"🔗 Data Relationships: Understand the relationships between variables. Correlation doesn't always imply causation. Dig deeper to uncover the underlying reasons behind observed patterns. #DataConnections"

🔍 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"

"💬 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.

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

📚 Title: Data science and machine learning (2020)

🔰 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

🛰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.

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.

📚 Title: Practical Data Science with Jupyter (2021)

🖥 Roadmap of free courses for learning Python and Machine learning.

▪Data Science
▪ AI/ML
▪ Web Dev

1. Start with this
https://kaggle.com/learn/python

2. Take any one of these

❯ https://openclassrooms.com/courses/6900856-learn-programming-with-python

❯ https://news.1rj.ru/str/pythondevelopersindia/76

❯ 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

❯ Data Scrapping, NumPy, Pandas
https://scaler.com/topics/course/python-for-data-science/

❯ Data Analysis
https://openclassrooms.com/courses/2304731-learn-python-basics-for-data-analysis

❯ Data Visualization
https://kaggle.com/learn/data-visualization

❯ Django
https://openclassrooms.com/courses/6967196-create-a-web-application-with-django

❯ Machine Learning
http://developers.google.com/machine-learning/crash-course

https://news.1rj.ru/str/datasciencefun/290

❯ Deep Learning (TensorFlow)
http://kaggle.com/learn/intro-to-deep-learning

Please more reaction with our posts

Data Analysis with Excel
👇👇
https://news.1rj.ru/str/excel_analyst/2

Power BI DAX Functions
👇👇
https://news.1rj.ru/str/PowerBI_analyst/2

All about SQL
👇👇
https://news.1rj.ru/str/sqlanalyst/29

Python for data analysis
👇👇
https://news.1rj.ru/str/pythonanalyst/26

Statistics Book and other useful resources
👇👇
https://news.1rj.ru/str/DataAnalystInterview/34

Join channel as per your interest :)

📚 Title: Machine Learning for Absolute Beginners (2020)

Are you done with watching 𝐒𝐐𝐋 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://news.1rj.ru/str/sqlspecialist/386

10. Programiz: https://www.programiz.com/sql