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Thank you for subscribing to Smash Notes weekly. Every update is a little different. If you see something you particularly like, please let me know. Click to see all the top choices for this week in one place. What is in this update? In no particular order:   Business:  Why would a multi billion dollar business be suing a small coffee shop, and what does it cost to defend yourself from a frivolous lawsuit? Startups:  Working on an idea, are you? What should you validate first? Ryan Hoover from Product Hunt shares his wisdom. It ain't easy. History: What's the big deal with the Second Amendment? Should we all have a gun already? Adam Conover started a new podcast called "Factually!" and on this episode he's invited a guest to talk about the Constitution, gun rights, and other fun history  things. Education: How do you educate your kids if you are a Billionaire? Josh Dahn, the head of Ad Astra, the school founded by Elon Musk, talks about their approach to creative teaching. It's fascinating, and I would almost work for Space X just to have my kids go there, almost. Machine Learning: Microsoft is tired of paying big bucks for data processing and is looking into new ways to do machine learning. They are calling it "Machine Teaching." What is it and why is it so much better?

What does the MLOps pipeline at London Based FinTech startup TrueLayer look like?   London Based Fintech start-up TrueLayer decided to use Machine Learning instead of a rule-based system in mid-2019 and in our 7th meetup we spoke to their lead data scientist Alex Spanos about everything that entailed.   During the meetup, we dove into how TrueLayer architected their MLOps pipeline for their Open Banking API: more specifically which tools they use and why, what prompted them to use machine learning, and how Alex sees the role of a Machine Learning Engineer. Alex has led the hiring process of Machine Learning Engineers and shared learnings on candidates and businesses alike.    Alex is the Lead Data Scientist at TrueLayer, focussing on building Open Banking API products powered by data. Prior to TrueLayer, he built predictive models in Financial Services, used social data to predict the \"next-big-thing\" in Fast Moving Consumer Goods and introduced Machine Learning techniques in subsurface imaging.   His academic background is in Applied Mathematics & Statistics.   Check out his blog entries for more info: https://blog.truelayer.com/improving-the-classification-of-your-transaction-data-with-machine-learning-c36d811e4257 https://alexiospanos.com/hiring-machine-learning-engineers-part-1/ https://alexiospanos.com/hiring-machine-learning-engineers-part-2/ Connect with Demetrios on LinkedIn:   https://www.linkedin.com/in/dpbrinkm/   Connect with Alex on Linkedin:   https://www.linkedin.com/in/alexspanos/ Join us on slack: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw

MLOps community meetup #56! Last Wednesday we talked to  Daniel Stahl, Head of Data and Analytic Platforms, Regions Bank. // Abstract: The Data Science practice has evolved significantly at Regions, with a corresponding need to scale and operationalize machine learning models. Additionally, highly regulated industries such as finance require a heightened focus on reproducibility, documentation, and model controls.  In this session with Daniel Stahl, we will discuss how the Regions team designed and scaled their data science platform using DevOps and MLOps practices.  This has allowed Regions to meet the increased demand for machine learning while embedding controls throughout the model lifecycle.  In the 2 years since the data science platform has been onboarded, 100% of data products have been successfully operationalized. // Bio: Daniel Stahl leads the ML platform team at Regions Bank and is responsible for tooling, data engineering, and process development to make operationalizing models easy, safe, and compliant for Data Scientists.   Daniel has spent his career in financial services and has developed novel methods for computing tail risk in both credit risk and operational risk, resulting in peer-reviewed publications in the Journal of Credit Risk and the Journal of Operational Risk. Daniel has a Masters in Mathematical Finance from the University of North Carolina Charlotte.      Daniel lives in Birmingham, Alabama with his wife and two daughters. ----------- Connect With Us ✌️-------------    Join our Slack community:  https://go.mlops.community/slack Follow us on Twitter:  @mlopscommunity Sign up for the next meetup:  https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Dan on LinkedIn: https://www.linkedin.com/in/daniel-stahl-6685a52a/ Timestamps: [00:00] Introduction to Ben Wilson [00:11] Ben's background in tech [01:17] "How do you do what I have always done pretty well which is being as lazy as possible in order to automate things that I hate doing. So I learned about Regression Problems." [03:40] Human aspect of Machine Learning in MLOps [05:51] MLOps is an organizational problem [09:27] Fragile Models [12:36] Fraud Cases [15:21] Data Monitoring [18:37] Importance of knowing what to monitor for [22:00] Monitoring for outliers [24:16] Staying out of Alert Hell [29:40] Ground Truth [31:25] Model vs Data Drift on Ground Truth Unavailability [34:25] Benefit to monitor system or business level metrics [38:20] Experiment in the beginning, not at the end [40:30] Adaptive windowing [42:22] Bridge the gap [46:42] What scarred you really bad?

This is the first episode of a podcast series on Machine Learning and Data privacy.  Machine Learning is the key to the new revolution in many industries. Nevertheless, ML does not exist without data and a lot of it, which in many cases results in the use of sensitive information. With new privacy regulations, access to data is today harder and much more difficult but, does that mean that ML and Data Science has its days counted? Will the Machines beat privacy?   Don't forget to subscribe to the mlops.community slack ( https://go.mlops.community/slack ) and to give a star to the Synthetic data open-source repo ( https://github.com/ydataai/ydata-synt... ) Useful links:   Medium post with the podcast transcription - https://medium.com/@fabiana_clemente/... In case you're curious about GDPR fines - enforcementtracker.com   The Netflix Prize - https://www.nytimes.com/2010/03/13/technology/13netflix.html Tensorflow privacy - https://github.com/tensorflow/privacy

