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“…What You Will Learn Get a solid foundation in scientific computing using Python Master common tasks related to SciPy and associated libraries such as NumPy, pandas, and matplotlib Perform mathematical operations such as linear algebra and work with the statistical and probability functions in SciPy Master advanced computing such as Discrete Fourier Transform and K-means with the SciPy Stack Implement data wrangling tasks efficiently using pandas Visualize your data through various graphs and charts using matplotlib In Detail With the SciPy Stack, you get the power to effectively process, manipulate, and visualize your data using the popular Python language. …”

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“…about the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. …”

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“…In Detail An Arduino might be a tiny computer but it can be used as the foundation for a huge range of projects. In this course, we'll show you how just some of the projects that are possible with an Arduino. …”

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“…What You Will Learn Understand what machine learning is Build linear models with TensorFlow 2 Learn how to build deep neural networks with TensorFlow 2 Learn how to perform image classification and regression with ANN Learn loss functions such as mean-squared error and cross-entropy loss Learn about stochastic gradient descent, momentum, and Adam optimization Audience This course is designed for anyone interested in deep learning and machine learning, anyone who wants to implement deep neural networks in TensorFlow 2, or anyone interested in building a foundation for convolutional neural networks, recurrent neural networks, LSTMs (Long Short Term Memory), and transformers. …”

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