In this talk, WeCloudData introduces the lifecycle of machine learning and its tools/ecosystems. For more detail about WeCloudData's machine learning course please visit: https://weclouddata.com/data-science/
Guiding through a typical Machine Learning PipelineMichael Gerke
Many People are talking about AI and Machine Learning. Here's a quick guideline how to manage ML Projects and what to consider in order to implement machine learning use cases.
This document provides an introduction to machine learning. It begins with an agenda that lists topics such as introduction, theory, top 10 algorithms, recommendations, classification with naive Bayes, linear regression, clustering, principal component analysis, MapReduce, and conclusion. It then discusses what big data is and how data is accumulating at tremendous rates from various sources. It explains the volume, variety, and velocity aspects of big data. The document also provides examples of machine learning applications and discusses extracting insights from data using various algorithms. It discusses issues in machine learning like overfitting and underfitting data and the importance of testing algorithms. The document concludes that machine learning has vast potential but is very difficult to realize that potential as it requires strong mathematics skills.
The document provides an overview of machine learning use cases. It begins with an agenda that will discuss the basic framework for ML projects, model deployment options, and various ML use cases like text classification, image classification, object detection, etc. It then covers the basic 5 step framework for ML projects - defining the problem, planning the solution, acquiring and preparing data, designing and training a model, and deploying the solution. Next, it discusses popular methods for various tasks like image classification, object detection, pose estimation. Finally, it shares several use cases for each task to demonstrate real-world applications.
Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them.
This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you!
Intro to Machine Learning with H2O and AWSSri Ambati
Navdeep Gill @ Galvanize Seattle- May 2016
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
The Machine Learning Audit. MIS ITAC 2017 KeynoteAndrew Clark
As it gains wider adoption, what does machine learning mean for internal auditors and their organizations? With the proliferation of buzzwords and the black box nature of machine learning, Mr. Clark will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality. The session will include an overview of what machine learning is, examine its current and potential impact on industries and organizations, and explain the need for an objective audit. The presentation will conclude with an example of what a machine learning audit would consist of, and what steps would be required to perform one.
Carmelo Iaria, AI Academy - How The AI Academy is accelerating NLP projects w...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/aXPE6IiKRmI
The 2018 Brazilian Presidential Elections represented a tangible demonstration of radical change in the way candidates conduct their campaigns, as the shift from traditional media to social media hit the shore of the largest country in the southern hemisphere.
Analyzing the political agenda, the broadcast TV-based debates and exchange on social media networks was an NLP feast that The AI Academy reckoned was too good to pass. In this panel, we present the work we conducted , and will show how Driverless AI helped us accelerate our NLP experiments thanks to the recent introduction of advanced text analytics recipes.
Bio: Maker/Dreamer/Iconoclast/Chaordic Leader with over 20 years of experience across a number of high-tech industries around the world. Curiosity towards new technologies and the ability to adapt to different cultural and social environments has taken him from a research lab in Italy to a start up in Denmark, to a multinational technology company in Silicon Valley, and ultimately to a leading broadband and video service provider in Brazil. Time and again his career journey has demonstrated his ability to recognize at a very early stage high-potential disruptive ideas and the determination to transform an idea into a real product / service.
Over the past seven years, Carmelo cultivated his passion for innovation by leading major technology incubations at a large Telecom operator, supporting the Brazilian startup ecosystem as a Mentor at a startup accelerator and continuously extending his business and technology knowledge through a blend of formal learning & hands-on projects implementations. His focus over the past few years has been on Data Science and Artificial Intelligence, carrying out in-depth technology investigations, product incubations and solutions development.
By establishing The AI Academy, Carmelo intends to create and foster a rich environment for the study, research and application of Machine/Deep Learning techniques to real-life use cases, bridging the AI gap between talent and Enterprises - and furthermore elevating Brazil's "AIQ", inserting São Paulo on the world's AI Map.
Guiding through a typical Machine Learning PipelineMichael Gerke
Many People are talking about AI and Machine Learning. Here's a quick guideline how to manage ML Projects and what to consider in order to implement machine learning use cases.
This document provides an introduction to machine learning. It begins with an agenda that lists topics such as introduction, theory, top 10 algorithms, recommendations, classification with naive Bayes, linear regression, clustering, principal component analysis, MapReduce, and conclusion. It then discusses what big data is and how data is accumulating at tremendous rates from various sources. It explains the volume, variety, and velocity aspects of big data. The document also provides examples of machine learning applications and discusses extracting insights from data using various algorithms. It discusses issues in machine learning like overfitting and underfitting data and the importance of testing algorithms. The document concludes that machine learning has vast potential but is very difficult to realize that potential as it requires strong mathematics skills.
The document provides an overview of machine learning use cases. It begins with an agenda that will discuss the basic framework for ML projects, model deployment options, and various ML use cases like text classification, image classification, object detection, etc. It then covers the basic 5 step framework for ML projects - defining the problem, planning the solution, acquiring and preparing data, designing and training a model, and deploying the solution. Next, it discusses popular methods for various tasks like image classification, object detection, pose estimation. Finally, it shares several use cases for each task to demonstrate real-world applications.
Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them.
This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you!
Intro to Machine Learning with H2O and AWSSri Ambati
Navdeep Gill @ Galvanize Seattle- May 2016
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
The Machine Learning Audit. MIS ITAC 2017 KeynoteAndrew Clark
As it gains wider adoption, what does machine learning mean for internal auditors and their organizations? With the proliferation of buzzwords and the black box nature of machine learning, Mr. Clark will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality. The session will include an overview of what machine learning is, examine its current and potential impact on industries and organizations, and explain the need for an objective audit. The presentation will conclude with an example of what a machine learning audit would consist of, and what steps would be required to perform one.
