This document provides an overview and introduction to the Apache Mahout machine learning library. It discusses how Mahout can be used to build scalable machine learning applications using algorithms like collaborative filtering, clustering, classification and frequent pattern mining. It also describes how Mahout can be used for recommendation engines and document classification. The document concludes with references and resources for learning more about Mahout.
NLP & Machine Learning - An Introductory Talk Vijay Ganti
An Introductory talk with the goal of getting people started on the NLP/ML journey. A practitioner's perspective. Code that makes it real and accessible.
NLP & Machine Learning - An Introductory Talk Vijay Ganti
An Introductory talk with the goal of getting people started on the NLP/ML journey. A practitioner's perspective. Code that makes it real and accessible.
Chatbots that use image recognition technology will become increasingly common, allowing users to interact with them using images and photos. For example, a chatbot that uses image recognition technology could help users identify objects or products in a photo and provide relevant information or recommendations. As augmented reality (AR) technology becomes more advanced, we can expect to see chatbots that are designed specifically for AR environments.
In this Python Machine Learning Tutorial, Machine Learning also termed ML. It is a subset of AI (Artificial Intelligence) and aims to grants computers the ability to learn by making use of statistical techniques. It deals with algorithms that can look at data to learn from it and make predictions.
AI Training in Lucknow, If you're looking for one of the best Artificial Intelligence Training programs in Lucknow, then you'll want to check out Innovitt Global. They offer a comprehensive AI training program that covers everything from the basics of AI to more advanced concepts. The program is designed to help students and working professionals alike to better understand and utilize AI in their field. With over 20 years of experience in the industry, Innovitt Global is one of the leading providers of AI training programs. Their programs are highly respected and well-recognized by both students and professionals alike. When it comes to choosing an AI training provider, you can't go wrong with Innovitt Global. Visit our website to apply for a best AI Training in Lucknow- https://trainingatinnovittglobal.com/ai-training-in-lucknow/
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...Edureka!
Machine Learning Training with Python: https://www.edureka.co/python )
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Testing of artificial intelligence; AI quality engineering skils - an introdu...Rik Marselis
Testing of AI will require a new skillset related to interpreting a system’s boundaries or tolerances. Indeed, as our paper points out, the complex functioning of an AI system means, amongst other things, that the focus of testing shifts from output to input to verify a robust solution. Also we introduce the 6 angles of quality for Artificial Intelligence and Robotics.
This paper was written by Humayun Shaukat, Toni Gansel and Rik Marselis.
Agile Network India | Agility Day @Noida | Enterprise agility through enginee...AgileNetwork
Abstract:
In this era of the digital world, all walk of lives is driven (controlled /influenced) by technology. With the maturity of DevOps, Technology priorities have become more pragmatic to serve business outcomes. One of the most noticeable evolutions in this area is “Code”. Yes, that’s right, it’s no more software as code but Configuration as code, Infrastructure as code, Pipeline as code, Design as code and so on… No exaggeration in describing, Everything as Code !
Now it’s time to manage this code, all these artifacts can be treated like software code and follow the same software development lifecycle. Will take this discussion on how to achieve the scale of code quality in this time of open source, Code contribution in a diversified ecosystem, beyond the boundaries of your team/company. Will share framework /best practices of higher code quality and how it helps in enabling a higher quality of life.
Key Takeaways:
1. Code as a foundation of everything we do
2. Factors/framework on scaling code quality
3. Startup mindset aka differentiator on products/services being provided.
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Facebook: https://www.facebook.com/edurekaIN/
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District Data Labs Workshop
Current Workshop: August 23, 2014
Previous Workshops:
- April 5, 2014
Data products are usually software applications that derive their value from data by leveraging the data science pipeline and generate data through their operation. They aren’t apps with data, nor are they one time analyses that produce insights - they are operational and interactive. The rise of these types of applications has directly contributed to the rise of the data scientist and the idea that data scientists are professionals “who are better at statistics than any software engineer and better at software engineering than any statistician.”
These applications have been largely built with Python. Python is flexible enough to develop extremely quickly on many different types of servers and has a rich tradition in web applications. Python contributes to every stage of the data science pipeline including real time ingestion and the production of APIs, and it is powerful enough to perform machine learning computations. In this class we’ll produce a data product with Python, leveraging every stage of the data science pipeline to produce a book recommender.
