📈 Context is Key: Interpret your findings in the context of your industry or domain. A seemingly significant trend might be trivial if it doesn't align with what's happening in your field. #ContextMatters"
Encyclopedia of Data Science & Machine Learning-J. Wang.pdf
261.8 MB
Encyclopedia of Data Science and Machine Learning
John Wang, 2023
John Wang, 2023
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"💡 Start Simple: Don't overcomplicate your analysis. Begin with simple approaches and gradually explore more complex techniques as needed. Simplicity often leads to clarity. #StartSimple"
"🔗 Data Relationships: Understand the relationships between variables. Correlation doesn't always imply causation. Dig deeper to uncover the underlying reasons behind observed patterns. #DataConnections"
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🔍 Missing Data Handling: Handle missing data wisely. Ignoring it or filling it with random values can distort results. Choose appropriate methods like imputation based on context. #MissingData"
"📈 Visual Storytelling: Use data visualization to tell a compelling story. Visuals make complex data accessible and engaging, enabling better communication of insights. #VisualStorytelling"
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"💬 Collaboration Matters: Collaborate with domain experts and stakeholders. Their insights can guide your analysis and help you uncover relevant trends and patterns. #CollaborativeInsights"
Generative AI is a multi-billion dollar opportunity!
There will be some winners and losers emerging directly or indirectly impacted by Gen AI 🚀 💹
But, how to leverage it for the business impact? What are the right steps?
✔️Clearly define and communicate company-wide policies for generative AI use, providing access and guidelines to use these tools effectively and safely.
Your business probably falls into one of these types of categories, make sure to identify early and act accordingly:
👀 Uses public models with minimal customization at a lower cost.
🤖 Integrates existing models with internal systems for more customized results, suitable for scaling AI capabilities.
🚀Develops a unique foundation model for a specific business case, which requires substantial investment.
✔️Develop financial AI capabilities to accurately calculate the costs and returns of AI initiatives, considering aspects such as multiple model/vendor costs, usage fees, and human oversight costs.
✔️Quickly understand and leverage Generative AI for faster code development, streamlined debt management, and automation of routine IT tasks.
✔️Integrate generative AI models within your existing tech architecture and develop a robust data infrastructure and comprehensive policy management.
✔️Create a cross-functional AI platform team, developing a strategic approach to tool and service selection, and upskilling key roles.
✔️Use existing services or open-source models as much as possible to develop your own capabilities, keeping in mind the significant costs of building your own models.
✔️Upgrade enterprise tech architecture to accomodate generative AI models with existing AI models, apps, and data sources.
✔️Develop a data architecture that can process both structured and unstructured data.
✔️Establish a centralized, cross-functional generative AI platform team to provide models to product and application teams on demand.
✔️Upskill tech roles, such as software developers, data engineers, MLOps engineers, ethical and security experts, and provide training for the broader non-tech workforce.
✔️Assess the new risks and hav an ongoing mitigation practices to manage models, data, and policies.
✔️For many, it is important to link generative AI models to internal data sources for contextual understanding.
It is important to explore a tailored upskilling programs and talent management strategies.
There will be some winners and losers emerging directly or indirectly impacted by Gen AI 🚀 💹
But, how to leverage it for the business impact? What are the right steps?
✔️Clearly define and communicate company-wide policies for generative AI use, providing access and guidelines to use these tools effectively and safely.
Your business probably falls into one of these types of categories, make sure to identify early and act accordingly:
👀 Uses public models with minimal customization at a lower cost.
🤖 Integrates existing models with internal systems for more customized results, suitable for scaling AI capabilities.
🚀Develops a unique foundation model for a specific business case, which requires substantial investment.
✔️Develop financial AI capabilities to accurately calculate the costs and returns of AI initiatives, considering aspects such as multiple model/vendor costs, usage fees, and human oversight costs.
✔️Quickly understand and leverage Generative AI for faster code development, streamlined debt management, and automation of routine IT tasks.
✔️Integrate generative AI models within your existing tech architecture and develop a robust data infrastructure and comprehensive policy management.
