Introduction to machine learning

	Read or watch me on youtube (HINDI)
	https://www.youtube.com/watch?v=iHOntnTQ3kU
	Machine learing is based on data and data is oil to run the business and making decisions. Now a days small and large business are using machine learing and artificial intellience to achieve the maximum profit.
	Types of data.....
	1. labelled data in supervised data
	2. unlabelled data ( cluster on the basis of their semilarities)
	3. No predefined data ...
	Python is a great language to make it possible to use data and make data meaningfull. Python is widely used to visualize data on the screen and make it useable. IOT and machine learing are connected where ML use algorithms to make decisions.
	Types of machine learning....
	1. Supervised Learning
	1.1 Regression
	1.2 Classification
	2. Unsupervised Learning
	1.1 Clustering
	1.2 Dimensionality Reduction
	3 Reinforcement Learning
	1. Supervised Learning......
	use supervised learning when the output data is known. the algorithm will predict new data . Supervised learning as the name indicates the presence of a supervisor as a teacher. So basically teacher teach to the machine with some data and then machine is provided with a new set of data so that supervised learning algorithm analyses the training data
	2. Unsupervised learning....
	Un supervised means no teacher is provided that means no training will be given to the machine. Therefore machine is restricted to find the hidden structure in unlabeled data by our-self.....
	Example ...
	Suppose it is given an image having both dogs and cats which have not seen ever. Thus the machine has no idea about the features of dogs and cats wo we can't categorize it in dogs and cats.
	But it can categorize them according to their similarities, patters and differences.....
	.....
	Clustering ( unsupervised learning)
	3. Semi Supervised Learning.....
	Why to use semi-supervised learning....
	...
	Supervised learning need labeled and accruate data it cost high when you have large volume of data , To label the data we need data scientist to make accruate the data...
	and unsupervised learning data is that it's application spectrum is limited.....
	Semi supervised learning is used when some amount of data is used to trained the model and make cluster according to the data then use unsupervised learning to make usable unlabled data and cluster them.....
	Intuitively, one may imagine the three types of learning algorithms as Supervised learning where a student is under the supervision of a teacher at both home and school, Unsupervised learning where a student has to figure out a concept himself and Semi-Supervised learning where a teacher teaches a few concepts in class and gives questions as homework which are based on similar concepts
	https://www.youtube.com/watch?v=iHOntnTQ3kU
	Thanks for reading..... Happy coding 😊👍
	---> Ajay yadav