Coffee Sessions #28 with Charlie You of Workday, Lessons learned from hosting the Machine Learning Engineered podcast //Bio Charlie You is a Machine Learning Engineer at Workday and the host of ML Engineered, a long-form interview podcast aiming to help listeners bring AI out of the lab and into products that people love. He holds a B.S. in Computer Science from Rensselaer Polytechnic Institute and previously worked for AWS AI. Charlie is currently working as a Machine Learning Engineer at Workday. He hosts the ML Engineered podcast, learning from the best practitioners in the world.   Check Charlie's podcast and website here: mlengineered.com https://cyou.ai/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Charlie on LinkedIn: https://linkedin.com/in/charlieyou/ Timestamps: [00:00] Introduction to Charlie You [01:50] Charlie's background on Machine Learning and inspiration to create a podcast [06:20] What's your experience been so far as the machine learning engineer and trying to put models into production and trying to get things out that has business value? [07:08] "I started the podcast because as I started working, I had the tingling that machine learning engineering is harder than most people thought, and like way harder than I personally thought." [08:20] What's an example of that where you target someone in your podcast, you keep that learning and you want an extra meeting the next day and say "Hey, actually I'm starting one of the world's experts on this topics and this is what they said"?    [10:06] In a world of tons of traditional software engineering assets and the process you put in place, how have they adopted what they're doing to the machine learning realm?    [19:00] About your podcast, what are some 2-3 most consistent trends that you've been seeing? [21:08] Instead of splintering so much as machine learning monitoring infrastructure specialist, are you going to departmentalize it in the future? [27:22] Is there such a thing as an MLOps engineer right now? [28:50] "We haven't seen a very vocal, very opinionated project manager in machine learning yet." - Todd Underwood [30:18] "Similarly with tooling, we haven't seen the emergence of the tools that encode those best practices." Charlie [31:42] "The day that you don't have to be a subject matter expert in machine learning to feel confident and deploy machine learning products, is the day that you will see the real product leadership in machine learning." Vishnu [34:12] I'd love to hear your take on some more trends that you've been seeing (Security and Ethics) [34:41] "Data Privacy and Security is always at the top of any consideration for infrastructure." Charlie [35:44] That's driven by legal requirements? How do you solve this problem? [37:27] How do we make sure that if that blows up, you're not left with nothing?   [42:28] In your conversations, have you seen people who goes with cloud provider? [43:25] Enterprises have much different incentives than startups do. [45:48] What are some used cases where companies are needing to service their entire needs? [45:48] What are some used cases where companies are needing to service their entire needs? [49:18] What are some takeaways that you had in terms of how you think about your career, what experiences you want to build as this MLOps based engineering is moving so fast?   [56:08] "Your edge is never in the algorithm"

The rapid shift in priorities among today's enterprises is leaving security professionals applying these zero trust- \"trust no-one, verify everything\"- principles to existing on-premises networks. In this episode, Sean's talking with Hed...

In this episode I am going to read Newfound's latest research paper, LIQUIDITY CASCADES: The Coordinated Risk of Uncoordinated Market Participants.This reading will refer to a number of figures within the paper, so I urge you to go to our website, thinknewfound.com, and download the PDF so you get better follow along.This paper is unlike any research we've shared in the past. Within we dive into the circumstantial evidence surrounding the \"weird\" behavior many investors believe markets are exhibiting. We tackle narratives such as the impact of central bank intervention, the growing scale of passive / indexed investing, and asymmetric liquidity provisioning.Spoiler: Individually, the evidence for these narratives may be nothing more than circumstantial. In conjunction, however, they share pro-cyclical patterns that put pressure upon the same latent risk: liquidity.In the last part of the paper we discuss some ideas for how investors might try to build portfolios that can both seek to exploit these dynamics as well as remain resilient to them.I hope you enjoy.

Tweet this Episode Tyler Renelle is a contractor and developer who has worked in various web technologies like Node, Angular, Rails, and much more. He's also build machine learning backends in Python (Flask), Tensorflow, and Neural Networks. The JavaScript Jabber panel dives into Machine Learning with Tyler Renelle. Specifically, they go into what is emerging in machine learning and artificial intelligence and what that means for programmers and programming jobs. This episode dives into: ? Whether machine learning will replace programming jobs ? Economic automation ? Which platforms and languages to use to get into machine learning ? and much, much more... Links: ? Raspberry Pi ? Arduino ? Hacker News ? Neural Networks (wikipedia) ? Deep Mind ? Shallow Algorithms ? Genetic Algorithms ? Crisper gene editing ? Wix ? thegrid.io ? Codeschool ? Codecademy ? Tensorflow ? Keras ? Machine Learning Guide ? Andrew Ng Coursera Course ? Python ? R ? Java ? Torch ? PyTorch ? Caffe ? Scikit learn ? Tensorfire ? DeepLearn.js ? The Singularity is Near by Ray Kurzweil ? Tensorforce ? Super Intelligence by Nick Bostrom Picks: Aimee ? Include media ? Nodevember ? Phone cases AJ ? Data Skeptic ? Ready Player One Joe ? Everybody Lies Tyler ? Ex Machina ? Philosophy of Mind: Brains, Consciousness, and Thinking Machines

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Objective: To map the evidence concerning the use of machine learning (ML) to predict treatment burden and outcomes in patients with nAMD, DMO, and RVO-associated macular oedema in order to understand the current state of the art and the gaps. Introduction: Neovascular age-related macular degeneration (nAMD), diabetic macular oedema (DMO), and retinal vein occlusion (RVO) associated macular oedema are some of the leading causes of visual loss globally.1 Intravitreal anti-vascular endothelial growth factor (VEGF) injections are currently used to treat such diseases.1 The treatment course and outcomes for each patient is variable and thus individualised treatment techniques are needed.2 Two of the most pressing issues are estimating treatment burden (e.g., patients who need fewer vs. more intravitreal injections) and visual prognosis (e.g., good vs. poor responders) in individual patients.2 This research aims to assess the potential of ML to predict treatment burden and outcomes to personalise anti-VEGF treatment in patients with nAMD, DMO, and RVO. We seek to understand the types of ML approaches that have been explored, their maturity, reported outcomes, and real-world applicability. Eligibility criteria: Studies will be included if they focus on ML algorithms that predict anti-VEGF treatment burden and/or outcomes in patients with nAMD, DMO, and RVO-associated macular oedema. The included studies will consist of full-length original research journal articles written or translated into English. We will exclude other types of literature (i.e., not full-length journal articles, not a report of original research and not centred on ML algorithms that predict anti-VEGF treatment burden and/or outcomes in patients with nAMD, DMO, and RVO-associated macular oedema). Publication dates of included studies will be from January 2000 onwards. Methods: Five academic databases will be searched (Ovid Medline, Embase, Web of Science Core Collection, IEEE, and ACM Digital Library). Search strings for each database were formulated in conjunction with a Massachusetts Eye and Ear, Harvard Medical School librarian. Studies will be uploaded to Covidence (literature management software), and duplicates will be removed. Studies will be screened by two independent screeners (SG & AYO). Disagreements will be resolved with a discussion with a third author (DM). SG will conduct a descriptive analysis of the data. We will use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) for reporting our study, and we will publish our protocol on the Open Science Framework (OSF).3 Results: We will report the number and type of included evidence, as well as any pertinent study characteristics. Main topics of interest and significant outcomes will also be presented. Inferences will be presented in an appropriate diagrammatic or tabular format. Conclusions: Based on the key concepts and topics extrapolated from the selected studies, the scoping review will aim to explore the breadth and depth of the current literature and address any knowledge gaps that can hold the potential to inform future research and future algorithm development. Keywords: Intravitreal injections, ophthalmology, artificial intelligence, systematic scoping review

