"π Data Relationships: Understand the relationships between variables. Correlation doesn't always imply causation. Dig deeper to uncover the underlying reasons behind observed patterns. #DataConnections"
π1
π 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"
"π Visual Storytelling: Use data visualization to tell a compelling story. Visuals make complex data accessible and engaging, enabling better communication of insights. #VisualStorytelling"
β€1
"π¬ 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 π πΉ
But, how to leverage it for the business impact? What are the right steps?
βοΈClearly 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:
π Uses public models with minimal customization at a lower cost.
π€ Integrates existing models with internal systems for more customized results, suitable for scaling AI capabilities.
πDevelops a unique foundation model for a specific business case, which requires substantial investment.
βοΈDevelop 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.
βοΈQuickly understand and leverage Generative AI for faster code development, streamlined debt management, and automation of routine IT tasks.
βοΈIntegrate generative AI models within your existing tech architecture and develop a robust data infrastructure and comprehensive policy management.
βοΈCreate a cross-functional AI platform team, developing a strategic approach to tool and service selection, and upskilling key roles.
βοΈUse 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.
βοΈUpgrade enterprise tech architecture to accomodate generative AI models with existing AI models, apps, and data sources.
βοΈDevelop a data architecture that can process both structured and unstructured data.
βοΈEstablish a centralized, cross-functional generative AI platform team to provide models to product and application teams on demand.
βοΈUpskill tech roles, such as software developers, data engineers, MLOps engineers, ethical and security experts, and provide training for the broader non-tech workforce.
βοΈAssess the new risks and hav an ongoing mitigation practices to manage models, data, and policies.
βοΈFor 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 π πΉ
But, how to leverage it for the business impact? What are the right steps?
βοΈClearly 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:
π Uses public models with minimal customization at a lower cost.
π€ Integrates existing models with internal systems for more customized results, suitable for scaling AI capabilities.
πDevelops a unique foundation model for a specific business case, which requires substantial investment.
βοΈDevelop 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.
βοΈQuickly understand and leverage Generative AI for faster code development, streamlined debt management, and automation of routine IT tasks.
βοΈIntegrate generative AI models within your existing tech architecture and develop a robust data infrastructure and comprehensive policy management.
βοΈCreate a cross-functional AI platform team, developing a strategic approach to tool and service selection, and upskilling key roles.
βοΈUse 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.
βοΈUpgrade enterprise tech architecture to accomodate generative AI models with existing AI models, apps, and data sources.
βοΈDevelop a data architecture that can process both structured and unstructured data.
βοΈEstablish a centralized, cross-functional generative AI platform team to provide models to product and application teams on demand.
βοΈUpskill tech roles, such as software developers, data engineers, MLOps engineers, ethical and security experts, and provide training for the broader non-tech workforce.
βοΈAssess the new risks and hav an ongoing mitigation practices to manage models, data, and policies.
βοΈFor 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.
π4
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.
π2
Chapman_&_Hall_CRC_machine_learning_&_pattern_recognition_series.pdf
14.6 MB
π Title: Data science and machine learning (2020)
π4
π° Complete SQL + Databases Bootcamp
β± 24.5 Hours π¦ 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://t.iss.one/sqlanalyst/38
Download All Courses: https://t.iss.one/sqlspecialist
β± 24.5 Hours π¦ 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://t.iss.one/sqlanalyst/38
Download All Courses: https://t.iss.one/sqlspecialist
π3β€1
π°How AI Helped Chandrayaan-3 Achieve Its Lunar Mission? π‘π π‘
ISROβs Chandrayaan-3, the third lunar mission has set history by touching down on moonβs 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 spacecraftβs design is being optimized for weight, performance, and safety using AI algorithms.
ISROβs Chandrayaan-3, the third lunar mission has set history by touching down on moonβs 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 spacecraftβs design is being optimized for weight, performance, and safety using AI algorithms.
