| Summary: | If you know the basics of Python and you want to know deep learning, this course is designed for you. You'll learn the theory behind this branch of artificial intelligence and machine learning, as well as the practical skills of building neural networks to create deep learning models for prediction and for automating and simplifying tasks. Once you've digested the fundamentals, we'll walk you through a project: implementing an artificial neural network in Python to create a deep learning model. Step by step, you'll see how to work with datasets and build each layer of the network. By the end of the course, you will be familiar with the fundamental neural network architectures, including recurrent neural networks (RNNs) and convolutional neural networks (CNNs)) networks, and you will be able to build your own DL neural networks using Python, Keras, and Tensorflow. In short, you will be ready to build models and create programs that take data input and automate feature extraction, simplifying real-world tasks for humans. This video course stands out from the hundreds of machine learning resources available on the internet because it filters out the fluff and unnecessary information and focuses on the essentials you need to get started on your deep learning journey. Consider this a fundamentals course that suits both beginners and more advanced deep learning practitioners who are looking to refresh or fill in the gaps in their knowledge
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