Carmelo Iaria, AI Academy - How The AI Academy is accelerating NLP projects w...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/aXPE6IiKRmI
The 2018 Brazilian Presidential Elections represented a tangible demonstration of radical change in the way candidates conduct their campaigns, as the shift from traditional media to social media hit the shore of the largest country in the southern hemisphere.
Analyzing the political agenda, the broadcast TV-based debates and exchange on social media networks was an NLP feast that The AI Academy reckoned was too good to pass. In this panel, we present the work we conducted , and will show how Driverless AI helped us accelerate our NLP experiments thanks to the recent introduction of advanced text analytics recipes.
Bio: Maker/Dreamer/Iconoclast/Chaordic Leader with over 20 years of experience across a number of high-tech industries around the world. Curiosity towards new technologies and the ability to adapt to different cultural and social environments has taken him from a research lab in Italy to a start up in Denmark, to a multinational technology company in Silicon Valley, and ultimately to a leading broadband and video service provider in Brazil. Time and again his career journey has demonstrated his ability to recognize at a very early stage high-potential disruptive ideas and the determination to transform an idea into a real product / service.
Over the past seven years, Carmelo cultivated his passion for innovation by leading major technology incubations at a large Telecom operator, supporting the Brazilian startup ecosystem as a Mentor at a startup accelerator and continuously extending his business and technology knowledge through a blend of formal learning & hands-on projects implementations. His focus over the past few years has been on Data Science and Artificial Intelligence, carrying out in-depth technology investigations, product incubations and solutions development.
By establishing The AI Academy, Carmelo intends to create and foster a rich environment for the study, research and application of Machine/Deep Learning techniques to real-life use cases, bridging the AI gap between talent and Enterprises - and furthermore elevating Brazil's "AIQ", inserting São Paulo on the world's AI Map.
machine learning in the age of big data: new approaches and business applicat...Armando Vieira
Presentation at University of Lisbon on Machine Learning and big data.
Deep learning algorithms and applications to credit risk analysis, churn detection and recommendation algorithms
Best Practices for Scaling Data Science Across the OrganizationChasity Gibson
Effective data science in the enterprise is about aligning the right model, data, and infrastructure with the right outcomes. Most organizations today struggle to unlock the potential of data science to enhance decision-making and drive business value.
Join Forrester and Anaconda for a webinar to learn best practices for scaling data science across your entire organization. Guest speaker Kjell Carlsson, a Forrester Senior Analyst, and Peter Wang, Anaconda CTO, will share their unique perspectives on how to tackle five key challenges facing organizations today:
- Identifying, defining, and prioritizing valuable problems
- Building the right teams
- Leveraging the proper tools and platforms
- Iterating and deploying effectively
- Reaching end-users to generate value
Machine learning is permeating our world. As it gains wider adoption, what does it mean for assurance professionals? This session will help you cut through the buzzwords and discover how machine learning can be leveraged in audit and compliance.
After completing this session, you will be able to:
Understand the two groups of algorithms
Understand the machine learning process
Describe use cases in assurance and compliance
Know where to learn more about machine learning
Leaders across the world are looking out for different strategies thru which they can leverage AI.
Realizing this we have successfully organized an event on "AI 4 Institution Leaders" at Nasik focused on the need for AI for educational institutions for the first time in India.
This document provides an introduction and overview of data science. It discusses Ravishankar Rajagopalan's educational and professional background working in data science. It then covers various topics related to data science including common applications, required skills, the typical project lifecycle, team aspects, career progression, interviews, and resources for learning. Examples of unusual real-world applications are also summarized, such as using machine learning to optimize inventory levels for an oil and gas company and implementing speech recognition to predict customer intent for a call center.
AISF19 - Building Scalable, Kubernetes-Native ML/AI Pipelines with TFX, KubeF...Bill Liu
This document discusses modern machine learning pipelines and popular open source tools to build them. It defines key characteristics of ML pipelines like experiment tracking, hyperparameter optimization, distributed execution, and metadata/data versioning. Popular tools covered are KubeFlow for Kubernetes+TensorFlow, Airflow for data and feature engineering, MLflow for experiment tracking, and TensorFlow Extended (TFX) libraries. The document demonstrates these tools and argues that while the field is emerging, simplicity is important and one should only use necessary components of different tools.
As it gains wider adoption, what does machine learning mean for internal auditors and their organizations? With the proliferation of buzzwords and the black box nature of machine learning, Mr. Clark will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality. The session will include an overview of what machine learning is, examine its current and potential impact on industries and organizations, and explain the need for an objective audit. The presentation will conclude with an example of what a machine learning audit would consist of, and what steps would be required to perform one.
JU Analytics Day Presentation by Naveen Agarwal, Creative Analytics Solutions...Naveen Agarwal
This document discusses opportunities and challenges in big data analytics for professionals. It begins with an introduction by Naveen Agarwal about himself and his work at Johnson & Johnson Vision Care analyzing big data. The document then covers topics like what constitutes big data, why big data potential has been difficult to realize, assessing an organization's maturity with big data, and case studies of analytics projects at J&J Vision Care addressing questions in areas like product quality, sales forecasting, and cannibalization. It also discusses roles for data professionals like business analysts, data scientists, software engineers and the skills required for these roles.
