Mike's ML Forge
Back in class, we had a whole course on Probability and Random Processes and bro Ngl, I was fighting for my life trying to stay awake 😂 I thought, “Nah, this can’t be useful. Just formulas, symbols, and pain." Now I'm deep into ML thing and guess what’s showing…
And the plot twist? That same lecturer from back then… She's now my internship advisor 💀😂
😁5
Chimaev is chaos and born for war.
DDP is a storm of will power and never afraid to bleed for victory.
DDP is a storm of will power and never afraid to bleed for victory.
⚡2🔥2
Mike's ML Forge
Photo
This isn't just a fight, it's a collision of two warriors who refuse to fold
🔥3
Forwarded from Tech Nerd (Tech Nerd)
Forwarded from Data 2 Pattern
Dimension reduction
Dimension reduction is the process of reducing the number of variables (dimensions) in a dataset while keeping its most important information. It is a powerful technique for simplifying complex data, which offers benefits such as improved computational efficiency, better model performance, and easier data visualization.
Why reduce dimensions?
💡 Curse of dimensionality: When a dataset has too many dimensions relative to the number of data points, it can become sparse, making it difficult for machine learning models to find meaningful patterns.
🔑 Eliminate redundancy and noise: Datasets often contain variables that are highly correlated or irrelevant, adding noise and complexity that can confuse models.
📊 Improve visualization: The human brain is limited to visualizing data in two or three dimensions. Dimensionality reduction allows you to represent high-dimensional data in a way that is easier for people to understand.
🎯 Increase efficiency: Fewer dimensions mean less computational time and resources are needed to process the data, which is especially important for large datasets.
⚡️ Prevent overfitting: By simplifying the dataset and removing noise, a model is less likely to learn the random fluctuations in the data and more likely to generalize well to new data.
Common techniques
There are two primary approaches to dimensionality reduction:
1. Feature extraction
This method transforms the original variables into a new, smaller set of variables (components) that are combinations of the original ones.
👉 Principal Component Analysis (PCA): A popular unsupervised method that creates new, uncorrelated components, ordered by the amount of variance they explain.
👉 Factor Analysis (EFA): An unsupervised method used to identify underlying, unobserved (latent) factors that cause the correlations among the observed variables.
👉 t-SNE (t-Distributed Stochastic Neighbor Embedding): A nonlinear method especially useful for visualizing high-dimensional data by placing similar data points closer together in a lower-dimensional space.
2. Feature selection
This method selects a subset of the most relevant original variables, discarding the rest. It does not transform the variables.
Filter methods: Use statistical measures to score features and keep the best ones, for example, by filtering out low-variance or highly correlated variables.
Wrapper methods: Evaluate different subsets of features by training and testing a model with each subset to see which performs best.
https://medium.com/@souravbanerjee423/demystify-the-power-of-dimensionality-reduction-in-machine-learning-26b70b882571
@data_to_pattern @data_to_pattern @data_to_pattern
Dimension reduction is the process of reducing the number of variables (dimensions) in a dataset while keeping its most important information. It is a powerful technique for simplifying complex data, which offers benefits such as improved computational efficiency, better model performance, and easier data visualization.
Why reduce dimensions?
💡 Curse of dimensionality: When a dataset has too many dimensions relative to the number of data points, it can become sparse, making it difficult for machine learning models to find meaningful patterns.
🔑 Eliminate redundancy and noise: Datasets often contain variables that are highly correlated or irrelevant, adding noise and complexity that can confuse models.
📊 Improve visualization: The human brain is limited to visualizing data in two or three dimensions. Dimensionality reduction allows you to represent high-dimensional data in a way that is easier for people to understand.
🎯 Increase efficiency: Fewer dimensions mean less computational time and resources are needed to process the data, which is especially important for large datasets.
⚡️ Prevent overfitting: By simplifying the dataset and removing noise, a model is less likely to learn the random fluctuations in the data and more likely to generalize well to new data.
Common techniques
There are two primary approaches to dimensionality reduction:
1. Feature extraction
This method transforms the original variables into a new, smaller set of variables (components) that are combinations of the original ones.
👉 Principal Component Analysis (PCA): A popular unsupervised method that creates new, uncorrelated components, ordered by the amount of variance they explain.
👉 Factor Analysis (EFA): An unsupervised method used to identify underlying, unobserved (latent) factors that cause the correlations among the observed variables.
👉 t-SNE (t-Distributed Stochastic Neighbor Embedding): A nonlinear method especially useful for visualizing high-dimensional data by placing similar data points closer together in a lower-dimensional space.
2. Feature selection
This method selects a subset of the most relevant original variables, discarding the rest. It does not transform the variables.
Filter methods: Use statistical measures to score features and keep the best ones, for example, by filtering out low-variance or highly correlated variables.
