Intro About machine learning algo and types

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

5. Unsupervised Learning

6. Reinforcement Learning

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!

9. Some ML terms:-

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