These are slides for a guest talk I gave for course 15.S14: Global Business of Artificial Intelligence and Robotics (GBAIR) taught in Spring 2017. Here is the YouTube video (filmed in 360/VR): https://youtu.be/s3MuSOl1Rog
AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
The success of data-driven solutions to dicult problems,
along with the dropping costs of storing and processing mas-
sive amounts of data, has led to growing interest in large-
scale machine learning. This paper presents a case study
of Twitter's integration of machine learning tools into its
existing Hadoop-based, Pig-centric analytics platform. We
begin with an overview of this platform, which handles \tra-
ditional" data warehousing and business intelligence tasks
for the organization. The core of this work lies in recent Pig
extensions to provide predictive analytics capabilities that
incorporate machine learning, focused specically on super-
vised classication. In particular, we have identied stochas-
tic gradient descent techniques for online learning and en-
semble methods as being highly amenable to scaling out to
large amounts of data. In our deployed solution, common
machine learning tasks such as data sampling, feature gen-
eration, training, and testing can be accomplished directly
in Pig, via carefully crafted loaders, storage functions, and
user-dened functions. This means that machine learning
is just another Pig script, which allows seamless integration
with existing infrastructure for data management, schedul-
ing, and monitoring in a production environment, as well
as access to rich libraries of user-dened functions and the
materialized output of other scripts.
Crowdsourced Data Processing: Industry and Academic PerspectivesAditya Parameswaran
This document provides a tutorial on crowdsourced data processing from both academic and industry perspectives. The tutorial is divided into three parts. Part 0 provides a background on crowdsourcing and surveys Parts 1 and 2. Part 1 surveys crowdsourced data processing algorithms from academia, discussing unit operations, cost models, error models, and examples like filtering and sorting. Part 2 surveys crowdsourced data processing in industry, finding that many large companies use internal platforms at large scale for tasks like categorization and content moderation, and that academic research is not yet widely used in industry.
How to design your ML application to be production ready from the day one
How to switch from notebooks to deployable and maintainable software
How to deploy, serve and monitor prediction pipelines
How to re-train models in production
How to shift machine learning experimentation phase to production
TechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - TrivadisTrivadis
This document provides an overview of artificial intelligence trends and applications in development and operations. It discusses how AI is being used for rapid prototyping, intelligent programming assistants, automatic error handling and code refactoring, and strategic decision making. Examples are given of AI tools from Microsoft, Facebook, and Codota. The document also discusses challenges like interpretability of neural networks and outlines a vision of "Software 2.0" where programs are generated automatically to satisfy goals. It emphasizes that AI will transform software development over the next 10 years.
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Big Data and Data Science: The Technologies Shaping Our LivesRukshan Batuwita
Big Data and Data Science have become increasingly imperative areas in both industry and academia to the extent that every company wants to hire a Data Scientist and every university wants to start dedicated degree programs and centres of excellence in Data Science. Big Data and Data Science have led to technologies that have already shaped different aspects of our lives such as learning, working, travelling, purchasing, social relationships, entertainments, physical activities, medical treatments, etc. This talk will attempt to cover the landscape of some of the important topics in these exponentially growing areas of Data Science and Big Data including the state-of-the-art processes, commercial and open-source platforms, data processing and analytics algorithms (specially large scale Machine Learning), application areas in academia and industry, the best industry practices, business challenges and what it takes to become a Data Scientist.
Practical model management in the age of Data science and MLQuantUniversity
Sri Krishnamurthy presents on practical model risk management in the age of data science and machine learning. He discusses how machine learning and AI are driving paradigm shifts in finance. However, he cautions that claims about machine learning capabilities need to be balanced with realities about data and model quality. Key challenges include ensuring interpretability, transparency, and proper evaluation of models in production. He promotes his company's solutions for addressing these challenges through end-to-end workflow management and model governance tools.
How to Become a Data Scientist
SF Data Science Meetup, June 30, 2014
Video of this talk is available here: https://www.youtube.com/watch?v=c52IOlnPw08
More information at: http://www.zipfianacademy.com
Zipfian Academy @ Crowdflower
Big Data for Data Scientists - Info SessionWeCloudData
In this talk, WeCloudData introduces the Hadoop/Spark ecosystem and how businesses use big data tools and platforms. For more detail about WeCloudData's big data for data scientist course please visit: https://weclouddata.com/data-science/
Building machine learning muscle in your team & transitioning to make them do machine learning at scale. We also discuss about Spark & other relevant technologies.
machine learning in the age of big data: new approaches and business applicat...Armando Vieira
Presentation at University of Lisbon on Machine Learning and big data.
Deep learning algorithms and applications to credit risk analysis, churn detection and recommendation algorithms
Best Practices for Scaling Data Science Across the OrganizationChasity Gibson
Effective data science in the enterprise is about aligning the right model, data, and infrastructure with the right outcomes. Most organizations today struggle to unlock the potential of data science to enhance decision-making and drive business value.
Join Forrester and Anaconda for a webinar to learn best practices for scaling data science across your entire organization. Guest speaker Kjell Carlsson, a Forrester Senior Analyst, and Peter Wang, Anaconda CTO, will share their unique perspectives on how to tackle five key challenges facing organizations today:
- Identifying, defining, and prioritizing valuable problems
- Building the right teams
- Leveraging the proper tools and platforms
- Iterating and deploying effectively
- Reaching end-users to generate value
Machine learning is permeating our world. As it gains wider adoption, what does it mean for assurance professionals? This session will help you cut through the buzzwords and discover how machine learning can be leveraged in audit and compliance.
After completing this session, you will be able to:
Understand the two groups of algorithms
Understand the machine learning process
Describe use cases in assurance and compliance
Know where to learn more about machine learning
Leaders across the world are looking out for different strategies thru which they can leverage AI.
Realizing this we have successfully organized an event on "AI 4 Institution Leaders" at Nasik focused on the need for AI for educational institutions for the first time in India.
This document provides an introduction and overview of data science. It discusses Ravishankar Rajagopalan's educational and professional background working in data science. It then covers various topics related to data science including common applications, required skills, the typical project lifecycle, team aspects, career progression, interviews, and resources for learning. Examples of unusual real-world applications are also summarized, such as using machine learning to optimize inventory levels for an oil and gas company and implementing speech recognition to predict customer intent for a call center.
AISF19 - Building Scalable, Kubernetes-Native ML/AI Pipelines with TFX, KubeF...Bill Liu
This document discusses modern machine learning pipelines and popular open source tools to build them. It defines key characteristics of ML pipelines like experiment tracking, hyperparameter optimization, distributed execution, and metadata/data versioning. Popular tools covered are KubeFlow for Kubernetes+TensorFlow, Airflow for data and feature engineering, MLflow for experiment tracking, and TensorFlow Extended (TFX) libraries. The document demonstrates these tools and argues that while the field is emerging, simplicity is important and one should only use necessary components of different tools.
As it gains wider adoption, what does machine learning mean for internal auditors and their organizations? With the proliferation of buzzwords and the black box nature of machine learning, Mr. Clark will help you cut through the noise and understand what fundamental changes are occurring and what is still more hype than reality. The session will include an overview of what machine learning is, examine its current and potential impact on industries and organizations, and explain the need for an objective audit. The presentation will conclude with an example of what a machine learning audit would consist of, and what steps would be required to perform one.
JU Analytics Day Presentation by Naveen Agarwal, Creative Analytics Solutions...Naveen Agarwal
This document discusses opportunities and challenges in big data analytics for professionals. It begins with an introduction by Naveen Agarwal about himself and his work at Johnson & Johnson Vision Care analyzing big data. The document then covers topics like what constitutes big data, why big data potential has been difficult to realize, assessing an organization's maturity with big data, and case studies of analytics projects at J&J Vision Care addressing questions in areas like product quality, sales forecasting, and cannibalization. It also discusses roles for data professionals like business analysts, data scientists, software engineers and the skills required for these roles.
These are slides for a guest talk I gave for course 15.S14: Global Business of Artificial Intelligence and Robotics (GBAIR) taught in Spring 2017. Here is the YouTube video (filmed in 360/VR): https://youtu.be/s3MuSOl1Rog
AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
The success of data-driven solutions to dicult problems,
along with the dropping costs of storing and processing mas-
sive amounts of data, has led to growing interest in large-
scale machine learning. This paper presents a case study
of Twitter's integration of machine learning tools into its
existing Hadoop-based, Pig-centric analytics platform. We
begin with an overview of this platform, which handles \tra-
ditional" data warehousing and business intelligence tasks
for the organization. The core of this work lies in recent Pig
extensions to provide predictive analytics capabilities that
incorporate machine learning, focused specically on super-
vised classication. In particular, we have identied stochas-
tic gradient descent techniques for online learning and en-
semble methods as being highly amenable to scaling out to
large amounts of data. In our deployed solution, common
machine learning tasks such as data sampling, feature gen-
eration, training, and testing can be accomplished directly
in Pig, via carefully crafted loaders, storage functions, and
user-dened functions. This means that machine learning
is just another Pig script, which allows seamless integration
with existing infrastructure for data management, schedul-
ing, and monitoring in a production environment, as well
as access to rich libraries of user-dened functions and the
materialized output of other scripts.
Crowdsourced Data Processing: Industry and Academic PerspectivesAditya Parameswaran
This document provides a tutorial on crowdsourced data processing from both academic and industry perspectives. The tutorial is divided into three parts. Part 0 provides a background on crowdsourcing and surveys Parts 1 and 2. Part 1 surveys crowdsourced data processing algorithms from academia, discussing unit operations, cost models, error models, and examples like filtering and sorting. Part 2 surveys crowdsourced data processing in industry, finding that many large companies use internal platforms at large scale for tasks like categorization and content moderation, and that academic research is not yet widely used in industry.
How to design your ML application to be production ready from the day one
How to switch from notebooks to deployable and maintainable software
How to deploy, serve and monitor prediction pipelines
How to re-train models in production
How to shift machine learning experimentation phase to production
TechEvent 2019: Artificial Intelligence in Dev & Ops; Martin Luckow - TrivadisTrivadis
This document provides an overview of artificial intelligence trends and applications in development and operations. It discusses how AI is being used for rapid prototyping, intelligent programming assistants, automatic error handling and code refactoring, and strategic decision making. Examples are given of AI tools from Microsoft, Facebook, and Codota. The document also discusses challenges like interpretability of neural networks and outlines a vision of "Software 2.0" where programs are generated automatically to satisfy goals. It emphasizes that AI will transform software development over the next 10 years.
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Big Data and Data Science: The Technologies Shaping Our LivesRukshan Batuwita
Big Data and Data Science have become increasingly imperative areas in both industry and academia to the extent that every company wants to hire a Data Scientist and every university wants to start dedicated degree programs and centres of excellence in Data Science. Big Data and Data Science have led to technologies that have already shaped different aspects of our lives such as learning, working, travelling, purchasing, social relationships, entertainments, physical activities, medical treatments, etc. This talk will attempt to cover the landscape of some of the important topics in these exponentially growing areas of Data Science and Big Data including the state-of-the-art processes, commercial and open-source platforms, data processing and analytics algorithms (specially large scale Machine Learning), application areas in academia and industry, the best industry practices, business challenges and what it takes to become a Data Scientist.