Webinar: Using GenAI for Increasing Productivity in PM by Amazon PM LeaderProduct School
In this webinar, you will learn how AI can take work off your plate, allowing you to focus on deep thinking or critical work. Cut out the drudge work in Product Management and get more out of your day.
Learnings:
Improve workflows that are high frequency - "manual tasks"
Increase the quality of output that has high importance - "brainy tasks"
Put GenAI to work today
Chatbots that use image recognition technology will become increasingly common, allowing users to interact with them using images and photos. For example, a chatbot that uses image recognition technology could help users identify objects or products in a photo and provide relevant information or recommendations. As augmented reality (AR) technology becomes more advanced, we can expect to see chatbots that are designed specifically for AR environments.
In this Python Machine Learning Tutorial, Machine Learning also termed ML. It is a subset of AI (Artificial Intelligence) and aims to grants computers the ability to learn by making use of statistical techniques. It deals with algorithms that can look at data to learn from it and make predictions.
AI Training in Lucknow, If you're looking for one of the best Artificial Intelligence Training programs in Lucknow, then you'll want to check out Innovitt Global. They offer a comprehensive AI training program that covers everything from the basics of AI to more advanced concepts. The program is designed to help students and working professionals alike to better understand and utilize AI in their field. With over 20 years of experience in the industry, Innovitt Global is one of the leading providers of AI training programs. Their programs are highly respected and well-recognized by both students and professionals alike. When it comes to choosing an AI training provider, you can't go wrong with Innovitt Global. Visit our website to apply for a best AI Training in Lucknow- https://trainingatinnovittglobal.com/ai-training-in-lucknow/
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...Edureka!
Machine Learning Training with Python: https://www.edureka.co/python )
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Testing of artificial intelligence; AI quality engineering skils - an introdu...Rik Marselis
Testing of AI will require a new skillset related to interpreting a system’s boundaries or tolerances. Indeed, as our paper points out, the complex functioning of an AI system means, amongst other things, that the focus of testing shifts from output to input to verify a robust solution. Also we introduce the 6 angles of quality for Artificial Intelligence and Robotics.
This paper was written by Humayun Shaukat, Toni Gansel and Rik Marselis.
Agile Network India | Agility Day @Noida | Enterprise agility through enginee...AgileNetwork
Abstract:
In this era of the digital world, all walk of lives is driven (controlled /influenced) by technology. With the maturity of DevOps, Technology priorities have become more pragmatic to serve business outcomes. One of the most noticeable evolutions in this area is “Code”. Yes, that’s right, it’s no more software as code but Configuration as code, Infrastructure as code, Pipeline as code, Design as code and so on… No exaggeration in describing, Everything as Code !
Now it’s time to manage this code, all these artifacts can be treated like software code and follow the same software development lifecycle. Will take this discussion on how to achieve the scale of code quality in this time of open source, Code contribution in a diversified ecosystem, beyond the boundaries of your team/company. Will share framework /best practices of higher code quality and how it helps in enabling a higher quality of life.
Key Takeaways:
1. Code as a foundation of everything we do
2. Factors/framework on scaling code quality
3. Startup mindset aka differentiator on products/services being provided.
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
District Data Labs Workshop
Current Workshop: August 23, 2014
Previous Workshops:
- April 5, 2014
Data products are usually software applications that derive their value from data by leveraging the data science pipeline and generate data through their operation. They aren’t apps with data, nor are they one time analyses that produce insights - they are operational and interactive. The rise of these types of applications has directly contributed to the rise of the data scientist and the idea that data scientists are professionals “who are better at statistics than any software engineer and better at software engineering than any statistician.”
These applications have been largely built with Python. Python is flexible enough to develop extremely quickly on many different types of servers and has a rich tradition in web applications. Python contributes to every stage of the data science pipeline including real time ingestion and the production of APIs, and it is powerful enough to perform machine learning computations. In this class we’ll produce a data product with Python, leveraging every stage of the data science pipeline to produce a book recommender.
Webinar: Using GenAI for Increasing Productivity in PM by Amazon PM LeaderProduct School
In this webinar, you will learn how AI can take work off your plate, allowing you to focus on deep thinking or critical work. Cut out the drudge work in Product Management and get more out of your day.
