This document provides an introduction to machine learning, including definitions, types, and case studies. It begins with an agenda and overview of artificial intelligence applications. It then defines machine learning as a field that allows computers to learn without being explicitly programmed. The main types of machine learning are described as supervised, unsupervised, semi-supervised, and reinforcement learning. Example case studies on Netflix recommendations, cancer diagnosis, and Amazon inventory are outlined. The document concludes with tips on prerequisites and resources for studying machine learning, including mathematics, programming tools, and course recommendations.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
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 Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
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.
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.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
This Edureka Machine Learning Algorithms tutorial will help you understand all the basics of machine learning and different kind of algorithms along with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is an Algorithm?
2. What is Machine Learning?
3. How is a problem solved using Machine Learning?
4. Types of Machine Learning
5. Machine Learning Algorithms
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Lecture1 introduction to machine learningUmmeSalmaM1
Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.
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.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
This Edureka Machine Learning Algorithms tutorial will help you understand all the basics of machine learning and different kind of algorithms along with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is an Algorithm?
2. What is Machine Learning?
3. How is a problem solved using Machine Learning?
4. Types of Machine Learning
5. Machine Learning Algorithms
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Lecture1 introduction to machine learningUmmeSalmaM1
Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.
IBM i & digital transformation - Presentation & basic demo
IBM Watson Studio, IBM DSX Local w/ Open Source (Spark) & IBM Technology (OpenPower, CAPI, NVLINK)
Intro to machine learning for web folks @ BlendWebMixLouis Dorard
Get a business understanding of ML by going through key concepts and concrete use cases that illustrate its possibilities for web-based companies.
In this presentation I introduce new technology that makes ML more accessible, and I explain in simple terms the limitations to what can be achieved. Finally, I discuss pragmatic considerations of real-world applications and I give a sneak peak at the Machine Learning Canvas — a framework for describing a predictive system that uses ML to provide value to its end user.
--
L'utilisation du Machine Learning s'est fortement développée ces dernières années, jusqu'à être présent aujourd'hui dans environ la moitié des applications que nous utilisons sur smartphone. Même s'ils n'ont pas connaissance du Machine Learning (ML), les utilisateurs d'applications mobile et web sont devenus demandeurs de fonctionnalités prédictives que le ML rend possibles. Par ailleurs, dans le cadre de l'entreprise, le ML représente un avantage compétitif important qui permet de valoriser ses data en les couplant à une intelligence machine.
Auparavant réservée aux grosses entreprises, cette technologie se démocratise grâce aux nouveaux outils de ML-as-a-Service et aux APIs de prediction. Afin d'en tirer profit, nous verrons ensemble les clés de compréhension du fonctionnement du machine learning, qui sous-tendent ses possibilités et ses limites. Nous verrons également comment amorcer son utilisation dans votre propre projet, au travers du Machine Learning Canvas qui permet de décrire un système où le ML est au cœur de la création de valeur.
Do you understand the differences between pattern recognition, artificial intelligence and machine learning? And most important, what they separately bring to the table? In this week’s webinar we will tackle the terminology and discuss its recent explosion of popularity, and also look at how the Ogilvy analytics team has applied machine learning methods to effectively answer client challenges and drive value.
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
This deck is from Interpol Conference 2017, these slides shows the holistic view of machine learning in cyber security for better organization readiness
Once you’ve made the decision to leverage AI and/or machine learning, now you need to figure out how you will source the training data that is necessary for a fully functioning algorithm. Depending on your use case, you might need a significant amount of training data, and you’ll want to consider how that is labeled and annotated too.
View Applause's webinar with Cognilytica principal analysts Ronald Schmelzer and Kathleen Walch, alongside Kristin Simonini, Applause’s Vice President of Product, as they tackle the modern challenges that today’s companies face with sourcing training data.
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.
Understand the five important FinTech Trends and how you can leverage those for your business. Discover how the FinTech trends integrate technology, process and products. International use cases will be discussed in this presentation. This is a must-view webinar for retail banking operations staff, wealth managers, and technology teams supporting banking business.
Join our webinar to hear how Consensus, a Target-owned subsidiary, utilizes AWS and Trifacta to prepare data for use in fraud detection algorithms. You’ll learn how self-service automated data wrangling can save your organization time and money, and tips for getting started with Trifacta’s solution, built for AWS.
.
Webinar attendees will learn:
- Why automating your data wrangling tasks can lead to greater data accuracy and more meaningful insights.
- How you can reduce your data preparation time by 60% and more with self-service data wrangling tools built for AWS.
- How easy it is to get started with machine learning solutions for data wrangling on the cloud.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
6. The Big Data Era
Data
• Large volumes
of data is
produced
everyday.
• Everyone has a
phone packed
with several
sensors.
Infrastructure
• The computing
power of GPUs
has increased
dramatically.
• Cloud providers
offer online
computing
(IaaS).
Services
• User
applications:
YouTube, Gmail,
Facebook,
Twitter.
• Online storage
available for
free or low cost.
7. Notable AI Achievements
ImageNet is a database
of 14 million images with
over 20,000 categories.
GPT-3 is a language
model with 175 billion
learning parameters.
9. Overlapping AI Related Terminology
• Artificial Intelligence (AI)
Trying to simulate human intelligence.
• Machine Learning (ML)
Learn by example from experience and historic
data.
• Deep Learning (DL)
Learn patterns using multi-layered data
processors.
• Data Science (DS)
Uses a variety of scientific methods, processes
and systems to solve problems involving data.
• Big Data
Analyze data sets that are too large or complex.
