Now is the time to major in computer science, before people realize that AI isn’t going to make it obsolete and enrollment skyrockets again.

Simple & Interesting concept from statsmodels Kindly give you love & support 👍🫡 https://t.co/ZsstWS00r3

These concepts may seem basic, but they form the foundation of machine learning, statistical inference, and decision theory. #DataScience #Probability #Statistics #MachineLearning #Analytics #Fundamentals

🔹 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻 (Expected Value) The expected value 𝐸(𝑋) gives us the long-run average of a random variable. Whether estimating outcomes or evaluating models, 𝗶𝘁’𝘀 𝗸𝗲𝘆 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝘁𝗼 “𝗲𝘅𝗽𝗲𝗰𝘁” 𝗼𝘃𝗲𝗿 𝗺𝗮𝗻𝘆 𝘁𝗿𝗶𝗮𝗹𝘀.

🔹 𝗢𝗱𝗱𝘀 and Their Interpretation Odds give an alternate—but 𝗲𝗾𝘂𝗮𝗹𝗹𝘆 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹—way to express likelihoods. For instance, an event with a 𝗽𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗳 𝟭/𝟲​ has 𝗼𝗱𝗱𝘀 𝗼𝗳 𝟭:𝟱. Useful in areas like 𝗹𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 & etc.

🔹 Rules of 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Understanding events and their probabilities is fundamental. From basic rules like 𝗣(𝗔^𝗰)=𝟭−𝗣(𝗔) to compound events 𝗣(𝗔∪𝗕)=𝗣(𝗔)+𝗣(𝗕)−𝗣(𝗔∩𝗕), these rules guide how we reason under uncertainty.

𝗕𝗮𝗰𝗸 𝘁𝗼 𝗕𝗮𝘀𝗶𝗰𝘀: 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗢𝗱𝗱𝘀 & 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻 🎲 As I delve deeper into data science, it's crucial to revisit and reinforce core statistical concepts that underpin every model we build. Here's a quick refresher on three essentials:

Linear Mixed Models the unsung hero of linear modeling Want to see how it works The PPT says it all #Statistics #DataScience #LinearMixedModel #statsmodels Check the link for PPT : https://t.co/NUEHzOQBvx

https://t.co/QmtipQIovR

If you know, you know...

So True!👀👨‍🎓

neurons🤣

Presenting: A linked list

True!

The linear model, while simpler, does better job guessing future trends. Choose your algorithms wisely! 𝗧𝗿𝗲𝗲-𝗯𝗮𝘀𝗲𝗱 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗿𝗲𝗻'𝘁 𝗮𝗹𝘄𝗮𝘆𝘀 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗰𝗵𝗼𝗶𝗰𝗲, especially when you need to forecast 𝗯𝗲𝘆𝗼𝗻𝗱 𝘆𝗼𝘂𝗿 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗱𝗮𝘁𝗮.

Tree algorithms are great at finding patterns in existing data, but they struggle to predict beyond what they've seen before. 𝗧𝗵𝗲𝘆 𝗰𝗮𝗻'𝘁 "𝗴𝗿𝗼𝘄 𝗻𝗲𝘄 𝗯𝗿𝗮𝗻𝗰𝗵𝗲𝘀" 𝗳𝗼𝗿 𝘂𝗻𝘀𝗲𝗲𝗻 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀.

𝗧𝗿𝗲𝗲-𝗕𝗮𝘀𝗲𝗱 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗔𝗿𝗲𝗻'𝘁 𝗔𝗹𝘄𝗮𝘆𝘀 𝘁𝗵𝗲 𝗕𝗲𝘀𝘁 𝗖𝗵𝗼𝗶𝗰𝗲 𝗳𝗼𝗿 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀—𝗛𝗲𝗿𝗲'𝘀 𝗪𝗵𝘆! Notice how the 𝘁𝗿𝗲𝗲 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 (𝗴𝗿𝗲𝗲𝗻) 𝗴𝗼𝗲𝘀 𝗳𝗹𝗮𝘁 𝗮𝗳𝘁𝗲𝗿 𝟮𝟬𝟬𝟬? That's the big problem.

while @scikit_learn offers a clear method for post-pruning, XGBoost & LightGBM Don't. Knowing differences is crucial for selecting the right tool for your machine learning tasks! 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: https://t.co/eQLJWgrTrZ 𝗩𝗶𝗱𝗲𝗼: https://t.co/b8Nuu9ijkc #ML #DataScience #AI

Just discovered 𝗧𝗥𝗔𝗡𝗦𝗙𝗢𝗥𝗠𝗘𝗥 𝗘𝗫𝗣𝗟𝗔𝗜𝗡𝗘𝗥, an interactive tool for learning about text-generative models like 𝗚𝗣𝗧-𝟮. If you're curious about how AI language models work, this looks like a great resource to check out: https://t.co/SXCB7vqYEz #AI #ML #DATA

https://t.co/5iPoOjAJ3n Get Started on @huggingface 🤗 via @DeepLearningAI's "Open Source Models with Hugging Face" course. Dedicate one hour to exploring a wealth of novel concepts. #AI #MachineLearning