The document provides an overview of machine learning, showcasing its definition, how it differs from traditional programming, and the fundamental steps involved in the machine learning process. It covers various algorithms, their use cases across different industries, and challenges faced when implementing machine learning solutions. The importance of choosing the right algorithm based on specific data requirements and objectives is emphasized throughout.

1. Machine Learning ML Overview
Algorithms Use Cases
and Applications
Your C ompany N ame

2. 2
Machine Learning
01
o What is Machine Learning?
o 7 Steps of Machine learning
o Machine Learning vs. Traditional Programming
o How does machine learning work?
o Machine learning Algorithms
o Machine learning use cases
o How to choose Machine Learning Algorithm
o Why to use decision tree algorithm learning
o Challenges and Limitations of Machine learning
o Application of Machine learning
o Why is machine learning important?

3. Machine Learning
3
Machine Learning is the result of General AI that involves developing machines
that can deliver results better than humans
Traditional
Programming
Data
(Input)
Program
Output
Data
(Input)
Output
Machine
Learning
Program
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“Learning”
Machine
Learning System
Input Data:
Feed Learner
Various Data
Output Data:
Present Rules

4. 7 Steps of Machine Learning
4
Gathering Data
Preparing that Data Choosing a Model
Training Evaluation
Hyperparameter Tuning Prediction
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5. Machine Learning vs. Traditional Programming
5
Traditional Modelling
Prediction
Result
Machine Learning
Learning
Model
Prediction
Result
Computer
New Data
Model
Sample Data
Expected
Result
Data
Handcrafted
Model
Computer
Computer
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6. How does Machine Learning Work?
6
Identify, the problem to
be solved and create a
clear objective.
Define Objectives
Preparing data is a crucial
step and involves building
workflows to clean, match and
blend the data.
Prepare Data
Data is fed as input and the
algorithm configured with the
required parameters. A
percent of the data can be
utilized to train the model.
Train Model
Publish the prepared
experiment as a web
service, so applications can
use the model.
Integrate Model
Collect data from hospitals,
health insurance companies,
social service agencies, police
and fire dept.
Collect Data
Depend on the problem to be solved
and the type of data an appropriate
algorithm will be chosen.
Select Algorithm
The remaining data is utilized to test the
model, for accuracy. Depending on the
results, improvements, can be performed in
the “Train model’ and/or “Select Algorithm”
phases, iteratively.
Test Model
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7. Machine Learning Algorithms
7
Machine Learning
o Linear
o Polynomial
o KNN
o Trees
o Logistic Regression
o Naïve-Bayes
o SVM
Regression
Decision Tree
Random Forest
Clustering
o SVD
o PCA
o K-means
o Apriori
o FP-Growth
Continuous
Categorical
Reinforcement
Classification
Association
Anlaysis
Hidden Markov
Model
Supervised Unsupervised
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8. Machine Learning Use Cases
8
Energy Feedstock &
Utilities
o Power Usage Analytics
o Seismic Data
Processing
o Your Text Here
o Smart Grid
Management
o Energy Demand &
Supply Optimization
Financial
Services
o Risk Analytics &
Regulation
o Customer Segmentation
o Your Text Here
o Credit Worthiness
Evaluation
Travel &
Hospitality
o Aircraft Scheduling
o Dynamic Pricing
o Your Text Here
o Traffic Patterns &
Congestion
Management
Manufacturing
o Predictive
Maintenance or
Condition Monitoring
o Your Text Here
o Demand Forecasting
o Process Optimization
o Telematics
Retail
o Predictive Inventory
Planning
o Recommendation
Engines
o Your Text Here
o Customer ROI &
Lifetime Value
Healthcare &
Life Sciences
o Alerts & Diagnostics
from Real-time Patient
Data
o Your Text Here
o Predictive Health
Management
o Healthcare Provider
Sentiment Analysis
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9. How to Choose Machine Learning Algorithm
9
How to Select Machine Learning Algorithms
What do you want to
do with your Data?
How to Select Machine Learning Algorithms
Algorithm Cheat Sheet
Additional
Requirements
Accuracy Linearity Number of
Parameters
Training
Time
Number of
Features
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10. Why use Decision Tree Machine Learning Algorithm?
10
Decision Trees
To Classify
Decision Tree
Non-linear Relationship
between Predictors &
Response
Linear Relationship
between Predictors
& Response
Use c4.5
Implementation
Use Standard
Regression Tree
Responsible
Variable has only
2 Categories
Response Variable has
Multiple Categories
Use Standard
Classification here
Use c4.5
Implementation
To Predict
Responsible
variable is
Continuous
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11. Challenges and Limitations of Machine learning
11
Advantages Disadvantages
Handling multi-dimensional & multi-
variety Data
Data
Acquisition
High error-
Susceptibility
Time and
Resources
Interpretation
Results
Easily Identifies Trends and Patterns
No Human Intervention needed
Continuous Improvement
Wide Applications
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12. Application of Machine Learning
12
Automatic Language
Translation
Medical Diagnosis
Stock Market Trading
Online Fraud Detection
Virtual Personal Assistant
Email Spam and Malware Filtering
Self Driving Cars
Product Recommendations
Traffic Prediction
Speech Recognition
Image Recognition
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13. Why is Machine Learning Important?
13
Phase 1 : Learning
Training
Data
o Normalization
o Dimension Reduction
o Image Processing, etc.
Pre-Processing
o Supervised
o Unsupervised
o Minimization, etc.
o Precision/recall
o Over fitting
o Test/cross Validation
data, etc.
Error Analysis
Model
New Data
Learning
Predicted Data
Prediction
Phase 2: Prediction
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