My journey learning and applying Machine Learning as a digital analyst. The Good, the Bad and the Ugly!
In this session, I will talk you through my journey, go through some of the key Machine Learning concepts in hopefully the simplest way possible, show you what Python code looks like ( this was done in the Demo part and so not available on these slides at the moment) when it comes to using it for Machine Learning purposes and give you some tips and resources for you to get started on that great journey!
NDepend is a static analysis tool for .NET managed code. This tool supports a large number of code metrics, allows for visualization of dependencies using directed graphs and dependency matrix. The tools also performs code base snapshots comparison, and validation of architectural and quality rules.
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
DN18 | Demystifying the Buzz in Machine Learning! (This Time for Real) | Dat ...Dataconomy Media
Abstract of the Presentation:
When Dat Tran started his data science career in 2013, everyone was into big data. In fact, big data was at the peak of inflated expectations (according to Gartner). You had to use tools like Hadoop and Spark to be one of the cool kids. Many data prophets out there told you that data is the new oil, or even gold. Year 2018, things haven’t changed. Data is still cool and going strong. It’s eating the world- and yes, you still need big data, and now also deep deep very deep learning. There’s a lot of bullshit bingo out there.
In this talk, Dat Tran wants to demystify the buzz in machine learning by presenting some simple guidelines for successful data projects and real practical use cases. He will also share use cases from idealo, Germany’s largest price comparison service. And yes it involves deep learning, and yes it can be quite technical sometimes as well.
About the Author:
Dat Tran is currently co-heading the data team at idealo.de, where he leads a team of Data Scientists and Data Engineers. His aim is to turn idealo into a machine learning powerhouse. His research interests are diverse, from traditional machine learning to deep learning. Previously, he worked for Pivotal Labs and Accenture. He is a regular speaker and has presented at PyData and Cloud Foundry Summit. He also blogs about his work on Medium. His background is in Operations Research and Econometrics. Dat received his MSc in Economics from Humboldt University of Berlin.
NDepend is a static analysis tool for .NET managed code. This tool supports a large number of code metrics, allows for visualization of dependencies using directed graphs and dependency matrix. The tools also performs code base snapshots comparison, and validation of architectural and quality rules.
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
DN18 | Demystifying the Buzz in Machine Learning! (This Time for Real) | Dat ...Dataconomy Media
Abstract of the Presentation:
When Dat Tran started his data science career in 2013, everyone was into big data. In fact, big data was at the peak of inflated expectations (according to Gartner). You had to use tools like Hadoop and Spark to be one of the cool kids. Many data prophets out there told you that data is the new oil, or even gold. Year 2018, things haven’t changed. Data is still cool and going strong. It’s eating the world- and yes, you still need big data, and now also deep deep very deep learning. There’s a lot of bullshit bingo out there.
In this talk, Dat Tran wants to demystify the buzz in machine learning by presenting some simple guidelines for successful data projects and real practical use cases. He will also share use cases from idealo, Germany’s largest price comparison service. And yes it involves deep learning, and yes it can be quite technical sometimes as well.
About the Author:
Dat Tran is currently co-heading the data team at idealo.de, where he leads a team of Data Scientists and Data Engineers. His aim is to turn idealo into a machine learning powerhouse. His research interests are diverse, from traditional machine learning to deep learning. Previously, he worked for Pivotal Labs and Accenture. He is a regular speaker and has presented at PyData and Cloud Foundry Summit. He also blogs about his work on Medium. His background is in Operations Research and Econometrics. Dat received his MSc in Economics from Humboldt University of Berlin.
How Graph Data Science can turbocharge your Knowledge GraphNeo4j
Knowledge Graphs are becoming mission-critical across many industries. More recently, we are witnessing the application of Graph Data Science to Knowledge Graphs, offering powerful outcomes. But how do we define Knowledge Graphs in industry and how can they be useful for your project? In this talk, we will illustrate the various methods and models of Graph Data Science being applied to Knowledge Graphs and how they allow you to find implicit relationships in your graph which are impossible to detect in any other way. You will learn how graph algorithms from PageRank to Embeddings drive ever deeper insights in your data.
Oracle Analytics Live!
Each month the webinar will feature updates from the Analytics Product Strategy team on the product vision, strategy and roadmap as well as provide various community speakers and live demos from customers, partners and analytics solutions engineers with plenty of time for live Q&A at the end to answer all of your questions!
Register: https://go.oracle.com/LP=98970?elqCampaignId=265208
A workshop on how to leverage lean design/design thinking & existing technologies to test ideas and build viable products and solution to business problems.
Are you an inquisitive person?
Do you have the enthusiasm and willingness to learn new topics?
Do you want to be a Data Scientist and make pots of money?
Do you like to know the future job prospects for Data Science?
Download my recent (12th January, 2021) presentation titled “Analytics – Future Trend and Job Prospects”.
NicheTech is a fastest growing IT company working on .Net Projects for offshore clients across the globe for over 18 countries.
We have expertise in MVC and SQL server based complex business projects.
C# developers are the best assets for us.
ASP.net development in india is the best option and NicheTech is the best available option for you.
Website : http://www.nichetechsolutions.com/ASP.net-MVC-Developers
Facebook: https://www.facebook.com/NicheTech
Twitter : https://twitter.com/nichetechsol
Growing as a software craftsperson (part 1) From Pune Software Craftsmanship.Dattatray Kale
Presentation on Saturday, April 13, 2019, From Pune Software Craftsmanship.
https://www.meetup.com/punesoftwarecraftsmancommunity/events/260255336/
Introduced Software Craftsmanship manifesto, professionalism for developers, Software engineering code of ethics, boys scout rule, Broken windows theory, Poka-yoke, and Cyclomatic Complexity.