Practical model management in the age of Data science and MLQuantUniversity
Sri Krishnamurthy presents on practical model risk management in the age of data science and machine learning. He discusses how machine learning and AI are driving paradigm shifts in finance. However, he cautions that claims about machine learning capabilities need to be balanced with realities about data and model quality. Key challenges include ensuring interpretability, transparency, and proper evaluation of models in production. He promotes his company's solutions for addressing these challenges through end-to-end workflow management and model governance tools.
How to Become a Data Scientist
SF Data Science Meetup, June 30, 2014
Video of this talk is available here: https://www.youtube.com/watch?v=c52IOlnPw08
More information at: http://www.zipfianacademy.com
Zipfian Academy @ Crowdflower
Big Data for Data Scientists - Info SessionWeCloudData
In this talk, WeCloudData introduces the Hadoop/Spark ecosystem and how businesses use big data tools and platforms. For more detail about WeCloudData's big data for data scientist course please visit: https://weclouddata.com/data-science/
Building machine learning muscle in your team & transitioning to make them do machine learning at scale. We also discuss about Spark & other relevant technologies.
Sara Nash and Urmi Majumder, Principal Consultants at Enterprise Knowledge, presented on April 19, 2023 at KM World in Washington D.C. on the topic of Scaling Knowledge Graph Architectures with AI.
In this presentation, Sara and Urmi defined a Knowledge Graph architecture and reviewed how AI can support the creation and growth of Knowledge Graphs. Drawing from their experience in designing enterprise Knowledge Graphs based on knowledge embedded in unstructured content, Sara and Urmi defined approaches for entity and relationship extraction depending on Enterprise AI maturity and highlighted other key considerations to incorporate AI capabilities into the development of a Knowledge Graph.
View presentation below in order to learn about how:
Assess entity and relationship extraction readiness according to EK’s Extraction Maturity Spectrum and Relationship Extraction Maturity Spectrum.
Utilize knowledge extraction from content to gather important insights into organizational data.
Extract knowledge with three approaches:
RedEx Rule, Auto-Classification Rule, Custom ML Model
Examine key factors such as how to leverage SMEs, iterate AI processes, define use cases, and invest in establishing robust AI models.
Simplifying AI and Machine Learning with Watson StudioDataWorks Summit
Are you seeing benefits from big data, AI and machine learning? Some companies are challenged by the complexity of the tools, access to quality data and the ability to operationalize these technologies. IBM’s Watson Studio addresses the needs of developers, data scientists and business analysts – who need to create, train and deploy machine and deep learning models, analyze and visualize data – all in an easy-to-use platform. Watson Studio supports Apple’s Core ML with Watson Visual Recognition service. It provides a suite of tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data and use that data to build, train and deploy models at scale. When coupled with IBM Watson Knowledge Catalog, it enables companies to create a secure catalog of AI assets including datasets, documents and models. In this session, you will learn how to use these new offerings to solve real world business problems and infuse AI into your business to drive innovation.
Speaker
Sumit Goyal, IBM, Software Engineer
1. MuleSoft would connect NTO's various data sources like CRM, marketing automation, support tickets into Data Cloud for a unified customer profile.
2. Using MuleSoft, NTO could continuously sync and transform data from these sources in real-time into Data Cloud.
3. Within Data Cloud, NTO could use Einstein analytics and AI to gain insights from the unified customer data and identify high value audiences.
4. MuleSoft flows and Anypoint Platform would then automate actions like personalized promotions or service requests back to the appropriate systems based on customer insights from Data Cloud.
Organize & manage master meta data centrally, built upon kong, cassandra, neo4j & elasticsearch. Managing master & meta data is a very common problem with no good opensource alternative as far as I know, so initiating this project – MasterMetaData.
Purpose of this presentation is to highlight how end to end machine learning looks like in real world enterprise. This is to provide insight to aspiring data scientist who have been through courses or education in ML that mostly focus on ML algorithms and not end to end pipeline.
Architecture and components mentioned in Slide 11 will be discussed in detailed in series of post on LinkedIn over the course of next few month
To get updates on this follow me on LinkedIn or search/follow hashtag #end2endDS. Post will be active in August 2019 and will be posted till September 2019
Cloud Machine Learning can help make sense of unstructured data, which accounts for 90% of enterprise data. It provides a fully managed machine learning service to train models using TensorFlow and automatically maximize predictive accuracy with hyperparameter tuning. Key benefits include scalable training and prediction infrastructure, integrated tools like Cloud Datalab for exploring data and developing models, and pay-as-you-go pricing.
Exploring Data Modeling Techniques in Modern Data Warehousespriyanka rajput
This article delves deep into data modeling techniques in modern data warehouses, shedding light on their significance and various approaches. If you are aspiring to be a data analyst or data scientist, understanding data modeling is essential, making a Data Analytics Course in Bangalore, Lucknow, Bangalore, Pune, Delhi, Mumbai, Gandhinagar, and other cities across India an attractive proposition.
GDG Cloud Southlake #3 Charles Adetiloye: Enterprise MLOps in PracticeJames Anderson
Charles is a Lead ML platforms engineer at MavenCode. He has well over 15 years of experience building large-scale, distributed applications. Topic: Enterprise MLOps in Practice. How to efficiently get your Machine Learning Models from Notebooks to Production!
