This video explains some basic notations in statistical learning, such as Xij.
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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...

This video explains what we mean by Tensor and Matrix. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you will start...

This video explains statistical machine learning, where you will start with the linear regression model. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains...

This video demonstrates a deeper CNN, where you will build a much bigger number of trainable parameters. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains...

This video demonstrates some basic operations in TensorFlow such as objects and we will apply some mathematical operations to the Tensor objects.
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This clip is from the chapter "Artificial Neural Networks" of the series...

This video demonstrates some of the basic commands in Python specifically the print statement, data structures, variables, and how to define a function. This clip is from the chapter "Artificial Neural Networks" of the series...

This video explains forward propagation and will dive deeper into the architecture of neural networks. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains...

This video explains the role of the activation function, which is an interesting phenomenon in the design of neural networks. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This...

This part of the video explains decision tree.
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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 Bootstrap as...

This video explains a lab session on Ridge regression, which holds a unique position in statistical machine learning.
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This clip is from the chapter "Labs" of the series "Fundamentals of Machine Learning".This section explains...

This video explains a lab session on Eigenfaces using PCA.
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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 linear regression, ridge...

This video explains logistic regression with a little bit of mathematics behind it.
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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 going beyond linearity; specifically, we will look at a couple of interesting examples to improve the linear regression model to see if we can create models that are non-linear.
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This clip is from the...

This video explains a lab session on a decision tree, getting dependencies, and how to create mock data.
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This clip is from the chapter "Labs" of the series "Fundamentals of Machine Learning".This section explains the various...

This video explains classification metrics and will cover terminologies such as accuracy, specificity, sensitivity, and so on.
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This clip is from the chapter "Lectures" of the series "Fundamentals of Machine Learning".This...

A Recurrent neural network is a type of artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks recognize data's sequential characteristics and use patterns to predict the...

This video explains recurrent neural networks and why we want to use 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 explains the need for taking up the course and introduces you to the author. This clip is from the chapter "Welcome" of the series "Fundamentals in Neural Networks".This section introduces you to the course and the course...

This video explains VGG16 which is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper "Very Deep Convolutional Networks for Large-Scale Image Recognition". This clip...

For a convolutional or pooling operation, the stride denotes the number of pixels by which the window moves after each operation. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural...

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. This clip is from the chapter "Convolutional Neural...

This video explains the purpose of neural networks. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This section explains artificial neural networks where you will learn every...

This video explains padding in convolutional neural networks. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains convolutional neural networks where you...

NLP is a tool for structuring data in a way that AI systems can process that deals with language. NLP uses AI to 'read' through a document and extract key information. This clip is from the chapter "Recurrent Neural Networks" of the...