anandprems

1_Data_tango_learning

My first preseantation on the topic DATA - Another Mysterious World! in Data Science series

2-Statistics---Part-1-MoCT

3_Statistics_MoCT_Mean

anandprems

Artificial-Neural-Network-in-Python-from-scratch

cnn

Computer-Programming-Python

Embedded-C-Programmings

mitbih_cnn

Dataset: https://www.physionet.org/content/mitdb/1.0.0/

SVM_Classification_Regression

anandprems/mitbih_cnn

Dataset: https://www.physionet.org/content/mitdb/1.0.0/

anandprems/Artificial-Neural-Network-in-Python-from-scratch

anandprems/CPP-Fall-2023-2024

anandprems/Embedded-C-Programmings

anandprems/anandprems

anandprems/cnn

anandprems/SVM_Classification_Regression

anandprems/3_Statistics_MoCT_Mean

anandprems/autoencoders_python

In this repository i will be explaining all about autoencoders, types and its functions

anandprems/BECE101-Basic-Electronics-VIT

In this repository I am uploading the materials used for the subject BECE101 Basic Electronics in particular.

anandprems/Classification---30-model---Pyforest

In this repository, i discussed about by using Pyforest command we can easily retrive around 40 differernt model's Accuracy, balanced accuracy, AUC-ROC, F1 score and time taken values, without spending much time in coding. I am not saying this is the best way to explore but still this is one kind of way to do coding

anandprems/chaf_tech_science_cmc

anandprems/Data-Science-Interview-Question-and-Answers

In this repository i am sharing some important questions related to Data Science, which was shared by ineuron edu company. While preparing for Data Science interview this documents plays important role

anandprems/Data-Science-Interview-Questions-Answers

Curated list of data science interview questions and answers

anandprems/Data_Science_2024

anandprems/eda_1

Some basic idea about Exploratory Data Analysis steps

anandprems/git_practice_session

Together, Git and GitHub form a powerful combination for modern software development. Git provides the robust version control necessary for managing changes, while GitHub offers a collaborative platform with additional tools and features to streamline the development process.

anandprems/hgb_classifier

anandprems/histogram_gradient_boosting_classifier

So here, we are using histogram based gradient boosting algorithm for PTB dataset which is acquired from Physionet website after doing some preprocessing. Dataset can be downloaded from, https://www.kaggle.com/shayanfazeli/heartbeat

anandprems/LightGBM_ML_Algorithm

Are you tired of slow and inaccurate machine learning algorithms? Do you struggle with large datasets and high-dimensional features? Look no further than LightGBM, a powerful and efficient machine learning algorithm that can handle these challenges and more.

anandprems/matplotlib_python

anandprems/ML-For-Beginners-2024

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

anandprems/physionet_ptbdb_ann

anandprems/Python_RAM_

anandprems/Python_RE

anandprems/Regression---40model---Pyforest

In this repository, i discussed about by using Pyforest command we can easily retrive around 40 differernt model's Adjusted R2, R2, RMSE and time taken values, without spending much time in coding. I am not saying this is the best way to explore but still this is one kind of way to do coding

anandprems/sai_ram_engineering_machine_learning

anandprems/Tree_Based_Machine_learning_PTB_dataset_

The primary objective of this particular paper is to classify the health-related data without feature extraction in Machine Learning, which hinder the performance and reliability. The assumption of our work will be like, can we able to get better result for health-related data with the help of Tree based Machine Learning algorithms without extracting features like in Deep Learning. This study performs better classification with Tree based Machine Learning approach for the health-related medical data. After doing pre-processing, without feature extraction, i.e., from raw data signal with the help of Machine Learning algorithms we are able to get better results. The presented paper which has better result even when compared to some of the advanced Deep Learning architecture models. The results demonstrate that overall classification accuracy of Random Forest, XGBoost, LightGBM and CatBoost, Tree-based Machine Learning algorithms for normal and abnormal condition of the datasets was found to be 97.88%, 98.23%, 98.03% and 95.57% respectively.

anandprems/workshop-related-to-ML-and-DL-

M.O.P. Vaishnav College for Women - 2 days workshop

anandprems/YouTube-Video-Processing-Scripts