but on average, it is expected to take approximately 3 months to finish if you dedicate around 5 hours per week. However, as it is self-paced, you have the flexibility to adjust your learning schedule based on your availability and progress.
You are encouraged to take the courses in the recommended sequence to ensure a smoother learning experience, as each course builds on the knowledge and skills developed in the previous ones. However, you are not required to follow a specific order, and you can take the courses in the order that best suits your needs and prior knowledge.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
These cookies are necessary for the website to function and cannot be switched off in our systems. They are usually only set in response to actions made by you which amount to a request for services, such as setting your privacy preferences, logging in or filling in forms. You can set your browser to block or alert you about these cookies, but some parts of the site will not then work.
These cookies may be set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites. They are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising.
These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance.
These cookies enable the website to provide enhanced functionality and personalization. They may be set by us or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly.