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1. The Big Question
Can We Make Computers Self Learn From Data?
Monday, December 9, 2019 Slide No : 1Machine Learning By Sathish Yellanki

2. Monday, December 9, 2019 Machine Learning By Sathish Yellanki Slide No : 2
What is Machine Learning?

3. Machine Learning is A Field of Computer Science That
Gives Computers The Ability To Learn Without Being Explicitly
Programmed
Wikipedia
Machine Learning is A Type of Artificial Intelligence (AI) That Allows
Software Applications To Become More Accurate in Predicting
Outcomes Without Being Explicitly Programmed
Tech Target
Machine Learning is The Science of Getting Computers to Act
Without Being Explicitly Programmed
Stanford University
Monday, December 9, 2019 Slide No : 3Machine Learning By Sathish Yellanki

4. What is The Functional Flow of Machine Learning?
Monday, December 9, 2019 Slide No : 4Machine Learning By Sathish Yellanki

5. Monday, December 9, 2019 Machine Learning By Sathish Yellanki Slide No : 5
Real Time
Systems
Machine
Learning Field
Stream The Data To
Be Analyzed
Apply Self-Learning
Algorithms
Turn The Data into
Knowledge
Artificial
Intelligence Field
Is The Goal
Achieved?
Yes
Stop
No
Re-Analyze The
Data

6. What Are The Different Types of Machine Learning
Approaches?
Monday, December 9, 2019 Slide No : 6Machine Learning By Sathish Yellanki

7. Monday, December 9, 2019 Machine Learning By Sathish Yellanki Slide No : 7
Supervised Learning
• Labeled Data
• Direct Feedback
• Predict Outcome OR Future
Un-Supervised Learning
• No Labeled
• No Feedback
• Find Hidden Structure in Data
Reinforcement Learning
• Decision Process
• Reward Systems
• Learn Through Series of Actions

8. Supervised Learning
Monday, December 9, 2019 Slide No : 8Machine Learning By Sathish Yellanki

9. Where Supervised Learning is More Suitable?
• Supervised Learning is More Suitable in “Making Predictions
About The Future”
What is The Main Goal of Supervised Learning?
• Learn A Model From Labeled Training Data Which Allows The
End-User To Make Predictions About Unseen OR Future Data.
What Does Supervised Learning Refers To?
• Supervised Learning Refers To A Set of Samples Where The
Desired “Output Signals Called Labels” Are Already Well
Known.
Monday, December 9, 2019 Slide No : 9Machine Learning By Sathish Yellanki

10. What is The Functional Flow of Supervised Learning?
Monday, December 9, 2019 Slide No : 10Machine Learning By Sathish Yellanki

11. Monday, December 9, 2019 Slide No : 11Machine Learning By Sathish Yellanki
Training Data
With Labels
Apply Machine
Learning Algorithm
Inject New
Data
Develop A
Predictive Model
Finalize And Cross
Verify The Prediction

12. What Are The Sub-Categories of Supervised Learning?
Monday, December 9, 2019 Slide No : 12Machine Learning By Sathish Yellanki

13. Classification Task
• The Main Goal is To Predict The Categorical “Class Labels” of
New Operational Instances, Based on Past Observations.
• Create Possible Classes which Are Discrete To Identify By
Properties.
• Class Labels Can Be “Binary Class” OR “Multi-Class” By Nature.
• Any Label That is Not Part of The Training Dataset is Recognized
Correctly.
What is Binary Class?
• In “Binary Class” The Training Samples Are Labeled Exactly By
Two Categories.
• The Sample Gets Distributed into Two Different Dimensions on
The Graph.
What is Multi Class?
• In “Multi Class” The Training Samples Are Labeled By Multiple
Categories.
• The Sample Gets Distributed into Multi Dimensional Axis on The
Graph.Monday, December 9, 2019 Slide No : 13Machine Learning By Sathish Yellanki

14. Regression Task
• The Main Goal of “Regression Task” is To Predict For Continuous
Outcomes.
What is The Approach of Regression Analysis?
• In “Regression Analysis” The Process is Given A Number of
“Predictor Called As Explanatory Variables” And A “Continuous
Response Variable Called As Outcome OR Target”, And The
Process Should Try To “Find A Relationship Between Those
Variables That Allows The Process To Predict An Outcome”
What is The Success Factor For Regression Analysis?
• “Regression Analysis” Can Be More Successful And Accurate
When We Supply
• Huge Sample OR Training Data
• More Number of Explanatory Variables
• Proper Relationship Between The Variables
Do We Have Any Classes in Regression Analysis?
• In Practical Sense Regression Analysis Does Not Has
Classification of Classes.Monday, December 9, 2019 Slide No : 14Machine Learning By Sathish Yellanki

