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