Machine Learning, case study, advantages, disadvantages,Challenges in implementation..

1. Machine Learning

2. DATADATA
OUTPUTOUTPUT
PROGRAMPROGRAM
ML works with data and processes it to
discover patterns that can be later used to
analyze new data. ML usually relies on specific
representation of data, a set of “features” that
are understandable for a Machine.

3. Popular Scripting languages platforms
used for ML : R , Python and Phantom
and SQL ,SAS , Java , MATLAB.

4. R is a programming language and
software environment for statistical
computing and graphics supported by
the R Foundation for Statistical
Computing. The R language is widely
used among statisticians and data
miners for developing statistical software
and data analysis. Polls, surveys of data
miners, and studies of scholarly literature
databases show that R's popularity has
increased substantially in recent years.

5. CategoriesCategories
• Supervised Learning
• Un Supervised Learning
• Semi Supervised Learning
• Reinforcement Learning
• Deep Learning

6. 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

7. Unsupervised learning is a type of machine
learning algorithm used to draw inferences
from datasets consisting of input data without
labeled responses. The most
common unsupervised learning method is
cluster analysis, which is used for exploratory
data analysis to find hidden patterns or
grouping in data.

8. Semi-supervised learning is a class
of supervised learning tasks and
techniques that also make use of unlabeled
data for training – typically a small amount
of labeled data with a large amount of
unlabeled data.

9. Reinforcement learning is an area of
machine learning inspired by behaviorist
psychology, concerned with how software
agents ought to take actions in an
environment so as to maximize some
notion of cumulative reward.

10. Deep Reinforcement Learning these
models, reinforcement learning finds
the actions with the best reward at each
play. This method is a widely used
method in combination with deep neural
networks to teach computers to play
Atari video games.

11. Methods
ClassificationClassification
RegressionRegression
ClusteringClustering
Model SelectionModel Selection
Dimensionality ReductionDimensionality Reduction
PreprocessingPreprocessing

12. Classification is to Identifying which category an
object belongs to.
Applications: Spam detection, Image recognition.
Algorithms: SVM, nearest neighbors,
random fores

13. Regression is predicting a continuous-valued
attribute associated with an object.
Applications: Drug response, Stock prices.
Algorithms: SVR, Ridge regression, Lasso….

14. Clustering is automatic grouping of similar
objects into sets.
Applications: Customer segmentation, Grouping
experiment outcomes
Algorithms: k-Means, spectral clustering, mean-
shift…

15. Dimensionality reduction is reducing the
number of random variables to consider.
Applications: Visualization, Increased efficiency
Algorithms: PCA, feature selection, non-negative
matrix factorization

16. Model Selection is comparing, validating and
choosing parameters and models.
Goal: Improved accuracy via parameter tuning
Modules: grid search, cross validation, metrics.

17. Preprocessing is feature extraction and
normalization.
Application: Transforming input data such as
text for use with machine learning algorithms.
Modules: preprocessing, feature extraction…

18. There are typically 3 phases of ML
Training Phase Here training data is used to train
the model by pairing the given input and expected
output.
Testing Phase Here Learning model is measured
for quality and estimate the properties like error,
recall, precision ..etc .
Application Phase: Here Model is subjected to
real world data for which result need to be derived

19. RAW DATA
Prediction
Rule
Training
Data
Evaluation
Dataset
Testing
Data
Predicted Behavior
Seed, Cleanse and
group dataset
Build and refine the
model
Validate the model

20. Few well-known uses of machine learning are spam
filters, recommendation engines, speech recognition
systems (speech-to-text or customer service), internet
advertising, news clustering (Google News), related
stories, handwriting recognition, questionable content
identification, automatic closed captioning, and machine
translations are ML-based

21. Case StudyCase Study: ML in customer analytics [Telecom]: ML in customer analytics [Telecom]
Network Data
Call Data Records
GPRS Data Records
Contact Center Logs
STRUCTUREDDATAUNSTRUCTUREDDATA
Data Aggregation
Build single of
Customer
Analytics
Engine
Next Best Offer
Campaign
Management
Social Network
Analytics
Churn
Prediction

22. Advantages Of MLAdvantages Of ML
•Useful where large scale data is available
•Large scale deployments of Machine Learning
beneficial in terms of improved speed and
accuracy
•Understands non-linearity in the data and
generates a function mapping input to output
(Supervised Learning)
•Recommended for solving classification and
regression problems
•Ensures better profiling of customers to
understand their needs
•Helps serve customers better and reduce
attrition

23. Disadvantages Of MLDisadvantages Of ML
• Limited understanding of the machinery of
classifiers (Black Box)
• Requires significant amount of data
• May not work in cases where data collection
is difficult or expensive
• Problem of over-fitting if model fitted on
small dataset

24. • Integration of data from different sources with in
this organization
• Good business understanding required to build
better input features
• Thorough understanding algorithms required it
can be Deployed
• Appropriate selection of machine learning
algorithm essential
• Implementing algorithms which can give more
business interpretability and insights
Challenges in implementing MLChallenges in implementing ML

25. •With big data a reality machine learning is finding wider
acceptance across various industries
•Machine learning is paving the way to solve complex
business challenges in an efficient and effective manner
•To reap the benefits of machine learning it is essential
to identify the areas where it can be applied effectively
•Good business understanding is required to build
smarter solutions
In SummaryIn Summary