15. Un-Supervised Learning
Monday, December 9, 2019 Slide No : 15Machine Learning By Sathish Yellanki

16. Where is Un-Supervised Learning is Applied?
• Un-Supervised Learning is Most Prominent in Discovering
Hidden Structures.
• In Un-Supervised Learning We Deal With Unlabeled Data OR
Data of Unknown Structure.
Where Un-Supervised Learning is More Suitable?
• Explore The Structure of The Data To Extract Meaningful
Information Without The Guidance of A Known “Outcome
Variable” OR A “Reward Function”.
What Does Un-Supervised Learning Refers To?
• Un-Supervised Learning Refers To A Set of Samples Where The
Desired “Output Signals Called Labels” Are Not Well Known
Monday, December 9, 2019 Slide No : 16Machine Learning By Sathish Yellanki

17. What is The Functional Flow of Un-Supervised Learning?
Monday, December 9, 2019 Slide No : 17Machine Learning By Sathish Yellanki

18. Monday, December 9, 2019 Slide No : 18Machine Learning By Sathish Yellanki
Training Data
Without Labels
Apply Machine
Learning Algorithm
Cross Verify
The Cluster
Develop A Clustered
Model
Finalize And Cross
Verify The
Classification

19. What Are The Sub-Categories of Un-Supervised Learning?
Monday, December 9, 2019 Slide No : 19Machine Learning By Sathish Yellanki

20. Clustering Concept OR Un-Supervised Classification
• The Main Task of Clustering Concept is To Find Sub-Groups
Within The Data.
What is Clustering Concept?
• Clustering is An Exploratory Data Analysis Technique Which
Allows The Developer To “Organize A Pile of Information into
Meaningful “Subgroups Called Clusters” Without Having
Any Prior Knowledge of Their Group Memberships”
How De We Finalize The Cluster?
• Cluster is Defined With A Group of Objects That “Share A Certain
Degree of “Similarity” But Are More “Dissimilar” To Objects”
in Other Clusters.
What is The Final Outcome of Clustering?
• Clustering is A Great Technique if Applied Properly For
“Structuring Information And Deriving Meaningful
Relationships From Data”
Monday, December 9, 2019 Slide No : 20Machine Learning By Sathish Yellanki

21. Dimensionality Reduction
• The Main Task of Dimensionality Reduction is Applied For Data
Compression.
What is Dimensionality Reduction Concept?
• Un-Supervised Dimensionality Reduction is A Very Common
Technique Used To Remove Noise From Data.
What is Most Challenging in Dimensionality Reduction?
• Identifying The “Noise” Within The Analyzed Dataset.
• Analyzing The Degrading Factor Upon The Predictive
Performance of The Algorithm.
What is The Final Implementation Concept?
• Compress The Data onto A “Smaller Dimensional
Subspace” And Retain The Most of The Relevant Information.
What is The Final Outcome of Dimensionality Reduction?
• A System The is More Comprehensive And Consistent in
Presentation.
Monday, December 9, 2019 Slide No : 21Machine Learning By Sathish Yellanki

22. Reinforcement Learning
Monday, December 9, 2019 Slide No : 22Machine Learning By Sathish Yellanki

23. Where Reinforcement Learning is Applied?
• Reinforcement Learning is Used in Solving Interactive Problems.
• In Reinforcement Learning We Interact With The Environment
To Learn The System in Continuity.
What is The Main Goal in Reinforcement Learning?
• The Goal is To Develop A “System” Which is Also Called As
“Agent” Which Can Improve its Performance Based on
“Interactions With The Environment”.
What Does Reinforcement Learning Refers To?
• In Reinforcement Learning The Information About The Current
State of The Environment Generally Includes A “Reward Signal”.
• An Agent Can Use Reinforcement Learning To Learn A Series of
Actions That Maximizes The Reward Via An Exploratory Trial-
And-Error Approach OR Deliberative Planning.
Monday, December 9, 2019 Slide No : 23Machine Learning By Sathish Yellanki

24. What is The Functional Flow of Reinforcement Learning?
Monday, December 9, 2019 Slide No : 24Machine Learning By Sathish Yellanki

25. Monday, December 9, 2019 Slide No : 25Machine Learning By Sathish Yellanki
Agent
Environment
State
Action
Reward
How Does Reward Maximization is Done?
• In General The Agent in Reinforcement Learning Tries To
Maximize The Reward By A Series of Interactions With The
Operational Environment.