This video demonstrates how to design a recurrent neural network or RNN. This clip is from the chapter "Recurrent Neural Networks" of the series "Fundamentals in Neural Networks".This section explains NLP, we will start with recurrent...

This video demonstrates convolutional operations in 1-dimension. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you...

This video explains image data in CNN (Convolutional Neural Network). This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where...

This video explains the cross-entropy function, which is designed under the assumption that the variable you are trying to predict is binary. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in...

This video explains the course outline and what the course has to offer. This clip is from the chapter "Welcome" of the series "Fundamentals in Neural Networks".This section introduces you to the course and the course outline.

The Convolution layer (CONV) uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. Its hyperparameters include the filter size and stride. The resulting output is called a feature...

Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. BRNNs are especially useful when the context of the input is needed. For example, in handwriting recognition, the...

This video explains backward propagation, which is defined by the optimization problem called the gradient descent algorithm. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This...

This video explains the two most famous and common procedures—cross-validation and Bootstrap.
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This clip is from the chapter "Lectures" of the series "Fundamentals of Machine Learning".This section explains the basics of...

In this final lab, you will learn about ROC-AUC (Receiver Operating Characteristic Curve-Area Under Curve).
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This clip is from the chapter "Labs" of the series "Fundamentals of Machine Learning".This section explains the...

This video explains a lab session on random forests and a review of decision trees.
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This clip is from the chapter "Labs" of the series "Fundamentals of Machine Learning".This section explains the various lab exercises on...

This video explains a lab session on neural networks and Multilayer Perceptron (MLP) models.
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This clip is from the chapter "Labs" of the series "Fundamentals of Machine Learning".This section explains the various lab...

This video explains model selection and regularization.
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This clip is from the chapter "Lectures" of the series "Fundamentals of Machine Learning".This section explains the basics of statistical learning, sampling, and...

This video explains a brief history of where machine learning started.
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This clip is from the chapter "Lectures" of the series "Fundamentals of Machine Learning".This section explains the basics of statistical learning,...

This video introduces you to deep learning, artificial neural networks, recurrent neural networks, and more.
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This clip is from the chapter "Lectures" of the series "Fundamentals of Machine Learning".This section explains the...

This video continues the lab from neural networks and will have a look at convolutional neural networks.
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This clip is from the chapter "Labs" of the series "Fundamentals of Machine Learning".This section explains the various...

In this video, we will cover derived features.
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This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...

In this video, we will cover an introduction to Scikit-Learn.
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This clip is from the chapter "Basics for Data Science: Python for Data Science and Data Analysis" of the series "Data Science and Machine Learning (Theory and...

In this video, we will cover an introduction to overfitting.
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This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...

In this video, we will cover Scikit-Learn for SVM and random forests.
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This clip is from the chapter "Basics for Data Science: Python for Data Science and Data Analysis" of the series "Data Science and Machine Learning (Theory...

The aim of this video is to explore more statistical functions for forecasting.
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This clip is from the chapter "Excel 2019 Advanced: Statistical Functions" of the series "The Complete Excel Guide: Beginners to Advanced".In...

This video explains OLS assumptions in detail.
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This clip is from the chapter "Assumptions for Linear Regression Analysis" of the series "Statistics for Data Science and Business Analysis".This section explains OLS...

In this video, the first assumption of OLS, linearity, is explained.
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This clip is from the chapter "Assumptions for Linear Regression Analysis" of the series "Statistics for Data Science and Business Analysis".This section...

Interacting with a simple linear regression activity