ELI5: Explain Like I'm Five Year Old, More than anything we learn, the actual intuition frames up stronger when we could deliver to a 5-year-old. In this Unicast of ELI5, amplifying the content from the Book Grokking Machine Learning, Beautifully Unfolded the intuition behind Predictions and Machine learning with a cute story of a kid. Grab Grokking Machine Learning by Luis Serrano | Manning Publications . Start your free trial at sundog-education.com to kickstart your career in Data Science.

In this episode we discuss just one out of the 102 different posters which was presented on the first night of the 2015 Neural Information Processing Systems Conference. This presentation describes a system which can answer simple questions about images. Check out: www.learningmachines101.com for additional details!!

This is the first of a two-parter with Peter Varhol on both the promises and the hype surrounding AI and Machine Learning. Matt, Perze and Michael go down the rabbit hole on the Machine Learning topic with Peter as we try to wrap our heads around both the realities of Machine Learning, AI and the unique testing challenges such systems offer. From Facebook's Chatbots negotiating an agreement to systems making predictive suggestions in ways that are both intriguing and creepy. There is a lot to the machine learning puzzle that we are just starting to understand and also prepare ourselves to effectively test. Hint: the algorithms themselves are only part of the puzzle. Resource by QualiTest Group

Application Programming Interfaces (APIs) are interfaces that enable multiple software applications to send and retrieve data from one another. They are commonly used for retrieving, saving, editing, or deleting data from databases, transmitting data between apps, and embedding third-party services into apps. The company BaseTen helps companies build and deploy machine learning APIs and applications.

DokuWiki: Machine Learning Engineering Dumped with DokuWiki-Dumper v0.1.43, and uploaded with dokuWikiUploader v0.1.43.

Machine learning (ML) is becoming an essential technology for our day to day life. Stop taking ML as a threat and learn it today as not learning it is a HUGE LOSS! Get started today with ML in Aaron's remarkable 45-mins talk. We will begin by talking about the paradigm of ML, then taking a deep dive into Neural Networks and building a Neural Network from scratch with Keras and TensorFlow (the hottest machine learning framework). You'll master the magic of neural networks that are powering incredible advances both in AI, self-driving cars, and much more! Finally, we will finish off by talking about Reinforcement learning and how it is empowering YouTube suggestions along with tips-and-tricks from a specialist plus a grand finale mind-blowing demo. Ready to master the paradigm of ML? Let's get started. Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/

Developing machine learning models is easy; training, deploying, monitoring, scaling, and maintaining them in an automated fashion - all while maintaining your sanity - is hard. In this session, I'll discuss the infrastructure and tooling my small team of data science practitioners and engineers is using to manage and orchestrate the machine learning model lifecycle, including pitfalls we've encountered along the way. Particular attention will be paid to where we've opted to use off-the-shelf solutions versus developing our own, the importance of developer ergonomics, and how to maximally empower data scientists to get their work into production without the need for a dedicated MLOps team. The talk will cover our ML stack as it exists in production today, and will touch on our application of a number of technologies and techniques, including: - AWS SageMaker - Airflow - Docker - Cookiecutter - Property-based testing - Jsonschema - Linting - Slack integration - Model artifacts and diagnostics - Automated deployments and rollbacks - Healthchecks - Autoscaling - DBT At the end of the session, attendees should expect to leave with new insights that they can apply immediately to their own ML systems and infrastructure, as well as a better understanding of how to minimize engineering and ops overhead, in the real world, across data science teams of any size and composition. Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/