Learnings:
Improve workflows that are high frequency - "manual tasks"
Increase the quality of output that has high importance - "brainy tasks"
Put GenAI to work today
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
When stars align: studies in data quality, knowledge graphs, and machine lear...
Mahout Tutorial FOSSMEET NITC
1. Practical Machine Learning
A Tutorial on Apache Mahout
Biju B
NLP R&D Division
365Media Pvt. Ltd.
bijub@365Media.in
FOSSMEET NITC,
Calicut
4-6 February 2011
Biju B & Jaganadh G Practical Machine Learning
2. nlp r d $ whoweare
Working in Natural Language Processing (NLP), Machine Learning,
Data Mining
Passionate about Free and Open source :-)
When gets free time teaches Python and blogs at
http://jaganadhg.freeflux.net/blog and contributes to
Openstreetmap
Works for 365Media Pvt. Ltd. Coimbatore India.
twitter handle : @jaganadhg, @bijub
Biju B & Jaganadh G Practical Machine Learning
3. Machine Learning
Machine Learning
Machine learning is a subfield of artificial intelligence (AI) concerned with
algorithms that allow computers to learn.
Biju B & Jaganadh G Practical Machine Learning
4. Machine Learning
Machine Learning
Machine learning is a subfield of artificial intelligence (AI) concerned with
algorithms that allow computers to learn.
Biju B & Jaganadh G Practical Machine Learning
5. Machine Learning
Machine Learning
Machine learning is a subfield of artificial intelligence (AI) concerned with
algorithms that allow computers to learn.
This talk is not aimed to give introduction about Machine Learning
Biju B & Jaganadh G Practical Machine Learning
6. Machine Learning
Machine Learning
Machine learning is a subfield of artificial intelligence (AI) concerned with
algorithms that allow computers to learn.
This talk is not aimed to give introduction about Machine Learning
Dont expect some mathy equations here
Biju B & Jaganadh G Practical Machine Learning
7. Machine Learning and Our Life
Do you think that Machine Learning has any impact in our life ??
Biju B & Jaganadh G Practical Machine Learning
8. Machine Learning and Our Life
Do you think that Machine Learning has any impact in our life ??
Yes
Biju B & Jaganadh G Practical Machine Learning
9. Machine Learning and Our Life
Do you think that Machine Learning has any impact in our life ??
Yes
In our day to day life we may use many Machine Learning powered
tools
Biju B & Jaganadh G Practical Machine Learning
10. Machine Learning and Our Life
Do you think that Machine Learning has any impact in our life ??
Yes
In our day to day life we may use many Machine Learning powered
tools
Recommendation Engines
Biju B & Jaganadh G Practical Machine Learning
11. Machine Learning and Our Life
Do you think that Machine Learning has any impact in our life ??
Yes
In our day to day life we may use many Machine Learning powered
tools
Recommendation Engines
Clustering
Biju B & Jaganadh G Practical Machine Learning
12. Machine Learning and Our Life
Do you think that Machine Learning has any impact in our life ??
Yes
In our day to day life we may use many Machine Learning powered
tools
Recommendation Engines
Clustering
Classification , Spam Filtering
Biju B & Jaganadh G Practical Machine Learning
13. Machine Learning and Our Life
Do you think that Machine Learning has any impact in our life ??
Yes
In our day to day life we may use many Machine Learning powered
tools
Recommendation Engines
Clustering
Classification , Spam Filtering
Sentiment Analysis
Biju B & Jaganadh G Practical Machine Learning
14. Machine Learning and Our Life
Do you think that Machine Learning has any impact in our life ??
Yes
In our day to day life we may use many Machine Learning powered
tools
Recommendation Engines
Clustering
Classification , Spam Filtering
Sentiment Analysis
Fraud Detraction
Biju B & Jaganadh G Practical Machine Learning
15. Mahout
Mahout
Open Source project by Apache Foundation
Goal of this project is to build scalable machine learning libraries
Biju B & Jaganadh G Practical Machine Learning
16. Mahout
Mahout
Mahout: a person who drives elephant ;-)
The name comes from the project’s use of Apache Hadoop.
Biju B & Jaganadh G Practical Machine Learning
17. Why a new library ?
There are more than 30 Java libraries/ tools available for Machine
Learning.
Weka , Mallet, Classifier4j, Rapidminer ........