π4
π¨βπThe Best Courses for AI from Universities with YouTube Playlists
Stanford University Courses
β’CS221 - Artificial Intelligence: Principles and Techniques
β’CS224U: Natural Language Understanding
β’CS224n - Natural Language Processing with Deep Learning
β’CS229 - Machine Learning
β’CS230 - Deep Learning
β’CS231n - Convolutional Neural Networks for Visual Recognition
β’CS234 - Reinforcement Learning
β’CS330 - Deep Multi-task and Meta-Learning
β’CS25 - Transformers United
Carnegie Mellon University Courses
β’CS 10-708: Probabilistic Graphical Models
β’CS/LTI 11-711: Advanced NLP
β’CS/LTI 11-737: Multilingual NLP
β’CS/LTI 11-747: Neural Networks for NLP
β’CS/LTI 11-785: Introduction to Deep Learning
β’CS/LTI 11-785: Neural Networks
Massachusetts Institute of Technology Courses
β’Introduction to Algorithms
β’Introduction to Deep Learning
β’6.S094 - Deep Learning
DeepMind x UCL
β’COMP M050 - Introduction to Reinforcement Learning
β’Deep Learning Series
Stanford University Courses
β’CS221 - Artificial Intelligence: Principles and Techniques
β’CS224U: Natural Language Understanding
β’CS224n - Natural Language Processing with Deep Learning
β’CS229 - Machine Learning
β’CS230 - Deep Learning
β’CS231n - Convolutional Neural Networks for Visual Recognition
β’CS234 - Reinforcement Learning
β’CS330 - Deep Multi-task and Meta-Learning
β’CS25 - Transformers United
Carnegie Mellon University Courses
β’CS 10-708: Probabilistic Graphical Models
β’CS/LTI 11-711: Advanced NLP
β’CS/LTI 11-737: Multilingual NLP
β’CS/LTI 11-747: Neural Networks for NLP
β’CS/LTI 11-785: Introduction to Deep Learning
β’CS/LTI 11-785: Neural Networks
Massachusetts Institute of Technology Courses
β’Introduction to Algorithms
β’Introduction to Deep Learning
β’6.S094 - Deep Learning
DeepMind x UCL
β’COMP M050 - Introduction to Reinforcement Learning
β’Deep Learning Series
π16π3β€1
Where to get data for your next machine learning project?
An overview of 5 amazing resources to accelerate your next project with data!
π 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.
π Kaggle Dataset
Explore, analyze, and share quality data.
π Open Data on AWS
This registry exists to help people discover and share datasets that are available via AWS resources
π Awesome Public Datasets
A topic-centric list of HQ open datasets.
π Azure public data sets
Public data sets for testing and prototyping.
An overview of 5 amazing resources to accelerate your next project with data!
π 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.
π Kaggle Dataset
Explore, analyze, and share quality data.
π Open Data on AWS
This registry exists to help people discover and share datasets that are available via AWS resources
π Awesome Public Datasets
A topic-centric list of HQ open datasets.
π Azure public data sets
Public data sets for testing and prototyping.
π4
Practical Data Science with Jupyter.pdf
19.5 MB
π Title: Practical Data Science with Jupyter (2021)
π7
π₯ Roadmap of free courses for learning Python and Machine learning.
