Pinned Repositories
ai-applications
roadmap to applied ai
AI_Supply_Chain
This is the code for "AI for Supply Chain" by Siraj Raval on Youtube
Air-Pollution-Monitoring-using-IoT-Data-Viz.-ML
A prototype developed by me to collect the real-time data of the different types of pollution gases present in the air and analyzing and visualizing them and doing certain predictions.
Amazing-Feature-Engineering
Andrew-NG-Notes
This is Andrew NG Coursera Handwritten Notes.
Applied_AI_coursenotes
article-resources
A repository for the source code, notebooks, data, files, and other assets used in the data science and machine learning articles on LearnDataSci
Audio-Classification-Data-Preparation
Dynamic Data-set preparation for audio, video, images
awesome-object-detection
Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
Predictive-Maintenance-For-Air-Quality-Index-using-Sensor-Data
Air Quality Index Predict the weather is good or bad
sw-ot-ashishpatel's Repositories
sw-ot-ashishpatel/Deep-Learning-Coursera
Deep Learning Specialization by Andrew Ng, deeplearning.ai.
sw-ot-ashishpatel/LearnAI-ADPM
Learning Materials related to Anomaly Detection and Predictive Maintenance Course
sw-ot-ashishpatel/Classification-of-Environmental-Sound-using-Deep-Learning
Classification of Environment Sound using CNN and ImageDataGenerator
sw-ot-ashishpatel/datascience-ai-machinelearning-resources
Alex Castrounis' curated set of resources for artificial intelligence (AI), machine learning, data science, internet of things (IoT), and more.
sw-ot-ashishpatel/tutorials
机器学习相关教程
sw-ot-ashishpatel/machine-learning-notes
My online shared notebook for Machine Learning notes
sw-ot-ashishpatel/Tensorflow-Tutorial
Tensorflow tutorial from basic to hard
sw-ot-ashishpatel/dask-tutorial
Dask tutorial
sw-ot-ashishpatel/tensorflow_tutorials
From the basics to slightly more interesting applications of Tensorflow
sw-ot-ashishpatel/ml-lessons
Intro to deep learning for medical imaging lesson, by MD.ai
sw-ot-ashishpatel/Fundamentals-of-Scalable-Data-Science
This repository has the solution to assignments for the course Fundamentals-of-Scalable-Data-Science by IBM on Coursera for future reference. It was a great learning experience and I would not want any other learners to take reference from the same and do them on their own.
sw-ot-ashishpatel/Spotify-Music-Analysis-Using-Machine-Learning
Web Scraped data from spotify.com website to perform predictive analysis using Python. Wrangled and pre-processed data and developed strategies to categorize the audio features of the song. Trained supervised classification methods to predict the likeness of the song. Trained Logistic Regression model on a scaled version of data to determine which audio feature has the biggest impact on the likeliness of the song.
sw-ot-ashishpatel/Applied_AI_coursenotes
sw-ot-ashishpatel/tricks-used-in-deep-learning
Tricks used in deep learning. Including papers read recently.
sw-ot-ashishpatel/ai-applications
roadmap to applied ai
sw-ot-ashishpatel/face-recognition
Deep face recognition with Keras, Dlib and OpenCV
sw-ot-ashishpatel/IBM-Coursera-Applied-AI-with-Deep-learning
sw-ot-ashishpatel/Classification-of-Water-based-on-Quality-using-ML
sw-ot-ashishpatel/Data-Analysis-Science
The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run this collections either in Jupyter notebook or python alone.
sw-ot-ashishpatel/MilkHeatTreatmentEvaluation
A readily available, fast, non-destructive front-face fluorescence technique is used as a tool for assessing heat treatment effects on milk. Spectral data on raw, pasteurized, UHT pasteurized and sterilized milk samples from a wide range of manufacturers are obtained, including reconstituted milk and its mixtures with pasteurized milk. The principal component analysis (PCA) is used to summarize all the data obtained, and a classification model is developed to distinguish between two classes of milk (1) raw and pasteurized milk and (2) milk that was exposed to high heat treatment (UHT pasteurization or sterilization), or contains products of such a treatment (dry milk). A validation procedure using a test set showed the model to be accurate to within less than 5%. A new spectral index for use in the present context, which uses the ratio of the vitamin A and Maillard reaction products peaks in fluorescence excitation spectra, is proposed and compared with the conventional FAST index.
sw-ot-ashishpatel/Air-Pollution-Monitoring-using-IoT-Data-Viz.-ML
A prototype developed by me to collect the real-time data of the different types of pollution gases present in the air and analyzing and visualizing them and doing certain predictions.
sw-ot-ashishpatel/Word-embedding-with-Python
word2vec, doc2vec, GloVe implementation with Python
sw-ot-ashishpatel/Music-Mood-Detection-using-Text-Mining-on-Lyrics-
During my undergrad, I implemented a music recommendation system based on music digital track analysis. However, it's time for me to use text mining technology on lyrics to upgrade that project. Goals: (1)build a music mood(happy or sad) classifier based on lyrics analysis (2)what words and their distributions are in different mood categories? (3)How are the key words change in songs for the recent years? Project evaluation: (1)data collection: the training data and validation data will be collected from the largest lyric database on Lyricwiki.org (2)feature selection: the most common feature type to consider are BOW(bag of word) and POS(part of speech) combined with stemming using word-net (3)Training model : SVM, Naive Bayes using grid search method. (4)data visualization for goal two and three This project will be done using python on jupyter notebook. reference: Hu, X. (2010). Improving music mood classification using lyrics, audio and social tags (Doctoral dissertation, University of Arizona).
sw-ot-ashishpatel/Papers2Code
My implementation of famous ML models in Python and Keras(TensorFlow)