Wrapper methods: Evaluate different subsets of features by training and testing a model with each subset to see which performs best.
https://medium.com/@souravbanerjee423/demystify-the-power-of-dimensionality-reduction-in-machine-learning-26b70b882571
@data_to_pattern @data_to_pattern @data_to_pattern
Medium
Demystify the Power of Dimensionality Reduction in Machine Learning
In the world of machine learning, navigating the vast landscape of high-dimensional data can be as thrilling as it is challenging. Imagine…
You want 1 month free perplexity AI ?
If you are student and have any card, like bybit debit card then you can earn 1 month free access
https://www.perplexity.ai/referrals/DQD91MCF
If you are student and have any card, like bybit debit card then you can earn 1 month free access
https://www.perplexity.ai/referrals/DQD91MCF
Perplexity AI
Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question.
❤1
Forwarded from Dagmawi Babi
What a fool I am to pray in a hurry. How could I forget who made the time.
Forwarded from BeNN
📢 Calling All Learners and Teachers—EdueraSkills (EdueraSkills.com) is Now Open!
Tired of wasting hours on random YouTube tutorials, fake “gurus,” and confusing roadmaps that never really get you anywhere? We’ve all been there. That’s exactly why we built EdueraSkills.com —to help People learn real skills like Python, Machine Learning, Python Libraries and others.
New to Computer and Technology world? We have got you visit our introduction to computer and Technology course.
Why EdueraSkills?
✅ Learn Faster – Our courses are beginner-friendly, straight to the point, and made for learners. No extra fluff—just what you need to start building skills.
✅ Affordable for Everyone.
✅ Learn From Local Instructors who will understand the circumstances to teach.
✅ Become a Teacher Too – If you’ve got a skill, you can join EdueraSkills as an instructor.
👉 Visit EdueraSkills.com
For more Contact Us or join our Channel
Channel: @Eduera_Skills
Personal: @EdueraSkills
#EdueraSkills #SkillUpEthiopia #LearnAndTeach
Tired of wasting hours on random YouTube tutorials, fake “gurus,” and confusing roadmaps that never really get you anywhere? We’ve all been there. That’s exactly why we built EdueraSkills.com —to help People learn real skills like Python, Machine Learning, Python Libraries and others.
New to Computer and Technology world? We have got you visit our introduction to computer and Technology course.
Why EdueraSkills?
✅ Learn Faster – Our courses are beginner-friendly, straight to the point, and made for learners. No extra fluff—just what you need to start building skills.
✅ Affordable for Everyone.
✅ Learn From Local Instructors who will understand the circumstances to teach.
✅ Become a Teacher Too – If you’ve got a skill, you can join EdueraSkills as an instructor.
👉 Visit EdueraSkills.com
For more Contact Us or join our Channel
Channel: @Eduera_Skills
Personal: @EdueraSkills
#EdueraSkills #SkillUpEthiopia #LearnAndTeach
❤3
Forwarded from Henok | Neural Nets
Research Internship for undergrad and MS students at Max Planck Institute for Intelligent Systems
https://rig-internships.de/program
https://rig-internships.de/program
rig-internships.de
Program
The RIGI program invites students from all over the world to join a world-class research environment at the Max Planck Institute for Intelligent Systems in Stuttgart, a leading center for robotics and AI. We offer paid, up to 90-day internships to bachelor’s…
❤1
Forwarded from BeNN
"Mathematics is the language with which God has written the universe." Galileo Galilei
Forwarded from The Data Guy
𝕋𝕙𝕚𝕤 𝕨𝕖𝕖𝕜 𝕛𝕦𝕤𝕥 𝕜𝕖𝕖𝕡𝕤 𝕘𝕖𝕥𝕥𝕚𝕟𝕘 𝕓𝕖𝕥𝕥𝕖𝕣🔥
365 Data Science has unlocked its entire AI & Data learning platform 100% free for 15 days (Nov 6–21).
I took Data Strategy, Data Visualization, and Web Scraping courses last year and they turned out to be some of the most practical ones I’ve ever done.
I was able to apply those skills directly at work, strengthen my analyses, and genuinely level up my career. If you’ve been planning to dive deeper into data or AI, this is your week.
Now everyone can access:
• 115+ AI and Data Science courses
• CPE-accredited certificates
• Real-world projects and practical case studies
• Trusted by 2M+ learners globally
No credit card. No catch. Just learning.
👉 https://365datascience.com/free-weeks-2025/
365 Data Science has unlocked its entire AI & Data learning platform 100% free for 15 days (Nov 6–21).
I took Data Strategy, Data Visualization, and Web Scraping courses last year and they turned out to be some of the most practical ones I’ve ever done.
I was able to apply those skills directly at work, strengthen my analyses, and genuinely level up my career. If you’ve been planning to dive deeper into data or AI, this is your week.
Now everyone can access:
• 115+ AI and Data Science courses
• CPE-accredited certificates
• Real-world projects and practical case studies
• Trusted by 2M+ learners globally
No credit card. No catch. Just learning.
👉 https://365datascience.com/free-weeks-2025/
365 Data Science
Start Learning Data Science for Free – 365 Data Science
Kickstart your data science journey with free lessons, hands-on exercises, and real-world data projects. Sign up now--no credit card required, no time limit.