Machine Learning algorithms, uses, applications and advancement of technologies Session by Artivatic AI Data Labs. This session was on more practical applications of ML in real life.

1. AI/ML Workshop
By Artivatic Data Labs Team
In Association with
21 Jan 2017
(By Ananth G, Omkar & Layak)
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2. Content
 Requirements For Making Good AI
 ML Algorithms
 Classification Algorithms
 Clustering Algorithms
 Advance ML Algorithms
 Reference
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3. Requirements For Making
Good AI
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Data Preparation
AI Algorithm and Model Creation
Autonomous and Interactive Learning
Scalability

4. Machine Learning Algorithms
• Categories of ML algorithms
• Classification / Supervised Learning
• Clustering / Unsupervised Learning
• Supervised algorithms can apply what has been
learned in the past to new data.
• Unsupervised algorithms can draw inferences from
datasets.
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5. Classification
 Classifies data into given set of predefined
categories.
 Supervised
 Accuracy depends on the training data set.
Training
Dataset
New
Data
Class of
new dataThis confidential document is legal property of Artivatic Data Labs Private Limited.
Classification
Algorithm
Classification
Rule

6. Where To Use Classification
 Fraud Detection
 Machine Vision
 Natural-Language Processing(NLP)
 Bioinformatics
 Healthcare and Disease Prediction
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7. Classification Algorithms
 Naive Bayes
• Use Bayes’ theorem,
• Where,
 A and B are events and P(B) ≠ 0
 P(A) and P(B) are the probabilities of observing A and B without regard to
each other.
 P(A | B), a conditional probability, is the probability of observing event A
given that B is true.
 P(B | A) is the probability of observing event B given that A is true
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8. • Neural Networks
• Based on the structure and functions of biological neural networks.
• Every link between two neurons are weighted
• Theoretically we can design any level of the Neural Network
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9. This confidential document is legal property of Artivatic Data Labs Private Limited.

10. • Some Common Classification Algorithms
• Nearest Neighbour Algorithms
• Support-Vector Machines
• Decision Tree Classifier
• Hierarchical Classifier
• Random Forest
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11. Clustering
 Creating clusters depends on the similarity between
given data.
 Unsupervised
 Data within a cluster are more similar than the data
between different clusters.
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12. Where To Use Clustering
 Analysis of Human Personality
 Market segment
 Finance Sector Analysis
 City Planning
 Global Climate
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13. Clustering Algorithm
• K-means clustering
• Create K clusters depending on distance between
individual data points
• Distance between intra-cluster points are lower than
distance between inter-cluster points
• Easy to Implement
• High Time Complexity
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14. K-mean Algorithm
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15. • DBSCAN
• Density-Based Spatial Clustering of Applications
with Noise
• Capable of ignoring Noise
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16. Advance Machine Learning Algorithm
 Artificial Neural Networks
 Deep Learning
 Genetic Algorithms
 Competitive Learning
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17. Any Questions?
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18. Thank You.
Write to us: contact@Artivatic.com
Visit Us:
Artivatic Data Labs Private Limited
25 & 26, 2nd Floor, AVS Compound, 80 Feet Road, Koramangala 4th Block, Bangalore -34
Website: www.Artivatic.com