Python Machine Learning (Part 4)
In Part 4 of the Python Machine Learning guided lab from Next.Tech, you will build your knowledge by learning more about neural networks and deep learning with Python and the Tensor Flow Library in order to practice more real world applications of deep machine learning algorithims.
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Unlock modern machine learning techniques with Python by using the latest cutting-edge open source Python libraries.
This is Part 4 of the Python Machine Learning series. In this course, we will delve into the exciting subfields of machine learning — neural networks and deep learning. Using both vanilla Python and the TensorFlow library, we will explore some real world applications of deep learning algorithms.
Objectives
Understanding the architecture of neural networks
Learning how to implement neural networks both from scratch and with TensorFlow.
Understanding activation functions, tensors, and computation graphs
Building multilayer perceptrons, regression models, deep convolutional neural networks, recurrent neural networks, and more.
Prerequisites
Python programming Understanding of basic linear algebra concepts Completion of Machine Learning Part 1, 2, 3 highly recommended Knowledge of introductory calculus (preferable, but not necessary)
Complete this entire course to earn a Python Machine Learning (Part 4) Certificate of Completion