The code from the Machine Learning Bookcamp book and a free course based on the book Machine Learning Zoomcamp Register at DataTalks.Club and join the #course-ml-zoomcamp channel Course telegram channel Course playlist Syllabus Introduction to Machine Learning Machine Learning for Regression Machine Learning for Classification Evaluation Metrics for Classification Deploying Machine Learning Models Decision Trees and Ensemble Learning Neural Networks and Deep Learning Serverless Deep Learning Kubernetes and TensorFlow Serving Taking the course 2023 Cohort We start the course again in September 2023 Sign up here Register at DataTalks.Club and join the #course-ml-zoomcamp channel Join the course telegram channel Subscribe to the public google calendar (subscribing works from desktop only) Tweet about it Start date: 11 September If you have questions, check FAQ All the materials specific to the 2023 will be in the 2023 cohort folder Self-paced mode You can take the course at your own pace. All the materials are freely available, and you can start learning at any time. To take the best out of this course, we recommened this: Register at DataTalks.Club and join the #course-ml-zoomcamp channel For each module, watch the videos and work through the code If you have any questions, ask them in the #course-ml-zoomcamp channel in Slack Do homework. There are solutions, but we advise to first attempt the homework yourself, and after that check the solutions Do at least one project. Two is better. Only this way you can make sure you're really learning. If you need feedback, use the #course-ml-zoomcamp channel Of course, you can take each module independently. Prerequisites Prior programming experience (at least 1+ year) Being comfortable with command line No prior exposure to machine learning is required Nice to have but not mandatory Python (but you can learn it during the course) Prior exposure to linear algebra will be helpful (e.g. you studied it in college but forgot) Asking questions The best way to get support is to use DataTalks.Club's Slack . Join the <code>#course-ml-zoomcamp</code> channel. To make discussions in Slack more organized: Follow these recommendations when asking for help Read the DataTalks.Club community guidelines 1. Introduction to Machine Learning 1.1 Introduction to Machine Learning 1.2 ML vs Rule-Based Systems 1.3 Supervised Machine Learning 1.4 CRISP-DM 1.5 Model Selection Process 1.6 Setting up the Environment 1.7 Introduction to NumPy 1.8 Linear Algebra Refresher 1.9 Introduction to Pandas 1.10 Summary 1.11 Homework 2. Machine Learning for Regression 2.1 Car price prediction project 2.2 Data preparation 2.3 Exploratory data analysis 2.4 Setting up the validation framework 2.5 Linear regression 2.6 Linear regression: vector form 2.7 Training linear regression: Normal equation 2.8 Baseline model for car price prediction project 2.9 Root mean squared error 2.10 Using RMSE on validation data 2.11 Feature engineering 2.12 Categorical variables 2.13 Regularization 2.14 Tuning the model 2.15 Using the model 2.16 Car price prediction project summary 2.17 Explore more 2.18 Homework 3. Machine Learning for Classification 3.1 Churn prediction project 3.2 Data preparation 3.3 Setting up the validation framework 3.4 EDA 3.5 Feature importance: Churn rate and risk ratio 3.6 Feature importance: Mutual information 3.7 Feature importance: Correlation 3.8 One-hot encoding 3.9 Logistic regression 3.10 Training logistic regression with Scikit-Learn 3.11 Model interpretation 3.12 Using the model 3.13 Summary 3.14 Explore more 3.15 Homework 4. Evaluation Metrics for Classification 4.1 Evaluation metrics: session overview 4.2 Accuracy and dummy model 4.3 Confusion table 4.4 Precision and Recall 4.5 ROC Curves 4.6 ROC AUC 4.7 Cross-Validation 4.8 Summary 4.9 Explore more 4.10 Homework 5. Deploying Machine Learning Models 5.1 Intro / Session overview 5.2 Saving and loading the model 5.3 Web services: introduction to Flask 5.4 Serving the churn model with Flask 5.5 Python virtual environment: Pipenv 5.6 Environment management: Docker 5.7 Deployment to the cloud: AWS Elastic Beanstalk (optional) 5.8 Summary 5.9 Explore more 5.10 Homework 6. Decision Trees and Ensemble Learning 6.1 Credit risk scoring project 6.2 Data cleaning and preparation 6.3 Decision trees 6.4 Decision tree learning algorithm 6.5 Decision trees parameter tuning 6.6 Ensemble learning and random forest 6.7 Gradient boosting and XGBoost 6.8 XGBoost parameter tuning 6.9 Selecting the best model 6.10 Summary 6.11 Explore more 6.12 Homework Midterm Project Putting everything we've learned so far in practice! 8. Neural Networks and Deep Learning 8.1 Fashion classification 8.1b Setting up the Environment on Saturn Cloud 8.2 TensorFlow and Keras 8.3 Pre-trained convolutional neural networks 8.4 Convolutional neural networks 8.5 Transfer learning 8.6 Adjusting the learning rate 8.7 Checkpointing 8.8 Adding more layers 8.9 Regularization and dropout 8.10 Data augmentation 8.11 Training a larger model 8.12 Using the model 8.13 Summary 8.14 Explore more 8.15 Homework 9. Serverless Deep Learning 9.1 Introduction to Serverless 9.2 AWS Lambda 9.3 TensorFlow Lite 9.4 Preparing the code for Lambda 9.5 Preparing a Docker image 9.6 Creating the lambda function 9.7 API Gateway: exposing the lambda function 9.8 Summary 9.9 Explore more 9.10 Homework 10. Kubernetes and TensorFlow Serving 10.1 Overview 10.2 TensorFlow Serving 10.3 Creating a pre-processing service 10.4 Running everything locally with Docker-compose 10.5 Introduction to Kubernetes 10.6 Deploying a simple service to Kubernetes 10.7 Deploying TensorFlow models to Kubernetes 10.8 Deploying to EKS 10.9 Summary 10.10 Explore more 10.11 Homework 11. KServe (optional) 11.1 Overview 11.2 Running KServe locally 11.3 Deploying a Scikit-Learn model with KServe 11.4 Deploying custom Scikit-Learn images with KServe 11.5 Serving TensorFlow models with KServe 11.6 KServe transformers 11.7 Deploying with KServe and EKS 11.8 Summary 11.9 Explore more Capstone Project 1 Putting everything we've learned so far in practice one more time! Article Writing an article about something not covered in the course. Capstone project 2 (optional) For those who love projects! Image classification competition If you liked our deep learning module, join us to build a model for classifying cups, glasses, plates, spoons, forks and knives. Submit your learning in public links here Previous cohorts 2021 Cohort Homeworks The 100 leaderboard 2022 Cohort Homeworks The 100 leaderboard Our other courses If you liked this course, you'll like other courses from us: Data Engineering Zoomcamp - free 9-week course about Data Engineering MLOps Zoomcamp - free 10-week course about MLOps To restore the repository download the bundle wget https://archive.org/download/github.com-DataTalksClub-machine-learning-zoomcamp_-_2023-09-13_18-42-48/DataTalksClub-machine-learning-zoomcamp_-_2023-09-13_18-42-48.bundle and run: git clone DataTalksClub-machine-learning-zoomcamp_-_2023-09-13_18-42-48.bundle Source: https://github.com/DataTalksClub/machine-learning-zoomcamp Uploader: DataTalksClub Upload date: 2023-09-13

