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Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Regression
In this video, we will cover regression.
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Hands-on Machine Learning Project Using Scikit-Learn: Pipeline in Scikit-Learn for Machine Learning Project
In this video, we will cover Pipeline in Scikit-Learn for machine learning project. This clip is from the chapter "Machine Learning: Machine Learning Crash Course" of the series "Data Science and Machine Learning (Theory and Projects) A...
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Data Science and Machine Learning (Theory and Projects) A to Z - Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 2
In this video, we will cover linear regression from scratch- part 2. 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 this...
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Data Science and Machine Learning (Theory and Projects) A to Z - OPTIONAL Section- Mathematics Wrap-Up: Mathematical Wrap-Up on Machine Learning
In this video, we will cover mathematical wrap-up on machine learning. 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 this...
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Practical Data Science using Python - Regression Problems
This video explains regression problems. This clip is from the chapter "Machine Learning" of the series "Practical Data Science Using Python".This section explains machine learning.
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Python In Practice - 15 Projects to Master Python - Asking the Model to Make Predictions - Machine Learning with Python
This video talks about asking the model to make predictions. This clip is from the chapter "Machine Learning with Python" of the series "Python in Practice - 15 Projects to Master Python".This section focuses on machine learning with...
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Fundamentals of Neural Networks - Welcome Message
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...
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Fundamentals of Neural Networks - VGG16
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...
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Fundamentals of Neural Networks - Purpose of Neural Networks
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...
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Fundamentals of Neural Networks - Padding
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...
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Fundamentals of Neural Networks - Lab 4 - Functional API
This video demonstrates functional API versus sequential API. 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...
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Fundamentals of Neural Networks - Lab 3 - Introduction to Neural Network
This video demonstrates how to use Keras TensorFlow as API to essentially design and craft the neural network architecture. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in Neural Networks".This...
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Fundamentals of Neural Networks - Lab 2 - Introduction to CNN
This video demonstrates the architecture and how to carry out the code using TensorFlow in collab and building a convolutional neural network. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in...
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Fundamentals of Neural Networks - Lab 1 - Introduction to Convolutional 1-Dimensional
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...
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Fundamentals of Neural Networks - Image Data
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...
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Fundamentals of Neural Networks - Cross-Entropy Loss Function
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...
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Fundamentals of Neural Networks - Course Outline
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.
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Fundamentals of Neural Networks - Convolutional Operation
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...
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Fundamentals of Neural Networks - Backward Propagation
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...
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Fundamentals of Neural Networks - Welcome to RNN
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...
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Fundamentals of Neural Networks - Lab 2 - Sequence to Sequence Stock Candlestick Forecast
This video demonstrates sequence-to-sequence stock candlestick forecast. 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...
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Fundamentals of Neural Networks - Lab 1 - RNN in Text Classification
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...
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Fundamentals of Neural Networks - Bi-Directional RNN
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...
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Fundamentals of Machine Learning - Sampling and Bootstrap
This video explains the two most famous and common procedures—cross-validation and Bootstrap. This clip is from the chapter "Lectures" of the series "Fundamentals of Machine Learning".This section explains the basics of statistical...