Machine learning Algorithms in Enterprise Softwares

1. Machine Learning
In
Enterprise Softwares
~Ashish Kumar

2. Image source: https://pics.me.me/machine-learnin-machine-learning-everywhere-emegenerator-net-29035157.png

3. ML in Enterprise Apps
Image source: https://www.sap.com/products/machine-learning-foundation.htmlhttps://www.sap.com/products/machine-learning-foundation.html

4. source: https://www.sap.com/services.html#pdf-asset=eca5fe9a-247d-0010-87a3-c30de2ffd8ff&page=2

5. McKinsey Report
Image source: https://www.youtube.com/watch?v=Op83nO706po

6. ML Terminology
Image source: https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0

7. Process Involved
Image source: https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0

8. ML Approaches
● Supervised Learning(Predictive)
○ Learn mapping given dataset y(x) , D ={((xi,yi)}, e.g., MNIST classification
● Unsupervised Learning:(Descriptive)
○ Given only inputs , find interesting patterns D = {xi} e.g., Determine k cluster centers k
● Semi-supervised Learning
● Reinforcement Learning
○ How to act or behave when given occasional reward or punishment signals, e.g., how a robot
learns to walk to a power outlet

9. Types of Output

10. Linear Regression
● A statistical model that attempts to show the
relationship between two variables with a linear
equation.
● Involves graphing a line over a set of data points
that most closely fits the overall shape of the data.
● Shows the extent to which changes in a "dependent
variable," which is put on the y-axis, can be
attributed to changes in an "explanatory variable,"
which is placed on the x-axis.
Image source: https://towardsdatascience.com/introduction-to-machine-learning-
algorithms-linear-regression-14c4e325882a

11. Logistic Regression
● Method for analyzing a dataset
● There are one or more independent variables that determine an outcome.
Image source: https://towardsdatascience.com/logistic-regression-b0af09cdb8ad

12. Logistic Regression

13. SVM
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane.
Suppose you are given plot of two label classes on graph as shown in image (A). Can you decide a separating line for the
classes?
Given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new
examples
Image A

14. Neural Networks
Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns.
Rnn: Networks that add connections feeding the hidden layers of the neural network back into themselves.

15. Application of Logistic Regression:
● Logistic regression is used when the response you want to predict/measure is categorical with two
or more levels. Some examples are gender of a person , outcome of a football match
● Marketing:
○ A marketing consultant wants to predict if the subsidiary of his company will make profit, loss
or just break even depending on the characteristic of the subsidiary operations.
● Human Resources:
○ The HR manager of a company wants to predict the absenteeism pattern of his employees
based on their individual characteristic.
● Finance:
○ A bank wants to predict if his customers would default based on the previous transactions and
history.

16. Application of Logistic Regression:
● Image Segmentation and Categorization
● Geographic Image Processing
● Handwriting recognition
● Healthcare :
○ Analyzing a group of over million people for myocardial infarction within a period of 10
years is an application area of logistic regression.
○ Prediction whether a person is depressed or not based on bag of words from the corpus
seems to be conveniently solvable using logistic regression and SVM.
○ It is one of the best tools used by statisticians, researchers and data scientists in
predictive analytics.
● It is one of the best tools used by statisticians, researchers and data scientists in predictive
analytics.

17. ML in Enterprise Applications
● Sales Recommendations and Predictions
Example : Recommend related tickers, Predict next ticket from same customer
● Suggest products
Supporting documentation to sales reps
● Build models
Disparate sources of sales and marketing data
● Improve ROI

18. ML in Various Applications

19. Neural Network - Applications
1. Finance:
2. Insurance:
○ Fraud detection,
○ Why an individual rejected their service
3. Operations management:
○ Optimize the functioning of equipment and extends its lifespan
○ Monitor the process, assist in optimization, detection of defective products
4. Retail:
○ Estimates which products were bought today,
○ How many times, and
○ What combination of products was bought
5. Marketing:
○ To arrange a productive target marketing campaign

20. Neural Network - Applications
6. Text Summarization:
If a company wants to display key information from any literature within their apps or website, Text
Summarization would be helpful.
7. Text Autofill or next text recommendation:
Businesses looking to transform their data entry work by improving their workflow digitally can
achieve faster automation
8. Language Translation
Rather than hiring native translators to translate a massive volume of content, businesses can at
least improve their translation process using Recurrent Neural Network
9. Call Center Analysis
10. Digital Asset Management in Marketing

21. How LinkedIn uses ML algorithms ?
● LinkedIn uses neural networks along with linear text classifiers
○ to detect spam or
○ to detect abusive content in its feeds when it is created
● Use neural nets to help understand all kinds of content shared on LinkedIn
○ — ranging from news articles to jobs to online classes
○ — to build better recommendation and search products for members and customers.
Source: https://www.cmswire.com/digital-experience/what-is-a-neural-network-and-how-are-businesses-using-it/

22. How DialogTech uses ML algorithms ?
● DialogTech uses neural networks
○ to classify inbound calls into predetermined categories or
○ to assign a lead quality score to calls
● ML Actions performed based on the call transcriptions and the marketing channel or keyword that drove
the call,
For example, a caller who is speaking with a dental office may ask to ‘schedule an appointment.’ The
neural network will seek, find and classify that phrase as a conversation, therefore providing marketers
with valuable insights into the performance of marketing initiatives.
Source: https://www.cmswire.com/digital-experience/what-is-a-neural-network-and-how-are-businesses-using-it/

23. Use Case 1: Customer Engagement and Commerce
● Able to design location-specific advertisements for specific products and
distribute customized information to Facebook users.
● Consumers also receive location-relevant promotions at the right time on
mobile devices.
● The promotional information displayed on mobile devices serves as shopping
guidance in stores.
● The location-based marketing strategy through the use of social media has
generated a sales uplift of 10% to 15%

24. Use Case 2: Hospital - Monitoring of Patient Care
● Gives a 360-degree view of patients,
● A fully integrated patient care lifecycle management solution,
● Covers all cases such as prevention, operation, recovery, and community or
home care,
● The solution is mobile health app for patients and community doctors,
● Provides personalized online care plans on mobile devices issued to patients
by hospital doctors,
● Integrate medical care provided by primary care physicians

25. Case Study : SAP Leonardo ML Foundation
● It provides an enterprise-grade platform for machine learning in the cloud.

26. SAP Applications
● SAP Cash Application
○ Offers automation in finance,
○ Intelligent and Integrated Payment Clearing Automation for SAP S/4HANA powered by SAP
Leonardo Machine Learning
● SAP Brand Impact
○ Automatically analyzes large volumes of videos,
○ Video Analytics to Measure Brand Exposure Faster, Accurately, and at Scale
● SAP Service Ticket Intelligence
○ Automatically categorizes customer tickets and proposes solutions

27. Thanks !