The code from the Machine Learning Bookcamp book and a free course based on the book Machine Learning Zoomcamp Register at DataTalks.Club and join the #course-ml-zoomcamp channel Course telegram channel Course playlist Syllabus Introduction to Machine Learning Machine Learning for Regression Machine Learning for Classification Evaluation Metrics for Classification Deploying Machine Learning Models Decision Trees and Ensemble Learning Neural Networks and Deep Learning Serverless Deep Learning Kubernetes and TensorFlow Serving Taking the course 2023 Cohort We start the course again in September 2023 Sign up here Register at DataTalks.Club and join the #course-ml-zoomcamp channel Join the course telegram channel Subscribe to the public google calendar (subscribing works from desktop only) Tweet about it Start date: 11 September If you have questions, check FAQ All the materials specific to the 2023 will be in the 2023 cohort folder Self-paced mode You can take the course at your own pace. All the materials are freely available, and you can start learning at any time. To take the best out of this course, we recommened this: Register at DataTalks.Club and join the #course-ml-zoomcamp channel For each module, watch the videos and work through the code If you have any questions, ask them in the #course-ml-zoomcamp channel in Slack Do homework. There are solutions, but we advise to first attempt the homework yourself, and after that check the solutions Do at least one project. Two is better. Only this way you can make sure you're really learning. If you need feedback, use the #course-ml-zoomcamp channel Of course, you can take each module independently. Prerequisites Prior programming experience (at least 1+ year) Being comfortable with command line No prior exposure to machine learning is required Nice to have but not mandatory Python (but you can learn it during the course) Prior exposure to linear algebra will be helpful (e.g. you studied it in college but forgot) Asking questions The best way to get support is to use DataTalks.Club's Slack . Join the <code>#course-ml-zoomcamp</code> channel. To make discussions in Slack more organized: Follow these recommendations when asking for help Read the DataTalks.Club community guidelines 1. Introduction to Machine Learning 1.1 Introduction to Machine Learning 1.2 ML vs Rule-Based Systems 1.3 Supervised Machine Learning 1.4 CRISP-DM 1.5 Model Selection Process 1.6 Setting up the Environment 1.7 Introduction to NumPy 1.8 Linear Algebra Refresher 1.9 Introduction to Pandas 1.10 Summary 1.11 Homework 2. Machine Learning for Regression 2.1 Car price prediction project 2.2 Data preparation 2.3 Exploratory data analysis 2.4 Setting up the validation framework 2.5 Linear regression 2.6 Linear regression: vector form 2.7 Training linear regression: Normal equation 2.8 Baseline model for car price prediction project 2.9 Root mean squared error 2.10 Using RMSE on validation data 2.11 Feature engineering 2.12 Categorical variables 2.13 Regularization 2.14 Tuning the model 2.15 Using the model 2.16 Car price prediction project summary 2.17 Explore more 2.18 Homework 3. Machine Learning for Classification 3.1 Churn prediction project 3.2 Data preparation 3.3 Setting up the validation framework 3.4 EDA 3.5 Feature importance: Churn rate and risk ratio 3.6 Feature importance: Mutual information 3.7 Feature importance: Correlation 3.8 One-hot encoding 3.9 Logistic regression 3.10 Training logistic regression with Scikit-Learn 3.11 Model interpretation 3.12 Using the model 3.13 Summary 3.14 Explore more 3.15 Homework 4. Evaluation Metrics for Classification 4.1 Evaluation metrics: session overview 4.2 Accuracy and dummy model 4.3 Confusion table 4.4 Precision and Recall 4.5 ROC Curves 4.6 ROC AUC 4.7 Cross-Validation 4.8 Summary 4.9 Explore more 4.10 Homework 5. Deploying Machine Learning Models 5.1 Intro / Session overview 5.2 Saving and loading the model 5.3 Web services: introduction to Flask 5.4 Serving the churn model with Flask 5.5 Python virtual environment: Pipenv 5.6 Environment management: Docker 5.7 Deployment to the cloud: AWS Elastic Beanstalk (optional) 5.8 Summary 5.9 Explore more 5.10 Homework 6. Decision Trees and Ensemble Learning 6.1 Credit risk scoring project 6.2 Data cleaning and preparation 6.3 Decision trees 6.4 Decision tree learning algorithm 6.5 Decision trees parameter tuning 6.6 Ensemble learning and random forest 6.7 Gradient boosting and XGBoost 6.8 XGBoost parameter tuning 6.9 Selecting the best model 6.10 Summary 6.11 Explore more 6.12 Homework Midterm Project Putting everything we've learned so far in practice! 8. Neural Networks and Deep Learning 8.1 Fashion classification 8.1b Setting up the Environment on Saturn Cloud 8.2 TensorFlow and Keras 8.3 Pre-trained convolutional neural networks 8.4 Convolutional neural networks 8.5 Transfer learning 8.6 Adjusting the learning rate 8.7 Checkpointing 8.8 Adding more layers 8.9 Regularization and dropout 8.10 Data augmentation 8.11 Training a larger model 8.12 Using the model 8.13 Summary 8.14 Explore more 8.15 Homework 9. Serverless Deep Learning 9.1 Introduction to Serverless 9.2 AWS Lambda 9.3 TensorFlow Lite 9.4 Preparing the code for Lambda 9.5 Preparing a Docker image 9.6 Creating the lambda function 9.7 API Gateway: exposing the lambda function 9.8 Summary 9.9 Explore more 9.10 Homework 10. Kubernetes and TensorFlow Serving 10.1 Overview 10.2 TensorFlow Serving 10.3 Creating a pre-processing service 10.4 Running everything locally with Docker-compose 10.5 Introduction to Kubernetes 10.6 Deploying a simple service to Kubernetes 10.7 Deploying TensorFlow models to Kubernetes 10.8 Deploying to EKS 10.9 Summary 10.10 Explore more 10.11 Homework 11. KServe (optional) 11.1 Overview 11.2 Running KServe locally 11.3 Deploying a Scikit-Learn model with KServe 11.4 Deploying custom Scikit-Learn images with KServe 11.5 Serving TensorFlow models with KServe 11.6 KServe transformers 11.7 Deploying with KServe and EKS 11.8 Summary 11.9 Explore more Capstone Project 1 Putting everything we've learned so far in practice one more time! Article Writing an article about something not covered in the course. Capstone project 2 (optional) For those who love projects! Image classification competition If you liked our deep learning module, join us to build a model for classifying cups, glasses, plates, spoons, forks and knives. Submit your learning in public links here Previous cohorts 2021 Cohort Homeworks The 100 leaderboard 2022 Cohort Homeworks The 100 leaderboard Our other courses If you liked this course, you'll like other courses from us: Data Engineering Zoomcamp - free 9-week course about Data Engineering MLOps Zoomcamp - free 10-week course about MLOps To restore the repository download the bundle wget https://archive.org/download/github.com-DataTalksClub-machine-learning-zoomcamp_-_2023-09-13_18-42-48/DataTalksClub-machine-learning-zoomcamp_-_2023-09-13_18-42-48.bundle and run: git clone DataTalksClub-machine-learning-zoomcamp_-_2023-09-13_18-42-48.bundle Source: https://github.com/DataTalksClub/machine-learning-zoomcamp Uploader: DataTalksClub Upload date: 2023-09-13