Driving Customer Loyalty with Azure Machine LearningCCG
Learn how you can leverage the elastic, on-demand processing power of Microsoft Azure to create faster, more applicable analytics by viewing this informative webinar. Data Scientist and Author, Ahmed Sherif, demonstrates key analytic use cases that can be spun up quickly with minimal effort and maximum return on investment. To watch the full recording of this webinar, visit http://ccgbi.com/resources/webinars/driving-customer-loyalty-with-AML
Data Science Operationalization: The Journey of Enterprise AIDenodo
Watch full webinar here: https://bit.ly/3kVmYJl
As we move into a world driven by AI initiatives, we find ourselves facing new and diverse challenges when it comes to operationalization. Creating a solution and putting it into practice, is certainly not the same. The challenges span various organizational and data facades. In many instances, the data scientists may be working in silos and connecting to the live data may not always be possible. But how does one guarantee their developed model in a silo is still relevant to live data? How can we manage the data flow and data access across the entire AI operationalization cycle?
Watch on-demand to explore:
- The journey and challenges of the Data Scientist
- How Denodo data virtualization with data movement streamlines operationalization
- The best practices and techniques when dealing with siloed data
- How customers have used data virtualization in their data science initiatives
Building Data Science into Organizations: Field ExperienceDatabricks
We will share our experiences in building Data Science and Machine Learning (DS/ML) into organizations. As new DS/ML teams are created, many wrestle with questions such as: How can we most efficiently achieve short-term goals while planning for scale and production long-term? How should DS/ML be incorporated into a company?
We will bring unique perspectives: one as a previous Databricks customer leading a DS team, one as the second ML engineer at Databricks, and both as current Solutions Architects guiding customers through their DS/ML journeys.We will cover best practices through the crawl-walk-run journey of DS/ML: how to immediately become more productive with an initial team, how to scale and move towards production when needed, and how to integrate effectively with the broader organization.
This talk is meant for technical leaders who are building new DS/ML teams or helping to spread DS/ML practices across their organizations. Technology discussion will focus on Databricks, but the lessons apply to any tech platforms in this space.
Want to know more about Common Data Model and Service? You need to understant what's the difference between CDS for Apps and Analytics? Feel free to use these slides and send me your feed backs.
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationDavid Solomon
The initial version of a maturity roadmap to help guide businesses when adopting AI technology into their workflow. IBM Watson Studio is referenced as an example of technology that can help in accelerating the adoption process.
Experimentation to Industrialization: Implementing MLOpsDatabricks
In this presentation, drawing upon Thorogood’s experience with a customer’s global Data & Analytics division as their MLOps delivery partner, we share important learnings and takeaways from delivering productionized ML solutions and shaping MLOps best practices and organizational standards needed to be successful.
We open by providing high-level context & answering key questions such as “What is MLOps exactly?” & “What are the benefits of establishing MLOps Standards?”
The subsequent presentation focuses on our learnings & best practices. We start by discussing common challenges when refactoring experimentation use-cases & how to best get ahead of these issues in a global organization. We then outline an Engagement Model for MLOps addressing: People, Processes, and Tools. ‘Processes’ highlights how to manage the often siloed data science use case demand pipeline for MLOps & documentation to facilitate seamless integration with an MLOps framework. ‘People’ provides context around the appropriate team structures & roles to be involved in an MLOps initiative. ‘Tools’ addresses key requirements of tools used for MLOps, considering the match of services to use-cases.
Brochure data science learning path board-infinity (1)NirupamNishant2
Board Infinity is a best digital marketing and data science institute in mumbai, which is a full-stack career platform for students and jobseekers enabled by personalised learning paths,career coaches and access to various job oppurtunities. We provide online and offline training in Data Science, Digital Marketing, Full stack Web Development,Product management< machine learning and Atrificial Intelligence,Online career counselling and other career solutions
Hadoop is a Java framework for managing large datasets distributed across clusters of commodity hardware. It allows for the distributed processing of large datasets across clusters of computers using simple programming models. Hadoop features distributed storage and processing of data and is designed to scale up from single servers to thousands of machines, each offering local computation and storage. It provides reliable, scalable, and distributed computing and storage for big data applications.
The document discusses the role of a data engineer. It states that data engineers transform data into a format that can be analyzed, developing and maintaining infrastructures for data generation. They work closely with data scientists to architect solutions that enable data analysis. Data engineers require a variety of technical skills and the ability to approach problems creatively. Their responsibilities include building data pipelines and infrastructure for extraction, transformation, and loading of data. Required skills listed include experience with distributed systems, cloud platforms, programming languages like Python and Scala, databases, data processing technologies like Spark and Hadoop, data warehousing, and software engineering practices.
AWS Well Architected-Info Session WeCloudDataWeCloudData
This document provides an overview of Big Data on AWS and discusses key concepts related to architecting Big Data solutions on AWS. It covers topics such as data security, scalability, performance efficiency, cost optimization, operational excellence, reliability, and disaster recovery. It includes examples of AWS services for Big Data like Amazon S3, DynamoDB, Redshift, EMR, and provides sample questions related to choosing the right AWS services for scenarios and designing Big Data architectures.
Data Engineering Course Syllabus - WeCloudDataWeCloudData
This document provides information about the Programming for Data Engineers course offered by WeCloudData. The course teaches essential programming skills for data engineering such as Scala, Spark, Linux, and Docker over 10 sessions. Students will learn key topics like Scala programming, Spark fundamentals, and how to build data pipelines. They will also complete hands-on projects and get interview preparation support to help find jobs as a data engineer.
