Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
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
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Index.....................
History of Machine Learning.
What is Machine Learning.
Why ML.
Learning System Model.
Training and Testing.
Performance.
Algorithms.
Machine Learning Structure.
Application.
Conclusion.
----------------------------------------------
THANK YOU
Half day session on Machine learning and its applications. It introduces Artificial Intelligence, move on Machine Learning, applications, algorithms, types, using Cloud for ML, Deep Learning and some resources to start with
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
Though the term machine learning has become very visible in
the popular press over the past few years—making it appear to be the newest shiny object—the technology has actually been
in use for decades. In fact, machine learning algorithms such as decision trees are already in use by many organizations for predictive analytics.
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
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Index.....................
History of Machine Learning.
What is Machine Learning.
Why ML.
Learning System Model.
Training and Testing.
Performance.
Algorithms.
Machine Learning Structure.
Application.
Conclusion.
----------------------------------------------
THANK YOU
Half day session on Machine learning and its applications. It introduces Artificial Intelligence, move on Machine Learning, applications, algorithms, types, using Cloud for ML, Deep Learning and some resources to start with
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
Though the term machine learning has become very visible in
the popular press over the past few years—making it appear to be the newest shiny object—the technology has actually been
in use for decades. In fact, machine learning algorithms such as decision trees are already in use by many organizations for predictive analytics.
These are some general ideas to get one started with "Machine Learning".Machine learning is a vast subject in the field of computer science & needs intense research to master.
Data Mining vs. Machine Learning Unveiling Major DifferencesCapital Numbers
Organizations around the globe are making the most out of modern technologies, including data mining and machine learning.
Let’s look at the example to help you elaborate more on the business application of both techniques. Let’s find out the meaning and differences between data mining and machine learning.
For more such insights, click:
https://www.capitalnumbers.com/blog
Machine Learning in Business What It Is and How to Use ItKashish Trivedi
Machine learning revolutionizes business by offering effective suggestions, accurate predictions, and advanced analytics, streamlining operations without extensive human effort. It's a process where AI learns autonomously, akin to human cognition, as demonstrated by DeepMind, learning from images and sounds without explicit labeling. This article delves into the essence of machine learning, showcasing its benefits, diverse business applications, various types, and real-world examples. Understanding these facets is key to harnessing its power in optimizing businesses and enhancing customer experiences.
Machine Learning: The First Salvo of the AI Business RevolutionCognizant
Machine learning (ML), a branch of artificial intelligence (AI), is coming into its own as a force in the business landscape, performing a variety of innovative and highly skilled activities that enhance customer experience and offer market advantages. This is a brief guide to getting started with ML, the thinking, tools and frameworks to make it a powerful business tool.
Machine learning applications nurturing growth of various business domainsShrutika Oswal
Machine learning is a science in which machines are becoming smarter and helping humans to make the best decisions based on previous data recommended practices. This technique is not new but is occupying fresh momentum. Machine Learning Algorithm learns from the previous records and analyses the data. Without any human interrupt, it will generate its own recommendation. A machine will add that recommendation as experience in its database and use it for further processing. In short, the machine learns from its own experience and gives you better and better output.
Machine learning is an iterative process as the more data added to machines learn from fresh feeds of data and then independently adapt new features to handle new data without constant human intervention. Machine learning was earlier used to predict what’s happing with the business but now the machine learning algorithm will suggest what action needs be taken by moving our business forward.
This PowerPoint presentation presents the results of a literature survey of machine learning applications nurturing the growth of various business domains. More specifically, it gives a brief introduction of Machine Learning, four major types of Machine Learning, enhancement in various business domains by the use of various machine learning algorithms.
BIG DATA AND MACHINE LEARNING
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
Machine learning and remarketing are two very popular ways of enhancing marketing campaigns. Used in tandem, they can deliver much better business outcomes. This session reveals how to get started with machine learning-driven remarketing using R.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
2. Introduction to Machine Learning
Machine learning is a method of data analysis that automates
analytical model building. Machine learning is a type of artificial
intelligence (AI) that, according to Arthur Samuel in 1959, gives
"computers the ability to learn without being explicitly
programmed.“Using algorithms that iteratively learn from data,
machine learning allows computers to find hidden insights without
being explicitly programmed where to look.
3. As I am a Data Scientist One may ask me a questions like
Why you should care about Data ?
Can’t you just take a representative sample and do statistical computation on
it ?
Anaswer Is:~
Machine Learning
ML focuses on learning by example - and the more example you have, the
better the learner.
It has been said that “more data usually beats better algorithms”.
4. Why machine learning is important?
Machine learning has several very practical applications that drive the kind of real business results –
such as time and money savings – that have the potential to dramatically impact the future of your
organization.
Things like growing volumes and varieties of available data, computational processing that is cheaper
and more powerful, and affordable data storage are main causes for the importance of machine
learning.
Data availability: Today, the amount of digital data generated through smart devices and Internet of
Things is huge . This data can be used for analysis to make intelligent decisions and Machine
Learning helps in doing so.
Computation power: Moore's law has ensured that the current hardware has the capability to
reliably store and analyze the massive data and perform massive amount of computations in a
reasonable amount of time. This allows to build complex Machine Learning models with billions of
parameters.
Moreover it can be said that is provides High-value predictions that can guide better decisions and
smart actions in real time without human intervention.
5. Fields of Application
Some of the fields where machine learning is used are as follows:
Financial services
Government agencies
Health care
Marketing and sales
Oil and gas
Transportation
Telecom
Retail etc.
7. Supervised Learning
This kind of a learning is possible at instances when the inputs and the outputs
are clearly identified, and algorithms are trained using labeled examples.