Artificial
Intelligence
Machine
Learning Data
Science
Deep
Learning
Big Data
11. What is Machine Learning
The subfield of computer science that “gives computers the ability to
learn without being explicitly programmed”.(Arthur Samuel, 1959)
Using previous data for answering future questions
Historic may contain answers or may not contain answers
Training Prediction
Labeled Unlabeled
12. Machine Learning vs. Traditional Programming
Traditional
Programming
Machine
Learning
Data
Rules
Answers
Data
Answers
Rules
Traditional Programming:
• Business requirements and data are analyzed.
• A set of hard-coded rules are programmed and
tested.
• Program process new data based on the coded
rules.
Machine Learning:
• Data and their labels (answers) are fed into a
model.
• Model “learns” useful features and frequent
patterns to predict answers.
• Trained model is used to “predict” answers for
new data.
13. • Traditional Approach:
Price = 1.2 x Area + 0.7 x # Bedrooms +
0.3 x # Bathrooms
Pricing formula is known
beforehand and is explicitly hard-
coded. The formula can be
deducted by manual analysis or
SME domain experience.
• Machine Learning:
Price = A x Area + B x # Bedrooms + C
x # Bathrooms
Pricing Formula is unknown at
the beginning and would need
the model to be trained to
“Learn” the formula attributes
A,B and C.
HOUSING PRICES
Estimate housing prices based on 3 features (properties):
Area of the House, Number of Bedrooms, Number of Bathrooms
14. Housing Prices
Area #Bedrooms #Bathrooms Price
130 3 1 1,200
160 3 2 1,500
90 2 1 900
…
Model
Hyperp
aramet
ers
Optimiz
ation
Price = A x Area + B x # Bedrooms
+ C x # Bathrooms
A = 1.247
B = 0.682
C = 0.319
Input Dataset:
Contains prepared
historic data of
actual house sales.
Model:
Model Learns appropriate
“Parameters” to “Fit” the
input data.
Output:
Parameters that completes the
formula and can be generalized to
predict unsold houses.
17. Supervised Learning
• Learn through examples collected from historic data.
• Examples contain the desired output (labels) that
will be predicted for future data.
• Is this a cat or a dog?
• Is this email a spam or not?
• What is the market value of a house given its area and
number of bedrooms?
Supervised
Unsupervised
Semi-
Supervised
Reinforcement
18. Supervised Learning
Output is continuous. Predicts
numerical values such as prices or
temperature.
Supervised
Unsupervised
Semi-
Supervised
Reinforcement
Regression
Classification
Output is discrete. Predicts
categorical labels such as: Cat or
Dog.
19. Unsupervised Learning
• Using historic data that has no labels.
• Discovers the intrinsic links of data.
• Group photos into 20 groups based on their metadata.
• Segment customer profiles based on their demographics
and purchase behavior.
• Find an anomaly in credit card usage patterns.
Supervised
Unsupervised
Semi-
Supervised
Reinforcement
20. Unsupervised Learning
• Useful for learning structure in the data (clustering)
or detecting outliers (anomaly).
Supervised
Unsupervised
Semi-
Supervised
Reinforcement Anomaly
21. Semi-Supervised Learning
• Historic data has a small amount of labeled data,
and a large amount of unlabeled data.
• The cost of manually labeling all data is prohibitive.
• The problem is initially treated as Unsupervised to group
data to different structure.
• After that, available labels are used to label entire
clusters.
Supervised
Unsupervised
Semi-
Supervised
Reinforcement
22. Reinforcement Learning
• An agent interacts with an environment and watches
the result of the interaction.
• Environment gives feedback via a positive or
negative reward signal.
• The agent learns to optimize its interactions to
maximize the reward.
• An autonomous vehicle learns to put safety first, minimize
ride time, and obey the rules of law.
• An stock trading agent can decide to buy, sell or hold
based on market status and transactions profit/loss.
Supervised
Unsupervised
Semi-
Supervised
Reinforcement
24. ML Productizing
AI-First AI-Inside
Actionable
Insights
AI tech is at the center and
is essential to the product
function. Examples: Virtual
assistants, Chatbots, self-
driving cars.
AI adds a useful function
that enhances user
experience. Example:
Recommendation engines,
process automation, Fraud
detection
AI leveraging data that you
collect to make informed
decisions.
Examples: Sales forecast,
Churn analysis
25. Case Study: Netflix Recommendation
Personalized recommendation using
Collaborative filtering
Scale:
• Volume: 13,612 titles (2019)
• Subs: 159 million (2020)
Results:
• High engagement rate, Low churn
• Personalization and recommendations
save Netflix more than $1Billion per
year.
26. Case Study: Infervision Cancer Diagnosis
Predominantly used in early-stage lung
cancer screening. Employs more than 50
deep learning algorithms to determine
each diagnosis
Scale:
• Trained using over 200,000 scans in
trials at 20 hospitals.
Results:
• Helped reduce the rate of missed cancer
diagnoses by 50 percent.
27. Case Study: Amazon Inventory Optimization
ML-powered inventory optimization
ensures that inventory preemptively
caters for forecast demands.
Scale:
• ship an average of 10 million packages
per day.
Results:
• Store 40% more inventory.
• Fulfill 1 and 2 days shipping on time.
30. Mathematics Study tips
• Probability Book
A First Course In Probability 9th ed.
• Mathematics for Machine Learning
3Blue1Brown
• Coursera: Mathematics for Machine Learning, by Imperial College of London
https://www.coursera.org/specializations/mathematics-machine-learning
• Book: Mathematics for Machine Learning
https://mml-book.github.io/
31. • Coursera: Machine Learning, offered by Stanford
https://www.coursera.org/learn/machine-learning
• YouTube: Stanford CS 229 – Machine Learning (Math focused)
https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
• Book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd
Edition
https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
Machine Learning Study tips