Machine learning in Healthcare - WeCloudDataWeCloudData
This document provides an overview of machine learning applications in healthcare. It discusses how machine learning can be used to improve diagnosis, treatment, and other areas by automating processes and analyzing patient information. Different types of health data that can be used as inputs for machine learning models are described, including medical information, molecular features, and medical images. Common machine learning tasks for images like detection, segmentation, and diagnosis are also outlined. The document then explains the basic machine learning process of gathering and cleaning data, building and evaluating models, and deploying selected models. Common machine learning algorithms like linear regression, regularization techniques, and deep learning approaches like convolutional neural networks are briefly introduced.
Deep Learning Introduction - WeCloudDataWeCloudData
This document provides an overview of machine learning and deep learning concepts including:
- Machine learning basics such as supervised vs. unsupervised learning and performance measures.
- A brief history of deep learning and basics such as neural networks.
- Linear algebra concepts from vectors to tensors that are important for machine learning.
- Specific machine learning algorithms including linear regression, logistic regression, and TensorFlow basics for defining and executing computation graphs.
Introduction to Machine Learning - WeCloudDataWeCloudData
WeCloudData offers data science training programs and customized corporate training. They have 21 part-time instructors and 2 full-time instructors with expertise in tools like Python, Spark, and AWS. WeCloudData organizes data science meetup events and conferences, and provides workshops at various conferences. Their Applied Machine Learning course teaches tools and techniques over 12 sessions, includes a hands-on project, and helps with interview preparation.
This document discusses trends in data science and the use of Python. It provides an overview of WeCloudData's education and training programs in data science, machine learning, big data, cloud computing, and artificial intelligence. It describes various part-time and full-time learning paths covering topics such as Python, SQL, machine learning algorithms, deep learning, data engineering, big data tools and platforms, and cloud computing with AWS. It also includes information on career services and past student outcomes like job placements and salaries.
This document provides information about an online SQL course for data science. The course is designed for beginners to learn essential SQL skills and get experience through hands-on projects. Students will learn SQL concepts and their real-world applications in industries like banking, retail and more. They will complete three projects analyzing real data sets and also be prepared for SQL interviews. The goal is for students to gain confidence in their SQL abilities for jobs in data analytics.
This document provides an overview of Python for data science. It introduces Python and its ecosystem for data science, including libraries for data analysis (Pandas), visualization (Matplotlib, Seaborn), machine learning (scikit-learn), and big data processing (Spark). It also outlines common data types and how to manipulate tabular data in Python.
Data Science Career Insights by WeCloudDataWeCloudData
This document provides information on data science career paths and training programs from WeCloudData. It includes an overview of WeCloudData's college diploma programs in data science, data engineering, AI, and analytics. It also describes WeCloudData's consulting, corporate training, and career services. The document outlines various learning paths for part-time programs in data science, big data, AWS, and AI. It provides details on WeCloudData's full-time data science immersive program, including the syllabus. Finally, it discusses factors for landing data science jobs and the data science job market in Canada.
The document discusses Precima's analytics processes and pipeline. It describes moving from on-premise systems like SAS and shell scripting to using AWS services like S3, Control-M, Luigi, and Redshift. It outlines considerations for pipeline design and reviews both past and current systems. The future vision involves using Databricks for data pipelines and Snowflake for queries, allowing decoupled, scalable computing and storage.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
3. WCD works with some of the most
talented and experienced data
science experts to deliver public
and corporate trainings. We
currently have 21 part-time and 2
full-time instructors.
Our instructors bring their analytical
expertise from various industries,
teach students advanced tools
such as Python, Hadoop, Spark,
and AWS, mentor students on end-
to-end data projects.
Introduction
Faculty Team
21
Instructors
10
Teaching
Assistants
4. Python for SAS
and SQL Users
Machine
Learning |
Deep Learning
Big Data
Executive
Workshops
Product & Services
Corporate Training
We offer customized corporate training to Canadian
companies with flexible schedules and learning
support!
We help train, upskill, and
reskill data teams!
5. Python for SAS Users
Machine Learning
Big Data
AI/DS for Executives
Corporate Data Programs
We’ve delivered customized trainings to many large Canadian companies
WeCloudData
Corporate
Program
We offer customized corporate training to Canadian
companies with flexible schedules and learning support!
We help train, upskill,
and reskill data teams!
7. Upcoming Events
Schedule
Track
Meetup
Org
Topic Date
Data Science WCD Introduction to Machine Learning May 29
Big Data WCD Big Data for Data Scientist – Open Class Jun 4
Big Data WCD Spark on Kubernetes Jun 5
Big Data Lightbend Kafka in Jail with Strimzi Jun 11
Cloud
Big Data & AI
Conference
Machine Learning from
Experimentation to Production on AWS
Jun 12
Big Data
Big Data & AI
Conference
Transforming big data from On-premise
to the Cloud
Jun 12
Data Science
Big Data & AI
Conference
Spark for Data Science Jun 13
Data Science
Big Data & AI
Conference
Moving Towards a Python Environment Jun 13
tordatascience
8. Workshop Provider
Conference/Clients
Workshop Provider
TMLS Conference
November, 2018
Workshop Provider
TD Canada
Analytics Month
October, 2018
• Machine Learning Open Data
• Spark ML and MLflow
• Deep Learning with PyTorch
• Python for SAS Users
• Machine Learning with Python
Workshop Provider
Big Data & AI
Toronto 2019
June, 2019
• Big Data in AWS Cloud
• Spark for Data Science
• Moving from On-Prem to Cloud
WeCloudData is the conference workshop choice of vendors in Toronto due to our expertise and specialty.