βͺData Science
βͺ AI/ML
βͺ Web Dev
1. Start with this
https://kaggle.com/learn/python
2. Take any one of these
β― https://openclassrooms.com/courses/6900856-learn-programming-with-python
β― https://t.iss.one/pythondevelopersindia/76
β― https://simplilearn.com/learn-python-basics-free-course-skillup
3. Then take this
https://netacad.com/courses/programming/pcap-programming-essentials-python
4. Attempt for this certification
https://freecodecamp.org/learn/scientific-computing-with-python/
5. Take it to next level
β― Data Scrapping, NumPy, Pandas
https://scaler.com/topics/course/python-for-data-science/
β― Data Analysis
https://openclassrooms.com/courses/2304731-learn-python-basics-for-data-analysis
β― Data Visualization
https://kaggle.com/learn/data-visualization
β― Django
https://openclassrooms.com/courses/6967196-create-a-web-application-with-django
β― Machine Learning
https://developers.google.com/machine-learning/crash-course
https://t.iss.one/datasciencefun/290
β― Deep Learning (TensorFlow)
https://kaggle.com/learn/intro-to-deep-learning
Please more reaction with our posts
βͺData Science
βͺ AI/ML
βͺ Web Dev
1. Start with this
https://kaggle.com/learn/python
2. Take any one of these
β― https://openclassrooms.com/courses/6900856-learn-programming-with-python
β― https://t.iss.one/pythondevelopersindia/76
β― https://simplilearn.com/learn-python-basics-free-course-skillup
3. Then take this
https://netacad.com/courses/programming/pcap-programming-essentials-python
4. Attempt for this certification
https://freecodecamp.org/learn/scientific-computing-with-python/
5. Take it to next level
β― Data Scrapping, NumPy, Pandas
https://scaler.com/topics/course/python-for-data-science/
β― Data Analysis
https://openclassrooms.com/courses/2304731-learn-python-basics-for-data-analysis
β― Data Visualization
https://kaggle.com/learn/data-visualization
β― Django
https://openclassrooms.com/courses/6967196-create-a-web-application-with-django
β― Machine Learning
https://developers.google.com/machine-learning/crash-course
https://t.iss.one/datasciencefun/290
β― Deep Learning (TensorFlow)
https://kaggle.com/learn/intro-to-deep-learning
Please more reaction with our posts
π28β€1
Data Analysis with Excel
ππ
https://t.iss.one/excel_analyst/2
Power BI DAX Functions
ππ
https://t.iss.one/PowerBI_analyst/2
All about SQL
ππ
https://t.iss.one/sqlanalyst/29
Python for data analysis
ππ
https://t.iss.one/pythonanalyst/26
Statistics Book and other useful resources
ππ
https://t.iss.one/DataAnalystInterview/34
Join channel as per your interest :)
ππ
https://t.iss.one/excel_analyst/2
Power BI DAX Functions
ππ
https://t.iss.one/PowerBI_analyst/2
All about SQL
ππ
https://t.iss.one/sqlanalyst/29
Python for data analysis
ππ
https://t.iss.one/pythonanalyst/26
Statistics Book and other useful resources
ππ
https://t.iss.one/DataAnalystInterview/34
Join channel as per your interest :)
π9
theobald-oliver-machine-learning-for-absolute-2020.pdf
15.9 MB
π Title: Machine Learning for Absolute Beginners (2020)
π4
Are you done with watching πππ tutorials but don't know where to practice it?
Check out these top 11 online sources that provide practical exercises and challenges to help you master SQL:
1. SQL Zoo: https://sqlzoo.net/wiki/SQL_Tutorial
2. SQLBolt : https://sqlbolt.com/
3. SQLPad: https://sqlpad.io/
4. Mode: https://mode.com/
5. Strata Scratch: https://www.stratascratch.com/
6. LeetCode: https://leetcode.com/problemset/all/
7. HackerRank: https://www.hackerrank.com/domains/sql
8. W3 Schools: https://www.w3schools.com/sql/default.asp
9. SQL Roadmap: https://t.iss.one/sqlspecialist/386
10. Programiz: https://www.programiz.com/sql
Check out these top 11 online sources that provide practical exercises and challenges to help you master SQL:
1. SQL Zoo: https://sqlzoo.net/wiki/SQL_Tutorial
2. SQLBolt : https://sqlbolt.com/
3. SQLPad: https://sqlpad.io/
4. Mode: https://mode.com/
5. Strata Scratch: https://www.stratascratch.com/
6. LeetCode: https://leetcode.com/problemset/all/
7. HackerRank: https://www.hackerrank.com/domains/sql
8. W3 Schools: https://www.w3schools.com/sql/default.asp
9. SQL Roadmap: https://t.iss.one/sqlspecialist/386
10. Programiz: https://www.programiz.com/sql
Sqlbolt
SQLBolt - Learn SQL - Introduction to SQL
SQLBolt provides a set of interactive lessons and exercises to help you learn SQL
π10β€1