5. What is not Machine Learning ?
● Rule Based Approach
● Legacy Systems
6. Learning
Algorithm
What is Machine Learning ?
● Solve prediction problem
● Logic is learned from examples & not by rules
Prediction Function
or
Trained Model
Training Data
Input Data
Prediction
7. Types of Machine Learning
Machine Learning
ReinforcementUnsupervisedSupervised
Task Driven Data Driven Environment Driven
8. Spam Mail Detection
● Input - Mail
● Output - Spam or Ham
● Supervised Machine Learning
● Binary Classification Problem
9. ● Input - Sensor Data
● Output - Failure time
● Supervised Machine Learning
● Regression Problem
Predicting Lift Failure
17. MLP - Business Understanding
● Business understanding includes clarity what you are trying to achieve.
● Machine learning is not possible with small data size
● Consolidating data pipeline to channelize continues flow of data.
● Web scraping, data lakes access, REST etc.
18. MLP - Data Wrangling
● Production data is never clean.
● It needs a major effort ( around 70% of total effort ) to make it ready for next stage
● Transforming & mapping data from raw format to another format ready for next stage
19. MLP - Data Visualization
● Visualization makes it easy to grasp difficult concepts
● Find useful pattern in the data
● Interactively drill down into charts for deeper details
20. Vectors - Fixed length array of numbers
● Text documents
● Image files
● CSV
● Audio
● Video
● Time Series data
● Many more ...
MLP - Data Preprocessing
Feature Extraction
21. MLP - Model Training
Learning Algorithm
Regression/Trees/SVM/Naiv
e Bayes/Neural Networks/
Prediction Function
or
Trained Model
22. ● Linear Regression
● Logistic Regression
● Naive Bayes
● Nearest Neighbors
● Decision Trees
● Ensemble Methods
● Clustering
● Support Vector Machines
● Neural Networks
● CNN
● RNN
● GAN
MLP - Learning Algorithms
24. MLP - Model Validation
● Training different learning method will give you different trained model.
● Also, each model have huge possibilities of configuration (hyper-parameters).
● Finding the best model among all possibilities & best configuration for it is done as a part
of Model Validation.
● If results are not satisfactory, one has to go back in the chain & fix a few things
28. 1. Reduce manual
effort of classifying
reviews.
2.Channelizing data
from Web server to
Analytics Engine.
1. Getting
data ready for
visualization.
2. Historical
data shows
past trends.
Visualization
of trend
Text needs to
be tokenized
& vectorized
Different
models were
trained.
Naive Bayes,
SGD Classifier
Choose the
best model
with best
hyper-
parameter
Naive Bayes
(MultinomialNB)
was chosen & put
in deployment
1. Customer Service Industry
● Manually labeled data is used for training model.
● Labels are target & review are feature data
● Batch training is supported by MultinomialNB allowing incremental learning
● Any mis-classification done by model will be labelled right & fed again
30. 2. Fast Query Chatbots
1. Reduce manual effort
understanding the text
query
2. Waiting for BI has a
long turnaround time
3. We are trying to do this
using chatbot
1. Getting data
ready for
visualization.
2. Historical
data shows
past trends
Visualization
of trend of
text & sql
Text cannot
be used for
ML
Needs to be
tokenized &
vectorized
Deep learning
models with
different layer
configuration
Choosing the
best model
with best
hyper-
parameter
Model with best
config was chosen
& put in
deployment
● Convert natural language query to SQL Query
● Model is trained with historical text (feature) & SQL (target)
● The generated SQL was executed & Output was subjected to visualization libraries
● Anybody without database & infra understanding can get visualization in seconds
32. ● Deep Learning - A specialization of Machine Learning
● ML vs DL vs AI
● AI Timeline
● What does AI consist of ?
● Where AI can be adopted in business
● Challenges in adopting AI
Module 2
AI in a
Nutshell
33. What is Deep Learning ?
● Specialized Learning Technique
● Rather than we choosing features for learning, this technique finds important
feature derivatives.
● Objective is to learn best derived features for prediction.
● It mimics the way our brain learns
● Very useful for natural language, computer vision, audio, video etc.
34. Do you always need Deep Learning ?
● More data is required for Deep Learning
● More Compute Power
● Models less interpretable
“Don’t kill a mosquito with a cannon ball”
Don’t use Deep Learning if you don’t need to
39. Imp : Advice to executives about AI
● Everybody should embrace modern capability of AI, on other they should also think
about business specific problems. Not every single tool that AI community can
develop can suit them correctly.
● Biggest challenge is people change not technology change, biggest gap now is
people who can map technology to business problem.
● Insourcing vs outsourcing. Building Team vs using enterprise solutions.
● AI will change everything in next few decades. Be a part of it.
40. Challenges - Data & Security
● Volume of data - Machine learning
on smaller data is infeasible.
● Accessibility of data - Important
data is not accessible & may be in
encrypted format.
info@zekeLabs.com | www.zekeLabs.com | +91
41. Compute, Storage & Network Power
● AI products needs data gathering from sensors, servers etc.
● Once gathered, data needs to be stored for further processing.
● Learning algorithms & data processing activities need lot of compute power.
42. Infrastructure for development
● Finding the best model is an iterative process.
● More experiments leads better model.
● Hyper-parameter Tuning
● Scaled infrastructure for developer is
important.
43. Infrastructure for deployment
● Speedy Deployment
● Easy deployment
● Fluctuating Demand
● Need of Elastic infrastructure
● Cost optimization
45. Cost optimization:
● Use Open Source alternatives
● Infrastructure optimization
● Don’t reinvent the wheel
46.
47. ● Will AI benefit human ?
● AI in human computer interaction
● Impact of AI on business
● Impact on workplace
● Impact on society
Module 3
Impact of
AI
48. AI benefit human - social, environmental
● Predicting diseases
● 60% People would prefer AI assistance over humans as financial advisors or tax
preparers
● 71% people believe that AI will help humans solve complex problems and help live
more enriched lives
55. Impact of artificial intelligence on society
● People are averse to the idea of availing annual
health check-ups at home with a robotic smart kit
(77%) or having chatbot assistant teachers in
universities/ colleges that lower the cost of overall
tuition (61%).
● Responsible AI ensures that its workings are aligned
to ethical standards and social norms pertinent within
its scope of operations.
● Explainable AI is responsible for building AI models
with accountability and the ability to describe or
depict why a certain decision was made by the
algorithm.
56. ● Programming Language
● Open source libraries
● Infrastructure Optimizations
● Other alternatives
Module 4
Identify the
right tools
58. Why Python makes life easy ?
● Easy to learn for ETL developers
● Integrates very well with other technologies
● Full-stack development -
○ Dashboard using bokeh,
○ Web application using django,
○ Machine learning models using scikit,
○ Scaling using PySpark
63. Monolithic Infrastructure - Preallocated Infra
Model Training
● Developers request access
whenever required
● Might incur delay in peak
working hours.
● Idle in non-working hours
Model Interfacing
● Idle in non-peak hours.
● May fall short in spikes.
● Pay even if infra is not used
64. Serverless Infrastructure - Elastic Allocation
Model Training
● No-preallocation
● Pay only for what you use
● Absolute no idle time for infra
● No wait time for developers
Model Interfacing
● Allocate infra only when required
● Scales down during non-peak
hours
● Improved customer experience
even in peak hours
66. Distributed Machine Learning using Spark
● Apache Spark is a distributed data processing
framework.
● Many machine learning algorithms are
implemented in Spark.
● Most of the API’s are same that of scikit-learn
● Scaled ETL & Machine Learning can be done
using Spark
75. Visit : www.zekeLabs.com for more details
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