By: Kalyan Munjuluri & Karishma Babbar Event: PyConZA 2021 URL: https://za.pycon.org/talks/23-machine-learning-lifecycle-made-easy-with-mlflow/ ABSTRACT Beyond the usual concerns in software development, machine learning development comes with additional challenges. These include trying multiple algorithms and parameters to get the best results, tracking these runs for reproducibility, and moving the model to diverse deployment environments. This talk demonstrates the use of an open-source platform called MLflow for managing the complete machine learning lifecycle with Python. The talk requires a basic understanding of Python and Machine Learning concepts. DESCRIPTION In theory, the crux of machine learning (ML) development lies with data collection, model creation, model training, and deployment. In reality, machine learning projects are not so straightforward. They are a cycle iterating between improving the data, model, and evaluation that is never really finished. Unlike in traditional software development, ML developers experiment with multiple algorithms, tools, and parameters to optimize performance, and they need to track these experiments to reproduce work. Furthermore, developers need to use many distinct systems to productionize models. In this talk, we introduce MLflow, an open-source platform that aims at simplifying the entire ML lifecycle where we can use any ML library and development tool of our choice to reliably build and share ML applications. MLflow offers simple abstractions through lightweight APIs to package reproducible projects, track results, and encapsulate models that are compatible with existing tools, thereby, accelerating ML lifecycle of any size. With the help of an example, we will show how using MLflow can ease bookkeeping of experiment runs and results across frameworks, quickly reproducing runs on any platform (cloud or local execution), and productionizing models on diverse deployment tools. At the end of this talk, you will be familiar with – Key concepts, abstractions, and components of open-source MLflow How each component of MLflow addresses challenges of ML lifecycle How to use MLflow Tracking during model training to record experimental runs How to use MLflow Tracking User Interface to visualize experimental runs with different tuning parameters and evaluation metrics How to use MLflow Projects for packaging reusable and reproducible models How to use MLflow Models general format to serve models using MLflow REST API The purpose of the session is to introduce the audience to MLflow and give a taste of the ML development lifecycle. It is intended at providing a breadth than depth survey of MLflow platform, and we leave the audience to experiment with it further through takeaway exercises. PRE-REQUISITES Basic knowledge of Python programming language Basic understanding of machine learning concepts TRACK Data Science in Production, Machine Learning, Data Engineering or MLOps Room: Video Room 2 Scheduled start: 2021-10-08 14:30:00 Sponsors: Gold: SPAN Digital: https://spandigital.com/ Takealot: http://takealot.com/ Andela: https://www.andela.com/ Silver: Python Software Foundation: https://www.python.org/psf/membership OfferZen: https://www.offerzen.com/ Patron: Thinkst Canary: https://canary.tools/ Afrolabs: http://www.afrolabs.co.za/

The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science. The current version of the web site was designed in 2007 by Arthur Asuncion and David Newman, and this project is in collaboration with Rexa.info at the University of Massachusetts Amherst. Funding support from the National Science Foundation is gratefully acknowledged. Many people deserve thanks for making the repository a success. Foremost among them are the donors and creators of the databases and data generators. Special thanks should also go to the past librarians of the repository: David Aha, Patrick Murphy, Christopher Merz, Eamonn Keogh, Cathy Blake, Seth Hettich, and David Newman.

The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science. The current version of the web site was designed in 2007 by Arthur Asuncion and David Newman, and this project is in collaboration with Rexa.info at the University of Massachusetts Amherst. Funding support from the National Science Foundation is gratefully acknowledged. Many people deserve thanks for making the repository a success. Foremost among them are the donors and creators of the databases and data generators. Special thanks should also go to the past librarians of the repository: David Aha, Patrick Murphy, Christopher Merz, Eamonn Keogh, Cathy Blake, Seth Hettich, and David Newman.

The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science. The current version of the web site was designed in 2007 by Arthur Asuncion and David Newman, and this project is in collaboration with Rexa.info at the University of Massachusetts Amherst. Funding support from the National Science Foundation is gratefully acknowledged. Many people deserve thanks for making the repository a success. Foremost among them are the donors and creators of the databases and data generators. Special thanks should also go to the past librarians of the repository: David Aha, Patrick Murphy, Christopher Merz, Eamonn Keogh, Cathy Blake, Seth Hettich, and David Newman.

