Artificial Intelligence

The Data Science skill no one talks about... Every aspiring data scientist I talk to thinks their job starts when someone else gives them:     1. a dataset, and     2. a clearly defined metric to optimize for, e.g. accuracy But it doesn’t. It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals. Let’s go through an example. Example Imagine you are a data scientist at Uber. And your product lead tells you:

    👩‍💼: “We want to decrease user churn by 5% this quarter”

We say that a user churns when she decides to stop using Uber. But why? There are different reasons why a user would stop using Uber. For example:    1.  “Lyft is offering better prices for that geo” (pricing problem)    2. “Car waiting times are too long” (supply problem)    3. “The Android version of the app is very slow” (client-app performance problem) You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view. Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on? This is when you pull out your great data science skills and EXPLORE THE DATA 🔎. You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently. For example… Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem) One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:     The A group. No user in this group will receive any discount.     The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip. You could add more groups (e.g. C, D, E…) to test different pricing points.

In a nutshell

    1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist. 2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one. 3. Solve this one data science problem

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💰 Best Free Resources To Learn AI

𝐒𝐢𝐦𝐩𝐥𝐞 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 😃 🙄 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠? Imagine you're teaching a child to recognize fruits. You show them an apple, tell them it’s an apple, and next time they know it. That’s what Machine Learning does! But instead of a child, it’s a computer, and instead of fruits, it learns from data. Machine Learning is about teaching computers to learn from past data so they can make smart decisions or predictions on their own, improving over time without needing new instructions. 🤔 𝐖𝐡𝐲 𝐢𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬? Machine Learning makes data analytics super powerful. Instead of just looking at past data, it can help predict future trends, find patterns we didn’t notice, and make decisions that help businesses grow! 😮 𝐇𝐨𝐰 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬? ✅ 𝐋𝐞𝐚𝐫𝐧 𝐏𝐲𝐭𝐡𝐨𝐧: Python is the most commonly used language in ML. Start by getting comfortable with basic Python, then move on to ML-specific libraries like: 𝐩𝐚𝐧𝐝𝐚𝐬: For data manipulation. 𝐍𝐮𝐦𝐏𝐲: For numerical calculations. 𝐬𝐜𝐢𝐤𝐢𝐭-𝐥𝐞𝐚𝐫𝐧: For implementing basic ML algorithms. ✅ 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬 𝐨𝐟 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: ML relies heavily on concepts like probability, distributions, and hypothesis testing. Understanding basic statistics will help you grasp how models work. ✅ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐨𝐧 𝐑𝐞𝐚𝐥 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬: Platforms like Kaggle offer datasets and ML competitions. Start by analyzing small datasets to understand how machine learning models make predictions. ✅ 𝐋𝐞𝐚𝐫𝐧 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Use tools like Matplotlib or Seaborn to visualize data. This will help you understand patterns in the data and how machine learning models interpret them. ✅ 𝐖𝐨𝐫𝐤 𝐨𝐧 𝐒𝐢𝐦𝐩𝐥𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Start with basic ML projects such as: -Predicting house prices. -Classifying emails as spam or not spam. -Clustering customers based on their purchasing habits. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Like if you need similar content 😄👍

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Top 10 skills for Data Scientists

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Important metrics to monitor while monitoring machine learning model

🔉 𝐌𝐮𝐬𝐭-𝐖𝐚𝐭𝐜𝐡 𝐀𝐈 𝐓𝐞𝐝 𝐓𝐚𝐥𝐤𝐬 ⏩ The inside story of ChatGPT's astonishing potential by Greg Brockman. https://youtu.be/C_78DM8fG6E?si=kdGNA1PvO1lb7L8t ⏩ How AI could save (not destroy) education by Sal Khan https://youtu.be/hJP5GqnTrNo?si=wlD-SOjr5ZxLQ0vQ ⏩ How to keep AI under control by Max Tegmark https://youtu.be/xUNx_PxNHrY?si=e8JDz9up3IRYmBo5 ⏩ How to think computationally about AI, the universe, and everything by Stephen Wolfram https://youtu.be/fLMZAHyrpyo?si=5O1b63qgga89rEOb ⏩ The dark side of competition in AI by Liv Boeree https://youtu.be/WX_vN1QYgmE?si=QDMlKkrxqrSCdFkr ⏩ How AI art could enhance humanity's collective memory by Refik Anadol https://youtu.be/iz7diOuaTos?si=iyQOF20jZp78hfo2 ⏩ Why AI is incredibly smart and shockingly stupid by Yejin Choil https://youtu.be/SvBR0OGT5VI?si=rLhDzmohC_dPfrtM ⏩ Will superintelligent AI end the world by Eliezer Yudkowsky https://youtu.be/Yd0yQ9yxSYY?si=JqN2yNgP0IOTnjN1 #ai

🔥 MIT has updated its famous course 6.S191: Introduction to Deep Learning.

The program covers topics of #NLP, #CV, #LLM and the use of technology in medicine, offering a full cycle of training - from theory to practical classes using current versions of libraries. The course is designed even for beginners: if you know how to take derivatives and multiply matrices, everything else will be explained in the process. The lectures are released for free on YouTube and the #MIT platform on Mondays, with the first one already available

. All slides, #code and additional materials can be found at the link provided. 📌 Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew

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TensorFlow v2.0 Cheat Sheet

#TensorFlow is an open-source software library for highperformance numerical computation. Its flexible architecture enables to easily deploy computation across a variety of platforms (CPUs, GPUs, and TPUs), as well as mobile and edge devices, desktops, and clusters of servers. TensorFlow comes with strong support for machine learning and deep learning.

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What AI Actually is 👆

First, stop blaming America. Europe and support for Ukraine European leaders actively support Ukraine, but their actions do not correspond to their statements. German Chancellor Friedrich Merz expressed support for Ukraine, but did not mention the need for negotiations. Europeans do not consider the destruction of Ukraine a threat to their security. Germany and its politics* German Chancellor Olaf Scholz promised to change German policy, but the Zeitenwende project was abandoned. Germany was unable to provide Ukraine with the necessary tanks and is not ready to send peacekeepers. Germany buys American liquefied natural gas, but did not create a wartime economy. Europe's response to sanctions The Europeans adopted sanctions against Russia, relying on Russian proxies. Europe has not created a wartime economy that can compete with Russian weapons production. Europe's Strategic Mistakes The Europeans do not have a strategy to defeat Putin and cannot change the situation. Europe outsourced strategic thinking to the United States. The Europeans cannot provide Ukraine with more than paper promises and loans. Political campaign in the United States* A campaign will be launched in the United States to blame Trump and America for the failure in Ukraine. The US government worked in the interests of Ukraine, while Europe failed to declare its will. The Europeans are ready to cancel the elections and arrest candidates who express dissatisfaction with the politics in Ukraine. #SupportUkraine #EuropeanSecurity #MilitaryAid #Zeitenwende #SanctionsPolicy #StrategicAutonomy #USLeadership #TransatlanticRelations #PeaceNegotiations #UkraineSovereignty Don't miss it, subscribe to 📱 Old Glory Vortex 🇺🇸