Jay Yagnik at AI Frontiers : A History Lesson on AIAI Frontiers
We have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.
Symposium 2019 : Gestion de projet en Intelligence ArtificiellePMI-Montréal
L’objectif d’un projet impliquant l’intelligence artificielle est d’accélérer la prise de décision, voir même, d’automatiser les actions qui doivent être effectuées dans le cadre d’une tache. La principale difficulté est qu’il n’est pas possible de savoir à l’avance quelle méthode d’AI permettra d’atteindre l’objectif. La gestion du projet est souvent atypique et nécessite d’être flexible en respectant toutefois des contraintes de budget. Pour cette raison une approche waterfall est à éviter. Toutefois, nous allons voir qu’elle peut être exploitée dans certaines phases du projet.
Lors de cette présentation, nous allons voir les trois phases du projet : prototypage de la solution, mise en production, ainsi que les stratégies de maintien à plus long terme de la solution.
Dr. Nathanael Weill
"What we learned from 5 years of building a data science software that actual...Dataconomy Media
"What we learned from 5 years of building a data science software that actually works for everybody." Dr. Dennis Proppe, CTO and Chief Data Scientist at GPredictive GmbH
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
https://www.youtube.com/c/DataNatives
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Dennis Proppe is the CTO and Chief Data Scientist at Gpredictive, where he helps building software that enables data scientists to build and deploy predictive models in a few minutes instead of weeks. He has 10 years+ of expertise in extracting business value from data. Before co-founding Gpredictive, he worked as a marketing science consultant. Dennis holds a Ph.D. in statistical marketing.
How Graph Data Science can turbocharge your Knowledge GraphNeo4j
Knowledge Graphs are becoming mission-critical across many industries. More recently, we are witnessing the application of Graph Data Science to Knowledge Graphs, offering powerful outcomes. But how do we define Knowledge Graphs in industry and how can they be useful for your project? In this talk, we will illustrate the various methods and models of Graph Data Science being applied to Knowledge Graphs and how they allow you to find implicit relationships in your graph which are impossible to detect in any other way. You will learn how graph algorithms from PageRank to Embeddings drive ever deeper insights in your data.
Oracle Analytics Live!
Each month the webinar will feature updates from the Analytics Product Strategy team on the product vision, strategy and roadmap as well as provide various community speakers and live demos from customers, partners and analytics solutions engineers with plenty of time for live Q&A at the end to answer all of your questions!
Register: https://go.oracle.com/LP=98970?elqCampaignId=265208
A workshop on how to leverage lean design/design thinking & existing technologies to test ideas and build viable products and solution to business problems.
Are you an inquisitive person?
Do you have the enthusiasm and willingness to learn new topics?
Do you want to be a Data Scientist and make pots of money?
Do you like to know the future job prospects for Data Science?
Download my recent (12th January, 2021) presentation titled “Analytics – Future Trend and Job Prospects”.
NicheTech is a fastest growing IT company working on .Net Projects for offshore clients across the globe for over 18 countries.
We have expertise in MVC and SQL server based complex business projects.
C# developers are the best assets for us.
ASP.net development in india is the best option and NicheTech is the best available option for you.
Website : http://www.nichetechsolutions.com/ASP.net-MVC-Developers
Facebook: https://www.facebook.com/NicheTech
Twitter : https://twitter.com/nichetechsol
Growing as a software craftsperson (part 1) From Pune Software Craftsmanship.Dattatray Kale
Presentation on Saturday, April 13, 2019, From Pune Software Craftsmanship.
https://www.meetup.com/punesoftwarecraftsmancommunity/events/260255336/
Introduced Software Craftsmanship manifesto, professionalism for developers, Software engineering code of ethics, boys scout rule, Broken windows theory, Poka-yoke, and Cyclomatic Complexity.
Jay Yagnik at AI Frontiers : A History Lesson on AIAI Frontiers
We have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.
Symposium 2019 : Gestion de projet en Intelligence ArtificiellePMI-Montréal
L’objectif d’un projet impliquant l’intelligence artificielle est d’accélérer la prise de décision, voir même, d’automatiser les actions qui doivent être effectuées dans le cadre d’une tache. La principale difficulté est qu’il n’est pas possible de savoir à l’avance quelle méthode d’AI permettra d’atteindre l’objectif. La gestion du projet est souvent atypique et nécessite d’être flexible en respectant toutefois des contraintes de budget. Pour cette raison une approche waterfall est à éviter. Toutefois, nous allons voir qu’elle peut être exploitée dans certaines phases du projet.
Lors de cette présentation, nous allons voir les trois phases du projet : prototypage de la solution, mise en production, ainsi que les stratégies de maintien à plus long terme de la solution.
Dr. Nathanael Weill
"What we learned from 5 years of building a data science software that actual...Dataconomy Media
"What we learned from 5 years of building a data science software that actually works for everybody." Dr. Dennis Proppe, CTO and Chief Data Scientist at GPredictive GmbH
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
https://www.youtube.com/c/DataNatives
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Dennis Proppe is the CTO and Chief Data Scientist at Gpredictive, where he helps building software that enables data scientists to build and deploy predictive models in a few minutes instead of weeks. He has 10 years+ of expertise in extracting business value from data. Before co-founding Gpredictive, he worked as a marketing science consultant. Dennis holds a Ph.D. in statistical marketing.
Similar to Measure camp 2021_my_journey_learning_machine_learning_the_good_the_bad_and_the_ugly (20)
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