The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science. The current version of the web site was designed in 2007 by Arthur Asuncion and David Newman, and this project is in collaboration with Rexa.info at the University of Massachusetts Amherst. Funding support from the National Science Foundation is gratefully acknowledged. Many people deserve thanks for making the repository a success. Foremost among them are the donors and creators of the databases and data generators. Special thanks should also go to the past librarians of the repository: David Aha, Patrick Murphy, Christopher Merz, Eamonn Keogh, Cathy Blake, Seth Hettich, and David Newman.

machine learning

The state of machine learning operations in 2019: reproducibility, explainability, bias evaluation and beyond by Alejandro Saucedo At: FOSDEM 2019 https://video.fosdem.org/2019/UA2.118/8_principles_production_data_science.webm Description This talk will provide a practical overview of the state of production machine learning frameworks, tools and techniques that seem to have become a trend for the coming year. I will be mainly covering the open source tools and frameworks available in 2019 to help you expand your DataOps and MLOps infrastructure. This talk will cover the technologies available to support specifically around reproducibility, monitoring, compliance and orchestration of data and computations. I will present these technologies through the 8 principles of machine learning. Talk Name The state of machine learning operations in 2019: reproducibility, explainability, bias evaluation and beyond Description This talk will provide a practical overview of the state of production machine learning frameworks, tools and techniques that seem to have become a trend for the coming year. I will be mainly covering the open source tools and frameworks available in 2019 to help you expand your DataOps and MLOps infrastructure. This talk will cover the technologies available to support specifically around reproducibility, monitoring, compliance and orchestration of data and computations. I will present these technologies through the 8 principles of machine learning. About me Alejandro Saucedo is a technology leader with over 10 years of software development experience, he is currently the Chief Scientist at The Institute for Ethical AI & Machine Learning. Throughout his career, Alejandro has held technical leadership positins across hyper-growth scale-ups and tech giants including Eigen Technologies, Bloomberg LP and Hack Partners. Alejandro has a strong track record building multiple departments of machine learning engineers from scrath, and leading the delivery of numerous large-scale machine learnig systems across the financial, insurance, legal, transport, manufcturing and construcion sectors (in Europe, US and Latin America). Room: UA2.118 (Henriot) Scheduled start: 2019-02-03 13:00:00+01 Source: https://www.youtube.com/watch?v=qnXjLZtosps Uploader: FOSDEM

Quo Vadis Quantum Machine Learning - Jens Eisert @QTMLConference

This video was translated from English to Spanish. As described in https://developers.googleblog.com/2022/12/improving-video-voice-dubbing-through-deep-learning.html , we used the techniques of cross-lingual voice imitation and lip reanimation, which makes the voice sound like the original speaker and adjusts the lip motion to make it appear more natural. Original English series: https://www.youtube.com/playlist?list=PLOU2XLYxmsII9mzQ-Xxug4l2o04JBrkLV -------------------------------------------------------------- Este vídeo se tradujo del inglés al español. Como se describe en https://developers.googleblog.com/2022/12/improving-video-voice-dubbing-through-deep-learning.html , hemos utilizado las técnicas de imitación de voz multilingüe y reanimación labial, que hace que la voz suene como la del hablante original y ajusta el movimiento de los labios para que parezca más natural. Serie original en inglés: https://www.youtube.com/playlist?list=PLOU2XLYxmsII9mzQ-Xxug4l2o04JBrkLV

Global public health continues to face substantial obstacles from mosquito borne diseases, making precise and effective techniques for mosquito species identification necessary. We present a unique method in this article called Mosquito Species Classification through Wingbeat Analysis A Hybrid Machine Learning Approach, which uses wingbeat analysis and deep learning techniques to classify mosquito species. Our approach leverages Convolutional Neural Networks CNNs as the core model to provide robust and dependable classification performance. We make use of an extensive dataset that includes wingbeat recordings from many species of mosquitoes and apply comprehensive pre processing and feature engineering techniques to enhance the models effectiveness. Specifically, we extract and combine features such as zero crossing rate ZCR , root mean square energy RMSE , mel frequency cepstral coefficients MFCC , as well as augmented features derived from audio transformations like add noise, shifting, pitching, and stretching. This combination of handcrafted and augmented features helps to enrich the training data and improve the generalizability of the model. After thorough testing and evaluation, we demonstrate that our CNN based method achieves superior performance in accurately classifying various mosquito species. Our findings underscore the potential of deep learning methods, particularly CNNs, to surpass conventional classification techniques in species identification tasks. Additionally, we highlight the critical role of accurate species classification in vector surveillance and epidemiological research, emphasizing the broader impact of our work on ecological studies and disease control strategies. Mir Irtiqa Farooq | Dr. Dheeraj Chhillar | Mudasir Ahmed Muttoo "Hybrid Machine Learning Approach for Mosquito Species Classification using Wingbeat Analysis: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd81085.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/81085/hybrid-machine-learning-approach-for-mosquito-species-classification-using-wingbeat-analysis-a-review/mir-irtiqa-farooq

Automakers are trying to make 'petextrian' happen on the way to automated vehicles, Kenneth shares the most dangerous word in software development, and America's bridges are falling down. Guest Credits: Loyal Listener — Blake Dillinger

Digital age has abundance of data through documents, videos etc. The analysis of data gets wasted if non-utilized for productive purposes. Machine Learning provides solutions with the input data fed and translate them to meaning output used for decision making.

Digital age has abundance of data through documents, videos etc. The analysis of data gets wasted if non-utilized for productive purposes. Machine Learning provides solutions with the input data fed and translate them to meaning output used for decision making.

Digital age has abundance of data through documents, videos etc. The analysis of data gets wasted if non-utilized for productive purposes. Machine Learning provides solutions with the input data fed and translate them to meaning output used for decision making.

This protocol is developed to conduct a scoping review on the role of machine learning and preeclampsia integrated estimate of risk (PIERS) models in predicting maternal and perinatal birth outcomes or complications among women with hypertensive disorders of pregnancy. It is planned to include studies conducted from January 2000 to September 2025 around the globe.

