I have started this presentation by short explanation of what machine learning is. Types of machine learning techniques. Need for machine Learning. Some Applications of Machine Learning and Two Algorithm with example.

1. Prepared By
RAM BABU KUMAR RAI
VTU10639
2
nd
YEAR
YEAR- 2019
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

2. TOPIC:
APPLICATIONS
OF
MACHINE LEARNING

3. PRESENTATION OVERVIEW
• INTRODUCTION TO MACHINE LEARNING
• CLASSIFICATIONS OF MACHINE
LEARNING
• NEED FOR MACHINE LEARNING
• APPLICATIONS OF MACHINE LEARNING

4. BEFORE KNOWING THE APPLICATIONS OF ML
LET US HAVE A SHORT INTRO ON MACHINE LEARNING.

6. What is Machine Learning ?

7.  Machine Learning is the way by which machines
are made intelligent by training them with some
datasets.
 It focuses on development of computer programs
that can learn automatically from experiences and
improve itself.
 It was coined by Arthur Samuel way back in 1959 at
IBM.

8. Classifications Of Machine Learning
1. Supervised Learning
 Machines are trained with well labelled data.
 After training, there accuracy is tested.
Classification
 Categories:
Regression
2. Unsupervised Learning
 No labelled data, work on data and fine some patterns.
 Group unsorted data according to their similarities and pattern
without any training.
 Categories: Clustering
Abstraction

9. 4. Reinforcement Learning
 No data is provided,
 It learns to react to an environment.
3. Semi-Supervised Learning
 Given data mixture of Classified and unclassified
data.
 Labelled is less in number
 Unlabeled data is in abundance.

10. SOME EXAMPLES OF ALGORITHM ARE:
SUPERVISED LEARNING:
1. Regression,
II. DECISION TREE
III. KNN
IV. Logistic Regression etc.
UNSUPERVISED LEARNING:
I. k-means for clustering problems.
II. Apriori algorithm for association rule learning problems.
III. Hierarchical Learning etc.
REINFORMENT LEARNING:
I. Markov Decision Process
II. Q-Learning etc.

11. Need For Machine Learning ?
• Machine can discover Hidden, non-obvious
patterns.
• It helps us from writing long programs.
• It solves big mathematics problem easily.
• It needs only one time programming, it learns from
experiences.
• It helps to detect unusual behaviors.
• It fasten the normal working of life.
Many more….

12. Applications Of Machine Learning

13. 1. Virtual Personal Assistants
 Siri, Alexa, Google Now are some examples.
 Operated with voice
 They help to find information when asked over voice.
 Activated with the help of voice.
ex: “Hello Google”, “Hey Alexa”, and so on.
Integrated to variety of platforms, Example: smart
speakers, Mobile Apps, Smartphones, etc.

14. Some Popular Virtual Personal Assistants

15. 2. Video Surveillance (uses Computer Vision)
■ It makes possible to detect crime before they happen.
■ Track unusual behavior of people like standing, seating, stumbling or
napping on benches, etc.
■ Sends detected data to human attendants.
■ If reported activities are counted true, they help to improve the
surveillance services.
■ It can provides various info about the vehicles too.

16. CCTV camera classifying different
vehicles

17. 3 . Stocks Predictions
 Analyses the previous behavior of the market
 Predicts rise and fall of stocks.
 Predicts the upcoming trend in markets.
 Market price of products in future.
 Also Learns from the current trends
 Upgrade itself automatically and periodically.
 Make itself more accurate

18. 4. Face Detection or Recognition
♦ Real time face detection can be used for locating homeless
children.
♦ It will help to stop human trafficking also.
♦ It is used to safeguard banking and authenticate real
person.
♦ It is used in many fields such as mobiles, computers, MNCs
to provide security and authentication entry.
♦ Facebook uses this technique in the image tagging.

