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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis
In this video, we will cover supervised PCA and Fishers linear discriminant analysis. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Versus SVD
In this video, we will cover PCA versus SVD. 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 Projects) A to Z".In...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Properties
In this video, we will cover PCA properties. 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 Projects) A to Z".In...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Max Variance Formulation
In this video, we will cover PCA Max Variance Formulation. 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...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Implementation
In this video, we will cover PCA implementation. 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 Projects) A to...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA For Small Sample Size Problems(DualPCA)
In this video, we will cover PCA for small sample size problems (DualPCA). This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Derivation
In this video, we will cover PCA derivation. 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 Projects) A to Z".In...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Kernel PCA
In this video, we will cover Kernel PCA. 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 Projects) A to Z".In...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Engineering: Image Features
In this video, we will cover image features. 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 Projects) A to Z".In...
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Data Science and Machine Learning (Theory and Projects) A to Z - Feature Engineering: Derived Features
In this video, we will cover derived features. 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 Projects) A to...
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Data Science and Machine Learning (Theory and Projects) A to Z - Deep Neural Network Architecture: Nonvectorized Implementations of Conv2d and Pool2d
In this video, we will cover nonvectorized implementations of Conv2d and Pool2d. This clip is from the chapter "Deep learning: Convolutional Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Why Activation Function Is Required
In this video, we will understand why activation function is required. This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...
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Data Science and Machine Learning (Theory and Projects) A to Z - RNN Implementation: Language Modelling Next Word Prediction Python 4
In this video, we will cover language modelling next word prediction Python 4. This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" 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 - RNN Architecture: Weight Sharing
In this video, we will cover weight sharing. This clip is from the chapter "Deep learning: Recurrent Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will...
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Data Science and Machine Learning (Theory and Projects) A to Z - Python for Data Science: NumPy Pandas and Matplotlib (Part 3)
In this video, we will cover NumPy Pandas and Matplotlib (part 3).
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Data Science and Machine Learning (Theory and Projects) A to Z - Python for Data Science: NumPy Pandas and Matplotlib (Part 2)
In this video, we will cover NumPy Pandas and Matplotlib (part 2).
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Data Science and Machine Learning (Theory and Projects) A to Z - Optional Estimation: Ridge Regression
In this video, we will cover ridge regression.
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Data Science and Machine Learning (Theory and Projects) A to Z - NumPy for Numerical Data Processing: NumPy BroadCasting and Concatenation
In this video, we will cover NumPy BroadCasting and concatenation. 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 Projects)...
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Data Science and Machine Learning (Theory and Projects) A to Z - Neural Style Transfer: Problem Setup
In this video, we will cover problem setup. This clip is from the chapter "Deep learning: Convolutional Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will...
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Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Multivariate Gaussian
In this video, we will cover multivariate Gaussian.
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Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Classification
In this video, we will cover classification. This clip is from the chapter "Deep learning: Artificial Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will...
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Data Science and Machine Learning (Theory and Projects) A to Z - Image Processing: RGB Images
In this video, we will cover RGB images. This clip is from the chapter "Deep learning: Convolutional Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we will...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Image Processing: Image Formation
In this video, we will cover image formation. This clip is from the chapter "Deep learning: Convolutional Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Image Processing: Grayscale Images
In this video, we will cover grayscale images. This clip is from the chapter "Deep learning: Convolutional Neural Networks with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this section, we...