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (25 - Part 4 Clustering)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (30 - Eclat)

Downloaded from U.S. Food and Drug Administration Youtube channel on 2025-09-12 17:49:31 https://youtube.com/watch?v=KaO3V-B1Wjg -------- This presentation introduced background information on artificial intelligence and machine learning in pharmaceutical development and described the current landscape of AI/ML-related submissions and reviews at CDER. The session listed key challenges associated with AI/ML applications and discussed evolving regulatory considerations for these technologies. AI/ML-related activities within the Office of Clinical Pharmacology were highlighted to demonstrate the agency's comprehensive approach to integrating these emerging technologies into drug development and regulation. Timestamps 02:55 – AI/ML and Related terminology 04:48 – Important Types of Neural Networks 07:27 – Opportunities for the Application of AI/ML in Drug discovery, Drug Development and Patient Care 12:25 – Regulatory Considerations for AI/ML in Drug Development (Personal Opinion, Still Evolving) 14:37 – Review Examples 22:08 – Challenges in the Application of AI/ML 25:30 – AI/ML Activities in Office of Clinical Pharmacology (OCP)/FDA 29:30 – Questions Speaker: Qi Liu, PhD, MStat, FCP Associate Director for Innovation & Partnership Office of Clinical Pharmacology (OCP) Office of Translational Sciences (OTS) Center for drug Evaluation and Research (CDER) | FDA Learn more at: https://www.fda.gov/drugs/news-events-human-drugs/regulatory-education-industry-redi-annual-conference-2024-innovation-medical-product-development ----------------------- FDA CDER’s Small Business and Industry Assistance (SBIA) educates and provides assistance in understanding the regulatory aspects of human drug products & clinical research. Upcoming Training - https://www.fda.gov/cdersbia SBIA Listserv - https://public.govdelivery.com/accounts/USFDA/subscriber/new?topic_id=USFDA_352 SBIA 2024 Playlist - https://www.youtube.com/playlist?list=PLey4Qe-Uxcxaf1HvpvnKO8-n8u9QW412g SBIA LinkedIn - https://www.linkedin.com/showcase/cder-small-business-and-industry-assistance SBIA Training Resources - https://www.fda.gov/cdersbialearn Twitter - https://twitter.com/FDA_Drug_Info Email - [email protected] Phone - (301) 796-6707 I (866) 405-5367

Bayesian Machine Learning In Python AB Testing

Think Machine Learning will replace humans or that ML models are always accurate? Think again! In this entertaining video, we debunk five of the most common myths about Machine Learning with humorous animations and engaging skits. Perfect for anyone exploring Data Science, this video separates fact from fiction to give you a clearer understanding of ML. Want to learn more? Check out our detailed fact-check article on our website!

What is a GPU and how is it different than a GPU? GPUs and CPUs are both silicone based microprocessors but they differ in what they specialize in. GPUs specialize in parallel computations, and CPUs specialize in serial computations. While GPUs are known for a key component of getting the great graphics you want in your gamming, they are also known in the Machine Learning and AI Community for helping crunch millions of parameters needed to train a machine. Learn more in this episode of GLITCH. UPDATES: I've developed a Product Management Course for AI & Data Science for those interested in the industry or wanting to get into Product Management. Here's the link! https://www.udemy.com/course/the-product-management-for-data-science-ai-course/?referralCode=DE25D5190902F792E9A1 JOIN The GLITCH Email List: https://glitch.technology/subscribe SAY HELLO https://twitter.com/daniellethe https://instagram.com/daniellethe https://medium.com/daniellethe

What is a GPU and how is it different than a GPU? GPUs and CPUs are both silicone based microprocessors but they differ in what they specialize in. GPUs specialize in parallel computations, and CPUs specialize in serial computations. While GPUs are known for a key component of getting the great graphics you want in your gamming, they are also known in the Machine Learning and AI Community for helping crunch millions of parameters needed to train a machine. Learn more in this episode of GLITCH. UPDATES: I've developed a Product Management Course for AI & Data Science for those interested in the industry or wanting to get into Product Management. Here's the link! https://www.udemy.com/course/the-product-management-for-data-science-ai-course/?referralCode=DE25D5190902F792E9A1 JOIN The GLITCH Email List: https://glitch.technology/subscribe SAY HELLO https://twitter.com/daniellethe https://instagram.com/daniellethe https://medium.com/daniellethe

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.

Any advanced class in Science, Technology, Engineering, and Mathematics fields requires prerequisite knowledge. Typically, different students will have different levels of knowledge in these prerequisite areas. A prerequisite (Linear Algebra for Machine Learning course) was implemented as an interactive online course using Jupyter Notebooks and nbgrader and compared with traditional classroom mode. Post-assessment test shows that traditional class provides a better level of understanding. However, a survey shows a preference by students and instructors for interactive implementation compared to traditional class. [For the full proceedings, see ED639633.]

Educación transcompleja: optimizando el aprendizaje adaptativo con inteligencia artificial y machine learning   ¿Máquinas que Enseñan o Humanos que Despiertan? María del Rosario Fernández de Silva Colección: Transtecnología Primera Edición, Julio, 2025 Depósito Legal: AR2025000131 ISBN: 978-980-456-003-3

While hierarchical machine learning approaches have been used to classify texts into different content areas, this approach has, to our knowledge, not been used in the automated assessment of text difficulty. This study compared the accuracy of four classification machine learning approaches (flat, one-vs-one, one-vs-all, and hierarchical) using natural language processing features in predicting human ratings of text difficulty for two sets of texts. The hierarchical classification was the most accurate for the two text sets considered individually (Set A, 77.78%; Set B, 82.05%), while the non-hierarchical approaches, one-vs-one and one-vs-all, performed similar to the hierarchical classification for the combined set (71.43%). These findings suggest both promise and limitations for applying hierarchical approaches to text difficulty classification. It may be beneficial to apply a recursive top-down approach to discriminate the subsets of classes that are at the top of the hierarchy and less related, and then further separate the classes into subsets that may be more similar to one other. These results also suggest that a single approach may not always work for all types of datasets and that it is important to evaluate which machine learning approach and algorithm works best for particular datasets. The authors encourage more work in this area to help suggest which types of algorithms work best as a function of the type of dataset.

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ML - python

Machine learning

Larry's Learning Math Machine is an easy to use, fun program for Windows 3.1 or better, that allows you to practice addition, subtraction, multiplication and division. A unique learning experience! Has awesome graphics, and great sounds! 

Larry's Learning Math Machine is an easy to use, fun program for Windows 3.1 or better, that allows you to practice addition, subtraction, multiplication and division. A unique learning experience! Has awesome graphics, and great sounds!