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artificial-intelligence-deep-learning-machine-learning-tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)

aws-machine-learning-university-accelerated-cv

aws-machine-learning-university-accelerated-nlp

aws-machine-learning-university-accelerated-tab

case_studies

Couple of Case Studies in Python for following Datasets.

credit_risk_model

A comprehensive credit risk model and scorecard using data from Lending Club

data-science-cheatsheet

datathon-on-cryptocurrenies

Algorithm for Timeseries_Analysis on different Cryptocurrencies

faceswap

Deepfakes Software For All

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

introduction-to-xgboost-using-r

Dataset : Porto Seguro’s Safe Driver Prediction

loan_prediction

machine_learning_basics_in_r

Following repository contains Introduction to Machine learning using R.

manipal_call_centre

Analysis on Revenue and leads sheet on manipal call centre data for the month of March,April and May.

pubg_placement_prediction

In a PUBG game, up to 100 players start in each match (matchId). Players can be on teams (groupId) which get ranked at the end of the game (winPlacePerc) based on how many other teams are still alive when they are eliminated. In game, players can pick up different munitions, revive downed-but-not-out (knocked) teammates, drive vehicles, swim, run, shoot, and experience all of the consequences -- such as falling too far or running themselves over and eliminating themselves. You are provided with a large number of anonymized PUBG game stats, formatted so that each row contains one player's post-game stats. The data comes from matches of all types: solos, duos, squads, and custom; there is no guarantee of there being 100 players per match, nor at most 4 player per group. You must create a model which predicts players' finishing placement based on their final stats, on a scale from 1 (first place) to 0 (last place).

sentiment_analysis

Sentiment Analysis for UCL final 2018 using Twitter API

time-series-analysis

Basic Time series Analysis on AirPassenger Dataset using ARIMA model

titanic-machine-learning-from-disaster

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.