19. Examples of Face Recognition
Mass face recognition

20. 5. Product Recommendations
■ Analyses our search from one site
■ Recommend the same products on all other
sites.
■ Used by E-Commerce sites like
Flipkart, Amazon, EBay, Snapdeal, etc.
■ Netflix

21. 6.Email Spam Filtering
■ System detects patterns in email.
■ Patterns like “Earn Cash ”, ” earn from home “ are likely to be
spam.
■ Use detected to make automated detections.
■ Over 3,25,000 malwares are detected everyday
■ Thus protects us from unwanted and hackable emails.
■ Email providers are using them
Such as Gmail. yahoo, Outlook, etc.

23. 7. Weather Forecasting
• Machines are trained with previous data.
• After training they predict the weather.
• Predicts Rain, cyclones.
• Can even forecast the areas where it will rain
and where cyclone can hit.

25. 8. DRIVERLESS VEHICLES
• Vehicles sense the environment and move
with little or no human input.
• Vehicles capable of navigating District
roadways
• No driver is needed
• Can overcome traffics successfully
• It will Avoid accidents, traffic jam, etc.
• It will reduce labor costs
• Parking space can be fully utilized.
• Companies working are: Google, BMW,
Tesla, BOSCH, Nissan, Ford, etc.

26. AUTONOMOUS CARS

27. 9. SPEECH RECOGNITION
• It is technology that recognizes Speech.
• Translate spoken language into text.
• It helps people with disabilities
• Music composing, sound auto tuning.
Ex: Google translator

28. • There is no doubt that voice-controlled programs such as
Apple’s Siri, Google Now, Amazon’s Alexa, and Microsoft’s
Cortana do not always understand our speech, but things are
likely to be improved in the near future.
• With the passage of time, the accuracy of speech recognition
engines is increasing

29. 1o. Medical field
■ Assist Doctors in Diagnosis
■ It can help them to diagnose numbers of health problems such as
 Breast cancer
◾ Lung cancer
◾ Prostate cancer
◾ Bone metastases
◾ Congenital heart defect
◾ Alzheimer’s disease
 It can even determine the stage of cancer.

30. 11. AI Based Computer Vision
Computer vision tasks is method for acquiring, processing,
analysing and understanding digital images, and extraction
of high-dimensional data from the real world in order to
produce numerical or symbolic information.

31. APPLICATIONS OF COMPUTER VISION
● Interaction, e.g., as the input to a device for computer-human
interaction;
● Modelling objects or environments, e.g., medical image analysis or
topographical modelling;
● Navigation, e.g., by an autonomous vehicle or mobile robot; and
● Organizing information, e.g., for indexing databases of images and
image sequences.

32. SYSTEM MODEL
● Image Acquisition- Getting images through camera
● Pre-processing-it includes(Re-sampling, noise-reduction, Sale space etc.)
● Feature extraction-identifying lines and edges
● Detection and Segmentation-getting points of interests.
● High level processing- small amount of data is fed based on certain
assumptions.
● Decision making- making final decision required for the application

33. ALGORITHMS USED IN COMPUTER VISION
● BASICS OF CONVOLUTION
● THE SOBEL AND LAPLACIAN DETECTORS
● IMAGE MOMENTS
● PIXELS NEIGHBOURHOODS AND
CONNECTEDNESS
● CONNECTED COMPONENTS LABELLING
● K-MEAN CLUSTERING

34. The Sobel Edge Detectors
● The Sobel edge detector is a gradient based method.
● It works with first order derivatives.
● It calculates the first derivatives of the image separately for the X and Y axes.
● The derivatives are only approximations (because the images are not continuous).

35. K-MEANS CLUSTERING ALGORITHM

36. ● Step 1: Get the dataset
● Step 2: Assign random
centers
● Step 3: "Own" datapoints
● Step 4: Shift the centers
● Step 5: The end!
Steps Involved in K-Mean Clustering

38. EXAMPE FOR K MEANS
FOR K=3

40. THANKYOU