9. Analytics Events
We help companies with hiring/branding events
WeCloudData organizes one of the
largest and most active data science
communities in Toronto with 7,500
members and 110 past events. We help
companies facilitate mini-conferences
and help them run hiring events.
10. 2005
2007
2008 2010
2011
2015
2012
2014 2016 2018
Instructor
Shaohua Zhang
• Co-founder and CEO of WeCloudData. Lead instructor for the corporate training program
• Certified SAS Predictive Modeler since 2007 (among the first 20 in the world)
• Helped build and lead the data science team at BlackBerry (2010 – 2015)
• Helping Communitech incubator and Open Data Exchange mentor startups on data strategies
• Specializes in machine learning, big data, and cloud computing
11. Learning Path
Data Science Program
Prerequisites
Data Science
Learning Path
Learn to build ML
models using
Sklearn
ML Applied
Master data
wrangling with
Python
Data Science
w/ Python
Harness big data
with Hadoop, Hive,
Presto, and
AtScale
Big Data
Build your portfolio
with hands-on
Capstone projects
ML Advanced
Machine Learning
at Scale with
PySpark ML and
Real-time
Deployment
Spark
Contact us about the courses:
• info@weclouddata.com
Upcoming courses:
• https://weclouddata.com/upcoming-course-schedule
17. Data Scientist
The Types
Operational DS
Focus: data wrangling, work with
large/small messy data, builds
predictive models
Strength: data handling, tools, business
knowledge
ML Engineer
Focus: ML model deployment, data
pipelines
Strength: coding, algorithms, machine
learning, platforms and tools
ML Researcher
Focus: algorithm development,
research, IP
Strength: ML/DL algorithms,
implmentation, research
DS Product Mngr
Focus: product strategy, business
communications, project management
Strength: product sense, business
requirements, DS acumen
18. Predictive
Modeler
GrowthAcquisition Maturity Decline Loss
● Lead Gen
● Digital Mktg
● Mobile Ads
● Cross/Up-sell
● Segmentation
● CLTV
● Taste graph
● Personalization
● Loyalty Management
● Context-based Mkgt
● Churn models
● Retention
Acquisition
Models
LTV Loyalty
Management
Retention Winback
Customer
Value
● Winback
models
Predict high risk customers
56. Complex Model Interpretation – Feature Importance
Feature Importance plots are quite common for explaining the models. But it’s not ideal. For
instance, it doesn’t get any indication of the direction of the relationship, whether it’s linear
or non-linear.
57. Complex Model Interpretation – Feature Importance
Feature Importance plots are quite common for explaining the models. But it’s not ideal. For
instance, it doesn’t get any indication of the direction of the relationship, whether it’s linear
or non-linear.
58. Complex Model Interpretation – LIME
Lime is short for Local Interpretable Model-Agnostic Explanations. Each part of the name reflects
something that we desire in explanations. Local refers to local fidelity - i.e., we want the explanation to
really reflect the behaviour of the classifier "around" the instance being predicted.This explanation is
useless unless it is interpretable - that is, unless a human can make sense of it. Lime is able to explain
any model without needing to 'peak' into it, so it is model-agnostic.
All previously mentioned methods can give an idea about the global behavior of
the model. They fail to tell why a particular instance is classified one way or the
other.
1. Perturb the observation
2. Calculate distance between permuted data and
original observations
3. Make predictions on the permuted data using
complex model
4. Pick m features best describing the complex model
outcome from the permuted data
5. Fit a simple model to the permuted data with m
features and similarity scores as weights
6. Feature weights from the simple model make
explanations for the complex models local behavior
61. Applied Machine Learning
Instructor – Jodie Zhu
• Machine Learning Engineer at Dessa
• University of Toronto, Master of Science (Biostatistics)
• Python Instructor at WeCloudData
• Career development mentor
• Expertise: Python | Data Science | Deep Learning
Machine Learning Engineer
Dessa
62. Python Programming
Instructor – Holly Xie
• Machine Learning Scientist at integrate.ai
• University of Waterloo, Master of Mathematics
• Machine Learning Instructor at WeCloudData
• Expertise: Machine Learning| Deep Learning
Machine Learning Scientist
Integrate.ai
63. Applied Machine Learning
Hands-on Project
This course is instructor-led and project-based. Students will be able to apply the Machine
Learning knowledge acquired in the course to a hands-on project.
Project:
• The instructor will work with the students to decide the project topics. It is highly
recommended that the students bring their own motivation and ideas. Otherwise, a
topic along with datasets will be assigned to the students
• The student is also encouraged to apply the learnings directly to his/her company’s
data problems and receive technical advice from the instructor
64. Applied Machine Learning
Interview Practice
For job seekers, this course also
provides supplementary materials to
help you prepare for data science
interviews
Interview Help
• Common ML interview questions
• Mock interview quiz
67. Upcoming Events
Schedule
Track
Meetup
Org
Topic Date
Data Science WCD Introduction to Machine Learning May 29
Big Data WCD Big Data for Data Scientist – Open Class Jun 4
Big Data WCD Spark on Kubernetes Jun 5
Big Data Lightbend Kafka in Jail with Strimzi Jun 11
Cloud
Big Data & AI
Conference
Machine Learning from
Experimentation to Production on AWS
Jun 12
Big Data
Big Data & AI
Conference
Transforming big data from On-premise
to the Cloud
Jun 12
Data Science
Big Data & AI
Conference
Spark for Data Science Jun 13
Data Science
Big Data & AI
Conference
Moving Towards a Python Environment Jun 13
tordatascience