Probabilistic Modeling:
	application of priciples of statistics to data analysis. Naive babes algorithm follows this model.
Kernal Methods:
	finds good decision boundries between two sets.
Decision trees(CART):
	model in which every node splits samples into branches against a rule 
Random Forest:
	large number of trees(CARTS) are used to predict outcome.
Boosting Machines:
	weak learners(decision trees) are sequentially used to reduce errors, Examples are Ada Boost and Gradient Boost
Why Deep learning:
	it uses layer-by-layer concept
Keras:
	Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library.
Deep learning properties:
	1. simplicity
		data->vectorize->nomalize
	2. scalability
	3. versatility and reusability
Terms in Deeplearning
	1. Class
		a category in a classification problem is called a class
	2. Sample/record
		Data Points are called samples
	3. label(output)
		class associated with specific sample is called label
	4. Data validation/Testing Data
	5. Features
		Columns in data tables
	6. Data Set
		1. First part of data set is Training
		2. Second part is called Testing
Data Set division:
	1. Test 2. Train 3. Validation
Machine Learning Problems
	1. Classification Problem
		when output is lebel like cat
	2. Regression problem
		when output is discrete number between range of numbers
	3. Clustring Problem/Unsupervised learning
		output considering behaviour, in form of groups or clusters
Datasets:
	1. Amnist
		70K images and label available
		Metrices :
			True Positive
			True Negative
			False Positive
			False Negative 
	2. Amnist Feshion
	3. Activation Function
		Activate only function that are required

Google colab
Mnist 
	60K images for training and 10K for testing
	Grey scale images are for Mnist dataset, RGB

tensorflow.keras.datasets import mnist
(train_images, train_label), test_images, test_labels) = mnist.load_data()

Same Terms:
	numpy array/tensor/metrix
Vectorization:
	conversion of data to numbers
trainimages[0].ndim
	tells dimentions like 2D
trainimages[0].shape
	tells shape like (28, 28)
trainlabel[0]
	tells image metrix at 0 index

y=Wx + B