It Involves basic introduction to machine learning with a small code snippet atthe end

1. INTRODUCTION TO
MACHINE LEARNING
Guided by
Ms. Akshatha Prabhu, B.E., M.Tech
Assistant Professor
Dept of C.S & Eng.
Presented By
Sangat D Babu
4ES12CS060

2. OUTLINE
 What is Machine Learning?
 Difference between AI and ML
 Classification of ML
 Different Algorithms in ML
 ML with Python
 Conclusion
 Reference

3. What Is Machine Learning?
 "Field of study that gives computers the ability to learn without being
explicitly programmed“
- Arthur Samuel
 "A computer program is said to learn from experience E with respect to some
class of tasks T and performance measure P if its performance at tasks in T, as
measured by P, improves with experience E“
-Tom M. Mitchell

4. What It Does?
 A core objective here is to generalize from its experience.
 Generalization in this context is the ability of a learning machine to perform
accurately on new, unseen examples/tasks after having experienced a
learning data set.

5. How It Generalize?
 Training
 Testing
 Predicting

6. AI and ML
 Machine learning deals with designing and developing algorithms to evolve
behaviors based on data. One key goal of machine learning is to be able to
generalize.
 Artificial intelligence encompasses other areas apart from machine learning,
including knowledge representation, natural language
processing/understanding, planning, robotics etc.
 Artificial Intelligence is a broader class which includes Machine Learning.

7. Classification Of ML
 Supervised learning: The computer is presented with example inputs and their
desired outputs, and the goal is to learn a general rule that maps inputs to
outputs.
 Unsupervised learning: No labels are given to the learning algorithm, leaving
it on its own to find structure in its input. Unsupervised learning can be a goal
in itself (discovering hidden patterns in data) or a means towards an end.

8. Supervised Learning
 Supervised learning is the machine learning task of inferring a function from
labeled training data. The training data consist of a set of training examples.
In supervised learning, each example is a pair consisting of an input object
(typically a vector) and a desired output value

10. Unsupervised Learning
 Unsupervised learning is the machine learning task of inferring a function to
describe hidden structure from unlabeled data. Since the examples given to
the learner are unlabeled, there is no error or reward signal to evaluate a
potential solution.

12. Algorithms in ML
 Naive Bayes classifier
 Support vector machines
 Decision tree learning

13. Naive Bayes classifier
 In machine learning, naive Bayes classifiers are a family of
simple probabilistic classifiers based on applying Bayes' theorem with strong
(naive) independence assumptions between the features.

14. Support vector machines
(SVM)
 In machine learning, support vector machines are supervised
learning models with associated learning algorithms that analyze data used
for classification and regression analysis. Given a set of training examples,
each marked for belonging to one of two categories, an SVM training
algorithm builds a model that assigns new examples into one category or the
other, making it a non-probabilistic binary linear classifier.

15. Decision tree learning
 Decision tree learning uses a decision tree as a predictive model which maps
observations about an item to conclusions about the item's target value. It is
one of the predictive modelling approaches used in statistics, data
mining and machine learning.

16. ML with Python
Open Source software
Scikit –learn
R
Tensor Flow
Open CV
Commercial Software
Google predict API
MATLAB
Amazon Machine Learning

17. scikit-learn
 scikit-learn (formerly scikits.learn) is an open source machine
learning library for the Python programming language. It features
various classification, regression and clustering algorithms including support
vector machines, random forests, gradient boosting, k-means and DBSCAN,
and is designed to interoperate with the Python numerical and scientific
libraries NumPy and SciPy.
 Python supports sklearn and many other libraries

19. CONCLUSION
 Machine Learning research has been extremely active the last few years. The
result is a large number of very accurate and efficient algorithms that are
quite easy to use for a practitioner.
 In next few years use of machine learning will rise rapidly.
 Google and many other companies are trying hard to develop a easy open
source platform to implement machine learning in your project.

20. REFERENCES
[1] Gunnar Ratsch ¨ Friedrich Miescher Laboratory of the Max Planck Society,
Spemannstrae 37, 72076 Tubingen, Germany http://www.tuebingen.mpg.de/raetsch -
- A Brief Introduction into Machine Learning
[2] https://en.wikipedia.org/wiki/Machine_learning
[3] http://scikitlearn.org/stable/modules/generated/sklearn.naive_bayes
.GaussianNB.html
[4] https://classroom.udacity.com/courses/ud120/lessons/2258728540/
concepts/24040886640923

21. THANK YOU