- Видео 8
- Просмотров 2 824
Conner Paine
Добавлен 25 июл 2020
Interested in ML. It's pretty cool.
Implementing a Machine Learning Paper: Building ID3 Decision Trees from Scratch!
In this video, I implement the ID3 Decision Tree algorithm from the influential "Induction of Decision Trees" paper by J.R. Quinlan, published in 1986. This basic version of the algorithm does not include noise or unknown attribute handling. My goal was to replicate the results from the paper and demonstrate the power of ID3 Decision Trees! The repository contains code for the full class as well as code for testing the model on the popular Kaggle "Breast Cancer Dataset."
Paper: Quinlan, J.R. Induction of decision trees. Mach Learn 1, 81-106 (1986). doi.org/10.1007/BF00116251
Link to GitHub repo: github.com/cjpaine109/ml-papers
Thanks for watching!
Paper: Quinlan, J.R. Induction of decision trees. Mach Learn 1, 81-106 (1986). doi.org/10.1007/BF00116251
Link to GitHub repo: github.com/cjpaine109/ml-papers
Thanks for watching!
Просмотров: 1 520
Видео
Machine Learning: Polynomial Regression (from scratch)
Просмотров 815 месяцев назад
In this video, I demonstrate how to build a Polynomial Regression model from scratch, including detailed code for creating a custom Polynomial Regression class. The process and code are straightforward and easy to follow. I appreciate your patience as I debug some minor errors along the way. Thank you for watching! Link to GitHub repo: github.com/cjpaine109/ai-notebooks
Machine Learning: Logistic Regression with MNIST (from scratch)
Просмотров 1015 месяцев назад
In this video, I create a Logistic Regression model from scratch to classify images in the popular MNIST dataset. Thank you for watching! Link to GitHub repo: github.com/cjpaine109/ai-notebooks
Comparing Gradient Descent Algorithms in Python (Implemented from Scratch)
Просмотров 225 месяцев назад
This video concludes my series on Gradient Descent algorithms. In this episode, I demonstrate how to implement each variation from scratch to compute the parameters for another linear model. Thank you for watching! GitHub repository: github.com/cjpaine109/ai-notebooks Special thanks to NeuralNine for the inspiration on visualizing convergence: NeuralNine RUclips Channel
Machine Learning: Mini-Batch Gradient Descent (from scratch)
Просмотров 335 месяцев назад
In this video, we explore how to compute the parameters for a Simple Linear Regression model using Mini-Batch Gradient Descent. In the first part, we generate some random linear data and fit a linear model to this data, providing an overview of how the algorithm works. In the second part, we create another class from scratch to fit a linear model to a popular Kaggle dataset. Thank you for watch...
Machine Learning: Linear Regression with Stochastic Gradient Descent (from scratch)
Просмотров 9535 месяцев назад
In this video, I demonstrate how to compute the parameters for a Simple Linear Regression model using Stochastic Gradient Descent (SGD). I explain the algorithm and create two comparison classes: Batch Gradient Descent (BGD) and SGD. I also fixed a few typos and added helpful comments in the code to improve clarity (in repo). Thanks for watching! Link to repo: github.com/cjpaine109/ai-notebooks
Machine Learning: Linear Regression using Batch Gradient Descent (from scratch)
Просмотров 455 месяцев назад
In this video, I compare two methods for computing the parameters of a linear model: the closed-form solution (The Normal Equation) and Batch Gradient Descent (BGD). While I have another video dedicated to explaining the Normal Equation, this video focuses on BGD. I demonstrate how to build a Linear Regression class from scratch using the Yahoo Finance API and compute the model parameters with ...
Machine Learning: Linear Regression (from scratch)
Просмотров 705 месяцев назад
In this video, I provide a concise explanation of Linear Regression and its underlying principles. I demonstrate how to construct a simple model from scratch to fit some randomly generated data points. Finally, I create a custom class that mimics the functionality of the Linear Regression class from scikit-learn. Code: github.com/cjpaine109/MLAlgorithmsFromScratch
Thank You.
even i didn't understand due my lack of skills and knowledge this is amazing keep it up !!!
cool video, I had to do this for my machine learning class last year. It was a good learning exercise and pretty fun as well
I'm currently studying ML for quant research, this is actually helpful. I subbed, keep it up.
Thanks a lot!!!