Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
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Updated
Apr 29, 2024 - Jupyter Notebook
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
😀😄😂😭 A curated list of Sentiment Analysis methods, implementations and misc. 😥😟😱😤
Manuscript of the book "Supervised Machine Learning for Text Analysis in R" by Emil Hvitfeldt and Julia Silge
Supervised machine learning case studies in R! 💫 A free interactive tidymodels course
Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning
Code for reproducing Manifold Mixup results (ICML 2019)
Deep-learning inversion: A next-generation seismic velocity model building method
This Repository contains Solutions to the Quizes & Lab Assignments of the Machine Learning Specialization (2022) from Deeplearning.AI on Coursera taught by Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig.
Repository For Codes And Concept Taught in Udemy Course
Repo for AIML case studies and projects
In this repo, all about Machine Learning and I covered both Supervised and Unsupervised Learning Techniques with Practical Implementation. Everything from scratch and I solved a lot of different problems with different Machine Learning techniques either related to Healthcare, E-commerce, Sports, or Daily Business Issues.
A repository of resources for understanding the concepts of machine learning/deep learning.
This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
K Means Clustering - Unsupervised learning
An adaptive model for prediction of one day ahead foreign currency exchange rates using machine learning algorithms
🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.
The data complexity library, DCoL, is a machine learning software that implements all metrics to characterize the apparent complexity of classification problems. The code is implemented in C++ and can be run on multiple platforms.
With unbalanced outcome distribution, which ML classifier performs better? Any tradeoff?
TFM - Análisis de sentimientos en Twitter
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