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"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“ – T. Michell (1997)
Example: A program for soccer tactics
• Task : Win the game
• Performance : Goals
• Experience : (x) Players’ movements (y) Evaluation
Why do Automate?
A few thousand years ago:
Path of Machine Evolution…
System that Do
• Replicate repetitive human
System that Think
• Cognitive capabilities handle
• Learn to understand context
and adapt to users and
• Macro-based applets
• Screen Scraping data collection
• Workflow Implementation
• Process Mapping
• Business Process Management
• Built-in Knowledge repository
• Learning capabilities
• Ability to work with unstructured data
• Pattern recognition
• Reading source data manuals
• Artificial Intelligence Systems
• Natural Language Understanding and Generation
• Self Optimizing / Self Learning
• Predictive Analytics / hypothesis generation
• Evidence based learning
Evolution of Machine Intelligence
• Raw computing power can automate
complex tasks!Great Algorithms
+ Fast Computers
• Automating automobiles into autonomous
automata!More Data + Real-
• Automating question answering and
information retrieval!Big Data + In-
• Deep Learning + Smart Algorithms =
• New algorithm learns handwriting of
unseen symbols from very few training
examples (unlike typical Deep Learning)
Why Machine Learning?
Human Behavior & their Life are not logical like Code, not linear like a Formulas and not consistent like Rules, so it is hard for Machines
to understand & respond to humans, that is the challenge for todays Digital world. Unless, Machine starts to Learn this ever changing
human behavior, it can neither understand effectively nor respond intelligently & personally with its human counterpart. Machine
Learning algorithms offers a mechanism to understand this non-linear, non-consistent and intuitive behavior.
Machine Learning to help machine Learn
about Human World.
Types of Tasks for ML
Decide between two classes
Group data points tightly
Fit the target values
Find something out of place
Calls to Customer Care
Delta Change in Calls
Grouping by distance from
Call drops due to technical
How to build Model?
Task : Prove Hypothesis
Experience : Nature of Training Data
Goal : Minimize Loss Function
Loss Function = | Predicted Value – Actual Value |
How to evaluate Model Performance?
• Less relevant Feature
• Smaller Training Data Set
• Higher Polynomials
• High/Low Learning Rate
• High/Low Regularization Value“Underfitting”
What are Key Data Learning Algorithms?
Learning from Data Paradigm
• Learning by fully
• Used For: Prediction,
labels), Regression (real
• Learning by Data
• Used for: Clustering,
estimation, Finding association
• Learning by Feedback
• Used for: Decision making
(robot, chess machine)
• Learning by partially
labelled and Data
• Used For: Prediction,
labels), Regression (real
What are Key Problem Solving Algorithms?
What is probable
effect of it?
How can we generalize
Is this A or B? Is
this A or B or C?
What is its decision
Can we draw straight
rules from it?
How is it Organized?
Can combining models gives
many it is?
Can we get higher
abstraction from it?
What is common in
What is the similarity
Can it draw finer
feature from it?
Is it weird? What should I do
Anomaly Detection Reinforcement
Engineering Intelligent System
What is difference between Software vs Intelligent System Engineering?
NFR / Performance Testing
Code Implementation Unit Testing
HLD - Architecture Level LLD – Class and method level
Technical Specification of
Monitoring Evaluating Managing
Error Analysis Tuning Model
Model Selection Model Training
Feature Extraction / Processing
Feature Ranking / Selection /
Data Acquisition Data Preprocessing
Software System Engineering Process Intelligent System Engineering Process
What is NEXT in ML?
What is DL?
• “Deep Learning is a set of algorithms in Machine Learning that Attempts to model high level abstractions in data by using
architecture composed of multiple non-linear transformations.”
• Deep Learning don’t need to provide explicit Feature Engineering. It learns based on algorithm’s non learner transformation
Demo – Predicting Consumer Churn
• Company has been managing CRM Process for a large US based
• Lately, Client has been showing concerns about Customer churn due to
• Company wants to help its client by developing an Intelligent System to
predict/detect customers which are likely to abandon their
Area Code Phone Int'l Plan VMail Plan
Intl Mins Intl Calls
• Customer Churning can be predicted by their Usage of Calls as well as Frequency of Customer Care calls.
Objective of Demo:
• To evaluate and select best performing ML Model for predicting Customer Churn. (Build Phase)
Irrelevant Columns Binary Value Columns (Yes/No)
Binary Classification Reading CSV File into
columns and modifying
Train models with
three best with Cross
Matrix – Find best
most suitable Algo
Demo - Evaluating Models
• Precision -When a classifier predicts an
individual will churn, how often does that
individual actually churn? (Accuracy)
Precision = 235 / 269
Recall = 235 / 483
Precision = 330 / 256
Recall = 330 / 483
Precision = 167 / 211
Recall = 167/ 483
• Recall -When an individual churns, how often
does my classifier predict that correctly?