Content: Classification, Sigmoid function, Decision Boundary, Cost function, Gradient descent, Overfitting, Regularisation
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Updated
May 6, 2024 - Jupyter Notebook
Content: Classification, Sigmoid function, Decision Boundary, Cost function, Gradient descent, Overfitting, Regularisation
Make use of PyTorch's custom modules to define a network architecture and train a model. Investigate how to improve a model's performance and deploy your model for wider use.
Brief study on Underfitting and Overfitting in Machine Learning
Overfitting is often caused by using a model with too many parameters or if the model is too powerful for the given dataset. On the other hand, underfitting is often caused by the model with too few parameters or by using a model that is not powerful enough for the given dataset. In this we are discussing about that.
In this repository you will learn how to handle overfitting with the help of Lasso and Ridge Regression regularizations, also working mechanism of those while using useful charts.
Recognize underfitting and overfitting, implement bagging and boosting, and build a stacked ensemble model using a number of classifiers.
Pursued an Introductory Machine Learning course in Python on Kaggle in my free time, where I practiced on a dataset and built a small model on Kaggle.
Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.
Supervised Learning - Regression Algorithm
IMP KEYS OF ML MODEL
A visual example of the concepts of under and overfitting in supervised machine learning using U.S. state border data.
Xinshao Wang, Ex-Postdoc and Ex-Visit Scholar@University of Oxford, Ex-Senior Researcher@ZenithAI
A series of documented Jupyter notebooks implementing polynomial regression models and model performance analysis
Adding noise as regularization method to reduce overffiting in neural networks
Machine Learning Course [ECE 501] - Spring 2023 - University of Tehran - Dr. A. Dehaqani, Dr. Tavassolipour
Fraud detection over twitter feed data
Python version of Andrew Ng's Machine Learning Course.
Dropout in Deep Learning
This repository is a related to all about Machine Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python), Feature Selection technique in python etc. Follow Coursesteach for more content
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