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.
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Foundations of Neural Networks Specialization
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
Recommended experience
What you'll learn
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.
Overview
Skills you'll gain
- Artificial Neural Networks
- Natural Language Processing
- Ethical Standards And Conduct
- Image Analysis
- Linear Algebra
- Debugging
- Artificial Intelligence and Machine Learning (AI/ML)
- Data Ethics
- Markov Model
- Computer Vision
- Data-Driven Decision-Making
- Artificial Intelligence
- Deep Learning
- Reinforcement Learning
- Unstructured Data
- Machine Learning Algorithms
- Unsupervised Learning
- Machine Learning
- Applied Machine Learning
Tools you'll learn
What’s included
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Specialization - 3 course series
What you'll learn
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.
Skills you'll gain
What you'll learn
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.
Skills you'll gain
What you'll learn
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.
Skills you'll gain
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Frequently asked questions
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.
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Financial aid available,