The learning algorithm receives a set of inputs along with the corresponding
correct outputs, and the algorithm learns by comparing its actual output with
correct outputs to find errors. Based on this, it would further modify the model
accordingly. This is a form of pattern recognition, as supervised learning
happens through methods like classification, regression, prediction and gradient
boosting, supervised learning uses patterns to predict the values of the label on
additional unlabeled data.
Supervised learning is commonly used in applications where historical data
predicts likely future events.
8. Real life example of supervised learning
Loan Status Prediction in banking
sector :A Company wants to automate
the loan eligibility process (real time)
based on customer detail provided while
filling online application form. These
details are Gender, Marital Status,
Education, Income, Loan Amount,
Credit History and others. To automate
this process, they have given a problem
to identify the customers segments, those
are eligible for loan amount so that they
can specifically target these customers.
Skills:
• Concordance, Information
• Value, Weight of Evidence,
• C-Statistic, H-L Stat, Gini,
• K-S, Somer’s D, RMSE, CP.
Statistical model:
• Logistic Model
• Decision Tree
• Random Forest etc.
9. Real life example of supervised learning
Churn Prediction:
Telecommunication market is
expanding day by day and thereby
due to growing competition
companies are facing loss of
customers and thereby a severe
loss in revenue. The customers
who are leaving the company and
moving to the other telecom
companies are called Churn.
Skills:
• Concordance, Information
• Value, Weight of Evidence,
• C-Statistic, H-L Stat, Gini,
• K-S, Somer’s D, RMSE, CP.
Statistical model:
• Logistic Model
• Decision Tree
• Survival Analysis
10. Unsupervised Learning
Unlike supervised learning, unsupervised learning is used against data that has
no historical data. The goal is to explore the data and find some structure
within.
Unsupervised learning works best on transactional data.
Popular techniques include self-organizing maps, nearest-neighbor mapping, k-
means clustering and singular value decomposition. These algorithms are also
used to segment text topics, recommend items and identify data outliers.
11. Real life example of unsupervised learning
Market Basket Analysis:
Nowadays all of we are familiar
with online retailers like flipkart,
Amazon etc. Now what they do,
they suggest some relevant
products on purchase of some
particular product. Identifying
products and content that go well
together. Using it, retailers get a
window into customers'
purchasing behavior.
Statistical Skills:
Association Rule . (Support,
Confidence, Lift)
Statistical Algorithm:
Apriori Algorithm
Collaborative filtering
12. Semi-supervised Learning
As the name suggests, semi-supervised learning is a bit of both – supervised
and unsupervised learning and uses both labeled and unlabeled data for training.
In a typical scenario it would use small amount of labeled data with large
amount of unlabeled data, the reason being that, unlabeled data is less
expensive and takes less effort to acquire.
This type of learning can be used with methods such as classification,
regression and prediction.
Semi-supervised learning is useful when the cost associated with labeling is too
high to allow for a fully labeled training process.
13. Real life example of semi-supervised learning
• One real world application for semi-
supervised learning, is webpage
classification. Say you want to classify
any given webpage into one of several
categories (like "Educational", "
Shopping", "Forum", etc.). This is a
case where it's expensive to go through
tens of thousands of webpages and
have humans annotate them (imagine
how boring and strenuous it would be).
However, in terms of availability,
webpages are abundant. Simply write a
Python/Java/etc. crawler, and you can
collect millions of pages in a few hours.
• In-depth analysis of product
reviews in retail: Suppose a
manufacturing company want to
analyze the rating and review of a
certain product to get a view about
the popularity of the product for
improving the quality of the
product or launch a better product.
Statistical model:
• Latent semantic analysis
• Support vector machine
14. Reinforcement Learning
This is a bit similar to the traditional type of data analysis as the algorithm
discovers through trial and error and decides which action results in greater
rewards.
This type of learning has three primary components: the agent (the learner or
decision maker), the environment (everything the agent interacts with) and
actions (what the agent can do).
The objective is for the agent to choose actions that maximize the expected
reward over a given amount of time. This is best achieved when the agent has a
good policy in hand. Learning the best policy, hence remains to be the goal in
reinforcement learning.
15. Real life example of reinforcement learning
• Optimization of anemia
management in patients
undergoing hemodialysis.
This is a relevant problem in
Nephrology, in which we focus on
obtaining the optimal
Erythropoietin (EPO) dosages that
should be administered for an
adequate longterm anemia
management.
• Optimization of a marketing
campaign.
In this case, we used data from a
marketing campaign to suggest
modifications based on RL to the
company policy in order to
maximize long-term profits.
16. Industry Figure
Global IT companies are looking into Analytics as well as ML as a next
generation growth engine.
Banking Sector and financial services globally are using ML to support
their own business.
Many Academic sector also uses Data Science, ML for statistical computing.
ML is not a decision making system , is a decision supporting system.
17. Role of a ML Expert
Documenting the types and structure of the business data (logical modeling).
Analyzing and mining business data to identify patterns and correlations
among the various data points.
Mapping and tracing data from system to system in order to solve a given
business or system problem.
Design and create data reports and reporting tools to help business
executives in their decision making.
Perform statistical analysis of business data.
18. Limitations of Machine Learning
Each narrow application needs to be specially trained
Require large amounts of structured training data
Learning must generally be supervised: Training data must be tagged
Do not learn incrementally or interactively, in real time
Poor transfer learning ability, re-usability of modules, and integration
19. Pros and cons of machine learning
Pros
Good for document level
High recall
Robust
Easy to scale
Fast development
Feature learning
Prameter Optimization
Cons
Requires large annotation
Course-grained
Difficult to debug
Fail in short messages
Only shallow NLP
Works with continuous loss function
Limited
Large data requirement