✔️Create a cross-functional AI platform team, developing a strategic approach to tool and service selection, and upskilling key roles.
✔️Use existing services or open-source models as much as possible to develop your own capabilities, keeping in mind the significant costs of building your own models.
✔️Upgrade enterprise tech architecture to accomodate generative AI models with existing AI models, apps, and data sources.
✔️Develop a data architecture that can process both structured and unstructured data.
✔️Establish a centralized, cross-functional generative AI platform team to provide models to product and application teams on demand.
✔️Upskill tech roles, such as software developers, data engineers, MLOps engineers, ethical and security experts, and provide training for the broader non-tech workforce.
✔️Assess the new risks and hav an ongoing mitigation practices to manage models, data, and policies.
✔️For many, it is important to link generative AI models to internal data sources for contextual understanding.
It is important to explore a tailored upskilling programs and talent management strategies.
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8 AI Tools Just for Fun:
1. Tattoo Artist
https://tattoosai.com
2. Talk to Books
https://books.google.com/talktobooks/
3. Vintage Headshots
https://myheritage.com/ai-time-machine
4. Hello to Past
https://hellohistory.ai
5. Fake yourself
https://fakeyou.com
6. Unreal Meal
https://unrealmeal.ai
7. Reface AI
https://hey.reface.ai
8. Voice Changer
https://voicemod.net
1. Tattoo Artist
https://tattoosai.com
2. Talk to Books
https://books.google.com/talktobooks/
3. Vintage Headshots
https://myheritage.com/ai-time-machine
4. Hello to Past
https://hellohistory.ai
5. Fake yourself
https://fakeyou.com
6. Unreal Meal
https://unrealmeal.ai
7. Reface AI
https://hey.reface.ai
8. Voice Changer
https://voicemod.net
Tattoosai
AI-powered Tattoo Generator: Your Personal Tattoo Artist
If you have an idea for a tattoo but can't find the right design, let our AI generate one within seconds. It lets you create the perfect design based on what you like, and it will give you unlimited options so that there's something for everyone.
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Chapman_&_Hall_CRC_machine_learning_&_pattern_recognition_series.pdf
14.6 MB
📚 Title: Data science and machine learning (2020)
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🔰 Complete SQL + Databases Bootcamp
⏱ 24.5 Hours 📦 278 Lessons
Most comprehensive resource online to learn SQL and Database Management & Design + exercises to give you real-world experience working with all database types.
Taught By: Mo Binni, Andrei Neagoie
Download Full Course: https://news.1rj.ru/str/sqlanalyst/38
Download All Courses: https://news.1rj.ru/str/sqlspecialist
⏱ 24.5 Hours 📦 278 Lessons
Most comprehensive resource online to learn SQL and Database Management & Design + exercises to give you real-world experience working with all database types.
Taught By: Mo Binni, Andrei Neagoie
Download Full Course: https://news.1rj.ru/str/sqlanalyst/38
Download All Courses: https://news.1rj.ru/str/sqlspecialist
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🛰How AI Helped Chandrayaan-3 Achieve Its Lunar Mission? 💡🚀 📡
ISRO’s Chandrayaan-3, the third lunar mission has set history by touching down on moon’s surface.
During the last stage of its landing, the Chandrayaan-3 spacecraft has gone through a window of "17 minutes of terror", where it was carrying out a series of maneuvers which was crucial for landing. It included altitude adjustments, firing thrusters, & scanning the surface for any obstacles - all of that was done with the help of AI. During this period, the Chandrayaan-3 team was able to monitor its progress from the ISRO Telemetry, Tracking, & Command Network in Bengaluru, while Al was at the helm of the Vikram lander. ISRO has already confirmed that the lander used autonomously controlled by Al using Machine Learning that operated its guidance,navigation,control & other systems.
Lander & rover, as well as entire ship is designed & developed using AI, The spacecraft’s design is being optimized for weight, performance, and safety using AI algorithms.
ISRO’s Chandrayaan-3, the third lunar mission has set history by touching down on moon’s surface.
