# (03)******************ML theory which algorithm used in the machine learning**********

# ---What is ML
# ----Machine Learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine Leaning focuses on the development of computer programs that can access data and use it learn for themselves. 

# ----there are 3 types 
# (01)----What is Supervised Learning?----Machine knows something beforehand
# ----Supervised Learning---Teaching the Machine
# --(1)-Already Tagged Data
# --(2)-Features and Labels present
# --(3)-Classification---Calssify which label a given set of features belog to!
# --(4)-Regression-------Find out the value of the label using previous data!

# (02)----What is Unsupervised Learing?----All machine knows is the data in front of it. No Features! No Labels
# ---Unsupervised Learning---Teaching the machine
# --(1)-Data Not Already Tagged
# --(2)-Features and labels not present
# --(3)-Training not done
# --(4)-Clustering and Association
# ----Clustering----Discover the inherent grouping in the data, such as grouping customers by purchasing behaviour.
# ----Association---Association rule learing problem. such as people that buy X also tend to buy Y

# (03)Reinforcement Learning?
# ---Reinforcement Learning is a Machine Learning method. ... Agent, State, Reward, Environment, Value function Model of the environment, Model based methods, are some important terms using in RL learning method. The example of reinforcement learning is your cat is an agent that is exposed to the environment.

# ----Regression model
# Regression model(Both linear and non_linear) are used for predicting a real value, like salary for example. if your independent variable is time, then you are forecasting future values, otherwise your model is predicting present but unknown values.

# ---Types of Machine Learning Regression Model:
# (1)Simple Linear Regression
# (2)Multiple Linear Regression
# (3)Polynomial Regression
# (4)Support Vector for Regression(SVR)
# (5)Decision Tree for Regression
# (6)Random Forest Regression

