Faisalsardar1
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34 items
machine learning course
stanford machine learning course (half course + summary)
- Package deal
- • 16 items •
- Supervised learning setup • Class notes
- Dataset split; Exponential family. Generalized Linear Models. • Summary
- Linear Algebra Review • Class notes
- Linear Algebra • Presentation
- Gaussian discriminant analysis. Naive Bayes.Laplace Smoothing. • Class notes
- And more ….
stanford machine learning course (half course + summary)
Python/Numpy Tutorial.
Text editor/IDE options.. (don’t settle with notepad) 
• PyCharm (IDE) 
• Visual Studio Code (IDE) 
• Sublime Text (IDE) 
• Atom 
• Notepad ++/gedit 
• Vim (for Linux)
- Package deal
- Presentation
- • 39 pages •
Text editor/IDE options.. (don’t settle with notepad) 
• PyCharm (IDE) 
• Visual Studio Code (IDE) 
• Sublime Text (IDE) 
• Atom 
• Notepad ++/gedit 
• Vim (for Linux)
Neural Networks
Deep Learning 
Supervised learning with non linear models 
Logistic Regression 
Neural Networks 
computational power 
data available 
algorithms 
Propagation equation
- Package deal
- Summary
- • 6 pages •
Deep Learning 
Supervised learning with non linear models 
Logistic Regression 
Neural Networks 
computational power 
data available 
algorithms 
Propagation equation
Neural Networks
Deep Learning 
Supervised Learning with Non-linear Models 
Neural Networks 
Backpropagation 
Vectorization Over Training Examples
- Package deal
- Class notes
- • 21 pages •
Deep Learning 
Supervised Learning with Non-linear Models 
Neural Networks 
Backpropagation 
Vectorization Over Training Examples
Kernels, SVM.
summary of Kernel Methods 
SVMs
- Package deal
- Summary
- • 8 pages •
summary of Kernel Methods 
SVMs
Kernel Methods
Kernels. SVM.
- Package deal
- Class notes
- • 30 pages •
More on Multivariate Gaussians
1 Definition 
2 Gaussian facts 
3 Closure properties 
4 Summary 
5 Exercise
- Package deal
- Class notes
- • 11 pages •
1 Definition 
2 Gaussian facts 
3 Closure properties 
4 Summary 
5 Exercise
Probability Theory Review
Probability theory is the study of uncertainty. Through this class, we will be relying on concepts 
from probability theory for deriving machine learning algorithms. These notes attempt to cover the 
basics of probability theory at a level appropriate for CS 229. The mathematical theory of probability 
is very sophisticated, and delves into a branch of analysis known as measure theory. In these notes, 
we provide a basic treatment of probability that does not address these finer details. 
1 Elem...
- Package deal
- Class notes
- • 12 pages •
Probability theory is the study of uncertainty. Through this class, we will be relying on concepts 
from probability theory for deriving machine learning algorithms. These notes attempt to cover the 
basics of probability theory at a level appropriate for CS 229. The mathematical theory of probability 
is very sophisticated, and delves into a branch of analysis known as measure theory. In these notes, 
we provide a basic treatment of probability that does not address these finer details. 
1 Elem...
Naive Bayes, Laplace Smoothing summary
Outline 
Naive Bayes 
Laplacesmoothing 
Event Models 
Kernel Methods
- Package deal
- Summary
- • 7 pages •
Outline 
Naive Bayes 
Laplacesmoothing 
Event Models 
Kernel Methods
Gaussian discriminant analysis. Naive Bayes.
Gaussian discriminant analysis & it is model 
Naive Bayes.
- Package deal
- Summary
- • 6 pages •
Gaussian discriminant analysis & it is model 
Naive Bayes.