During the last stage of its landing, the Chandrayaan-3 spacecraft has gone through a window of "17 minutes of terror", where it was carrying out a series of maneuvers which was crucial for landing. It included altitude adjustments, firing thrusters, & scanning the surface for any obstacles - all of that was done with the help of AI. During this period, the Chandrayaan-3 team was able to monitor its progress from the ISRO Telemetry, Tracking, & Command Network in Bengaluru, while Al was at the helm of the Vikram lander. ISRO has already confirmed that the lander used autonomously controlled by Al using Machine Learning that operated its guidance,navigation,control & other systems.
Lander & rover, as well as entire ship is designed & developed using AI, The spacecraft’s design is being optimized for weight, performance, and safety using AI algorithms.
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👨🎓The Best Courses for AI from Universities with YouTube Playlists
Stanford University Courses
•CS221 - Artificial Intelligence: Principles and Techniques
•CS224U: Natural Language Understanding
•CS224n - Natural Language Processing with Deep Learning
•CS229 - Machine Learning
•CS230 - Deep Learning
•CS231n - Convolutional Neural Networks for Visual Recognition
•CS234 - Reinforcement Learning
•CS330 - Deep Multi-task and Meta-Learning
•CS25 - Transformers United
Carnegie Mellon University Courses
•CS 10-708: Probabilistic Graphical Models
•CS/LTI 11-711: Advanced NLP
•CS/LTI 11-737: Multilingual NLP
•CS/LTI 11-747: Neural Networks for NLP
•CS/LTI 11-785: Introduction to Deep Learning
•CS/LTI 11-785: Neural Networks
Massachusetts Institute of Technology Courses
•Introduction to Algorithms
•Introduction to Deep Learning
•6.S094 - Deep Learning
DeepMind x UCL
•COMP M050 - Introduction to Reinforcement Learning
•Deep Learning Series
Stanford University Courses
•CS221 - Artificial Intelligence: Principles and Techniques
•CS224U: Natural Language Understanding
•CS224n - Natural Language Processing with Deep Learning
•CS229 - Machine Learning
•CS230 - Deep Learning
•CS231n - Convolutional Neural Networks for Visual Recognition
•CS234 - Reinforcement Learning
•CS330 - Deep Multi-task and Meta-Learning
•CS25 - Transformers United
Carnegie Mellon University Courses
•CS 10-708: Probabilistic Graphical Models
•CS/LTI 11-711: Advanced NLP
•CS/LTI 11-737: Multilingual NLP
•CS/LTI 11-747: Neural Networks for NLP
•CS/LTI 11-785: Introduction to Deep Learning
•CS/LTI 11-785: Neural Networks
Massachusetts Institute of Technology Courses
•Introduction to Algorithms
•Introduction to Deep Learning
•6.S094 - Deep Learning
DeepMind x UCL
•COMP M050 - Introduction to Reinforcement Learning
•Deep Learning Series
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Where to get data for your next machine learning project?
An overview of 5 amazing resources to accelerate your next project with data!
📌 Google Datasets
Easy to search Datasets on Google Dataset Search engine as it is to search for anything on Google Search! You just enter the topic on which you need to find a Dataset.
📌 Kaggle Dataset
Explore, analyze, and share quality data.
📌 Open Data on AWS
This registry exists to help people discover and share datasets that are available via AWS resources
📌 Awesome Public Datasets
A topic-centric list of HQ open datasets.
📌 Azure public data sets
Public data sets for testing and prototyping.
An overview of 5 amazing resources to accelerate your next project with data!
📌 Google Datasets
Easy to search Datasets on Google Dataset Search engine as it is to search for anything on Google Search! You just enter the topic on which you need to find a Dataset.
📌 Kaggle Dataset
Explore, analyze, and share quality data.
📌 Open Data on AWS
This registry exists to help people discover and share datasets that are available via AWS resources
📌 Awesome Public Datasets
A topic-centric list of HQ open datasets.
📌 Azure public data sets
Public data sets for testing and prototyping.
👍4
Practical Data Science with Jupyter.pdf
19.5 MB
📚 Title: Practical Data Science with Jupyter (2021)
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