SlideShare a Scribd company logo
Ashvini Jangid
Intro. To Machine Learning
Imjangid.github.io
Introduction Tools & Language Applications Machine learning
model
Generating Neural
network
1 2 3 4 5
Our Agenda
Step by Step learn
Classification
Regression
Linear regression for
regression problem
Random forest for
classification and
regression problem
An Approach to AI
Reward based
learning
Learning from +ve and –ve
feedback
Machine learn to act
Problem sit in between
both
Where large amount of
input data and some are
lable
Learn structure in the
input variable
Make best predication for
unlable data
LearningTypes
And algorithm
Clustering
Association
K-means for clustering
problem
Apriori algorithm for
association rule learning
problem
Types Of Learning
3
Semi SupervisedReinforcementUnsupervisedSupervised
Types Of Learning
Supervised Learning
 Linear Classifiers
 Support Vector Machines
 Decision Trees
 Boosted Trees
 Random Forest
 Neural Network
Unsupervised Learning
Reinforcement Learning
Neural Network What Can NN do?
•identify faces,
•recognize speech,
•read your handwriting
•translate texts,
•play games
•Control autonomous vehicles
and robots
•and surely a couple more things!
Some ML terms:-
4 Language useful in ML
Every Language has own quality but data science need some more
Import __hello__
It is a high-level programming language that
supports imperative, object-oriented, and functional
programming paradigms.
Print(“HelloWorld”)
R is a language and environment for statistical
computing and graphics. It is a GNU project which is
similar to the S language and environment
Console.log(“HelloWorld”)
It is a language which is also characterized as
dynamic, weakly typed, prototype-based and multi-
paradigm
Object HelloWorld{
def main(args: Array[String]):Unit = {
println(“HelloWorld”)
}
}
language providing support for functional programming and a strong static type system. Designed to be concise
11
Python R
SCALA
JS
machine_learning by ashvini jangid

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machine_learning by ashvini jangid

  • 1. Ashvini Jangid Intro. To Machine Learning Imjangid.github.io
  • 2. Introduction Tools & Language Applications Machine learning model Generating Neural network 1 2 3 4 5 Our Agenda Step by Step learn
  • 3. Classification Regression Linear regression for regression problem Random forest for classification and regression problem An Approach to AI Reward based learning Learning from +ve and –ve feedback Machine learn to act Problem sit in between both Where large amount of input data and some are lable Learn structure in the input variable Make best predication for unlable data LearningTypes And algorithm Clustering Association K-means for clustering problem Apriori algorithm for association rule learning problem Types Of Learning 3 Semi SupervisedReinforcementUnsupervisedSupervised Types Of Learning
  • 4. Supervised Learning  Linear Classifiers  Support Vector Machines  Decision Trees  Boosted Trees  Random Forest  Neural Network
  • 7.
  • 8. Neural Network What Can NN do? •identify faces, •recognize speech, •read your handwriting •translate texts, •play games •Control autonomous vehicles and robots •and surely a couple more things!
  • 10.
  • 11. 4 Language useful in ML Every Language has own quality but data science need some more Import __hello__ It is a high-level programming language that supports imperative, object-oriented, and functional programming paradigms. Print(“HelloWorld”) R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment Console.log(“HelloWorld”) It is a language which is also characterized as dynamic, weakly typed, prototype-based and multi- paradigm Object HelloWorld{ def main(args: Array[String]):Unit = { println(“HelloWorld”) } } language providing support for functional programming and a strong static type system. Designed to be concise 11 Python R SCALA JS