This is a repo for ml workshop from college

Notes:

-----------------------Day 1-------------------

Teaching the computer without explicit code. make the machine understand.

Applications weather prediction stock market prediction You-tube video suggestion

Types supervised -provide past data -generate patterns -structure data(dog/cat analogy hair,size,etc) un-supervised -unstructured data

ONLY 5 different types of algorithms 1.classification 2.regression 3.anomaly detection 4.clustering 5.reinforcement

Classification(knn,decision tree classifier etc) knn - k nearest neighbor algorithm has discrete values

Regression has continuous values

Clustering group the data

Anomaly detection eg Spam changes in data

Reinforcement learning punishment/reward

Practical implementation step 1 -get data step2 -clean data step3 -make a model step4-train step5 -Predict

DEPENDENCIES PYTHON3 https://github.com/scriptonist/scripts

Apple/orange Gender classification packages scikitlearn -ml library, graph and stuff

================================== DAY 2===========================

references sirajology and udacity

knn algorithm implementation challenge: take 5 algorithm test with iris and find out the one with the most accuracy

70 accuracy is good

world challenge: create a face detection algorithm

Linear Regression

Random forest Give massive data, creates different decision tree and executes everything together

SDG classifier Schotiomatic gradient decent will optimise the the model to provide better accuracy and helps in generalisation Refer Jupyter Notebooks from the git repo of Udacity pytorch