Large Amount of data processing is not an easy task
Machine Learning tools are supposed to produce quick results
If the amount of data is too large it is not easy to process with a
single machine (Even if it is powerful)
Mahout is scalable: the core algorithms in Mahout are implemented
on top of Apache Hadoop using the map/reduce paradigm
Biju B & Jaganadh G Practical Machine Learning
19. Algorithms in Apache Mahout
Collaborative Filtering
Biju B & Jaganadh G Practical Machine Learning
20. Algorithms in Apache Mahout
Collaborative Filtering
User and Item based recommenders
Biju B & Jaganadh G Practical Machine Learning
21. Algorithms in Apache Mahout
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Biju B & Jaganadh G Practical Machine Learning
22. Algorithms in Apache Mahout
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Mean Shift clustering
Biju B & Jaganadh G Practical Machine Learning
23. Algorithms in Apache Mahout
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Mean Shift clustering
Dirichlet process clustering
Biju B & Jaganadh G Practical Machine Learning
24. Algorithms in Apache Mahout
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Mean Shift clustering
Dirichlet process clustering
Latent Dirichlet Allocation
Biju B & Jaganadh G Practical Machine Learning
25. Algorithms in Apache Mahout
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Mean Shift clustering
Dirichlet process clustering
Latent Dirichlet Allocation
Singular value decomposition
Biju B & Jaganadh G Practical Machine Learning
26. Algorithms in Apache Mahout
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Mean Shift clustering
Dirichlet process clustering
Latent Dirichlet Allocation
Singular value decomposition
Parallel Frequent Pattern mining
Biju B & Jaganadh G Practical Machine Learning
27. Algorithms in Apache Mahout
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Mean Shift clustering
Dirichlet process clustering
Latent Dirichlet Allocation
Singular value decomposition
Parallel Frequent Pattern mining
Complementary Naive Bayes classifier
Biju B & Jaganadh G Practical Machine Learning
28. Algorithms in Apache Mahout
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Mean Shift clustering
Dirichlet process clustering
Latent Dirichlet Allocation
Singular value decomposition
Parallel Frequent Pattern mining
Complementary Naive Bayes classifier
Random forest decision tree based classifier
Biju B & Jaganadh G Practical Machine Learning
29. Recommendation
Filter information based on user preference
Searching a large set of people and finding a smaller set with tastes
similar to you
e.g :- Amazon’s book recommendation , Netflix movie
recommendation
Biju B & Jaganadh G Practical Machine Learning
30. Document Classification
Classify documents based on its content
e.g: - spam filtering,priority inbox
Biju B & Jaganadh G Practical Machine Learning
31. Demo
Building recommendations engines with Mahout
Document Classification with Mahout
Biju B & Jaganadh G Practical Machine Learning
32. Reference
Biju B & Jaganadh G Practical Machine Learning
33. Reference
Mahout in Action - Book by Sean Owen and Robin Anil, published
by Manning Publications.
Taming Text - By Grant Ingersoll and Tom Morton, published by
Manning Publications.
Introducing Apache Mahout - Grant Ingersoll - Intro to Apache
Mahout focused on clustering, classification and collaborative
filtering. https://www.ibm.com/developerworks/java/library/j-
mahout/index.html
Programming Collective Intelligence: Building Smart Web 2.0
Applications
http://www.amazon.com/Programming-Collective-Intelligence-
Building-Applications/dp/0596529325
Biju B & Jaganadh G Practical Machine Learning
34. Useful Resources
Apache Mahout Site http://mahout.apache.org/
Apache Mahout Mailing List user@mahout.apache.org
The code which I used for Mahout demo is available at
http://bitbucket.org/jaganadhg/blog/src/tip/bck9/java/
Twenty News Group data set
http://people.csail.mit.edu/jrennie/20Newsgroups/20news-
bydate.tar.gz
Biju B & Jaganadh G Practical Machine Learning
35. Questions ??
Biju B & Jaganadh G Practical Machine Learning
36. Acknowledgments
Thanks to :
Manning Publications for Review Copy of the book ”Mahout in
Action”
Apache Mahout mailing list members
Ted Dunning and Robin Anil for suggestions
@chelakkandupoda for review and criticism
Mukundhanchari R&D Director 365Media Pvt. Ltd. for support and
encouragement
Biju B & Jaganadh G Practical Machine Learning
37. Finally
Biju B & Jaganadh G Practical Machine Learning