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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

Machine Learning Algorithms

Deep Learning

Computer Vision

Artificial Neural Networks

Probability & Statistics

Artificial Intelligence

Image Analysis

Performance Tuning

Linear Algebra

Machine Learning

There are 6 modules in this course

You’ll also delve into the architecture of feedforward neural networks and the innovative techniques used to prevent overfitting, such as dropout and regularization. The course uniquely emphasizes Convolutional Neural Networks (CNNs), highlighting their applications in fields like computer vision and image processing. Real-world examples and research insights will help you stay current with advancements in neural networks while preparing you to propose innovative solutions for emerging challenges. This course offers the tools and knowledge to advance your expertise in algorithms and machine learning methodologies.

Overview and Foundations

Learning in Neural Networks

Feedforward Neural Networks

Regularization in Neural Networks

Convolutional Neural Networks