Forwarded from Oops, My Brain Did That
God
make me invisible from the eyes of others and attract me to you and help me repel from this worldly planet and come to you , help me overcome my sins and be christ-like you
In your name I pray amen🫀☦️
make me invisible from the eyes of others and attract me to you and help me repel from this worldly planet and come to you , help me overcome my sins and be christ-like you
In your name I pray amen🫀☦️
❤4
What's your interest in Machine Learning, AI, and Data Science?
Anonymous Poll
19%
I'm actively learning and practicing
69%
I'm interested but haven't started yet
6%
I'm curious but not sure if I want to learn
6%
Not interested at all
Mike's ML Forge
What's your interest in Machine Learning, AI, and Data Science?
Hey everyone! I know many of you are interested in ML, AI, and Data Science but haven’t started yet. I’m also learning, so let’s grow together and build a great community! 🚀
🤝2✍1
Beginner Roadmap to ML, AI & Data Science
💡 A step-by-step guide to help you start your journey
1️⃣ Learn Python Basics 🐍
Python is the most widely used language in AI & Data Science. Start by mastering:
✅ Variables, Data Types, Loops, Functions
✅ Object-Oriented Programming (OOP) basics
✅ File handling (reading CSV, JSON)
✅ Virtual environments & package management (pip, conda)
Recommended Resources:
freeCodeCamp’s Python Course
Python Crash Course by Eric Matthes (Book)
W3Schools Python Tutorials
🔗 Practice: Write small noscripts & automation projects (e.g., a weather app, a to-do list manager).
2️⃣ Master Data Handling 📊
Data Science is all about working with data. Learn how to:
✅ Use pandas for data manipulation (DataFrames, Series, handling missing values)
✅ Use NumPy for numerical computations (arrays, linear algebra)
✅ Use Matplotlib & Seaborn for data visualization
Recommended Resources:
Kaggle’s Python & pandas courses
"Python for Data Analysis" by Wes McKinney (Book)
Real-world datasets on Kaggle & UCI Machine Learning Repository
🔗 Practice:
Analyze a dataset (e.g., FIFA player stats, Netflix movies)
Clean & visualize real-world messy data
3️⃣ Understand Math & Statistics 📏
Math is essential for ML, but you don’t need a PhD. Focus on:
✅ Linear Algebra (vectors, matrices, dot products)
✅ Probability & Statistics (mean, variance, distributions)
✅ Calculus Basics (derivatives & optimization)
Recommended Resources:
Khan Academy (Linear Algebra & Stats)
"The Elements of Statistical Learning" (Book)
3Blue1Brown (YouTube for math visualization)
🔗 Practice:
Use Python (NumPy, SciPy) to apply math concepts
Simulate probability distributions with Python
4️⃣ Learn Machine Learning Basics 🤖
Start with Supervised & Unsupervised Learning concepts:
✅ Supervised Learning: Linear Regression, Decision Trees, Random Forests, SVM
✅ Unsupervised Learning: Clustering (K-Means, DBSCAN), PCA
✅ Evaluation Metrics: Accuracy, Precision-Recall, RMSE
Recommended Resources:
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" (Aurélien Géron)
Scikit-Learn Documentation & Tutorials
Kaggle micro-courses
🔗 Practice:
Predict house prices using Linear Regression
Classify iris flowers using Decision Trees
5️⃣ Work on Real-World Projects 🚀
Apply what you’ve learned with real-world data.
✅ Kaggle datasets & competitions
✅ End-to-end projects: Data collection → Cleaning → Modeling → Deployment
Project Ideas:
🔹 Sentiment analysis of tweets (Text data)
🔹 Predict sales revenue (Regression)
🔹 Identify handwritten digits (Image classification)
Final Tips 🎯
🔹 Start small, build consistently
🔹 Work on real datasets (Kaggle, Google Dataset Search)
🔹 Engage with the community (Kaggle, Stack Overflow, LinkedIn)
💡 A step-by-step guide to help you start your journey
1️⃣ Learn Python Basics 🐍
Python is the most widely used language in AI & Data Science. Start by mastering:
✅ Variables, Data Types, Loops, Functions
✅ Object-Oriented Programming (OOP) basics
✅ File handling (reading CSV, JSON)
✅ Virtual environments & package management (pip, conda)
Recommended Resources:
freeCodeCamp’s Python Course
Python Crash Course by Eric Matthes (Book)
W3Schools Python Tutorials
🔗 Practice: Write small noscripts & automation projects (e.g., a weather app, a to-do list manager).
2️⃣ Master Data Handling 📊
Data Science is all about working with data. Learn how to:
✅ Use pandas for data manipulation (DataFrames, Series, handling missing values)
✅ Use NumPy for numerical computations (arrays, linear algebra)
✅ Use Matplotlib & Seaborn for data visualization
Recommended Resources:
Kaggle’s Python & pandas courses
"Python for Data Analysis" by Wes McKinney (Book)
Real-world datasets on Kaggle & UCI Machine Learning Repository
🔗 Practice:
Analyze a dataset (e.g., FIFA player stats, Netflix movies)
Clean & visualize real-world messy data
3️⃣ Understand Math & Statistics 📏
Math is essential for ML, but you don’t need a PhD. Focus on:
✅ Linear Algebra (vectors, matrices, dot products)
✅ Probability & Statistics (mean, variance, distributions)
✅ Calculus Basics (derivatives & optimization)
Recommended Resources:
Khan Academy (Linear Algebra & Stats)
"The Elements of Statistical Learning" (Book)
3Blue1Brown (YouTube for math visualization)
🔗 Practice:
Use Python (NumPy, SciPy) to apply math concepts
Simulate probability distributions with Python
4️⃣ Learn Machine Learning Basics 🤖
Start with Supervised & Unsupervised Learning concepts:
✅ Supervised Learning: Linear Regression, Decision Trees, Random Forests, SVM
✅ Unsupervised Learning: Clustering (K-Means, DBSCAN), PCA
✅ Evaluation Metrics: Accuracy, Precision-Recall, RMSE
Recommended Resources:
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" (Aurélien Géron)
Scikit-Learn Documentation & Tutorials
Kaggle micro-courses
🔗 Practice:
Predict house prices using Linear Regression
Classify iris flowers using Decision Trees
5️⃣ Work on Real-World Projects 🚀
Apply what you’ve learned with real-world data.
✅ Kaggle datasets & competitions
✅ End-to-end projects: Data collection → Cleaning → Modeling → Deployment
Project Ideas:
🔹 Sentiment analysis of tweets (Text data)
🔹 Predict sales revenue (Regression)
🔹 Identify handwritten digits (Image classification)
Final Tips 🎯
🔹 Start small, build consistently
🔹 Work on real datasets (Kaggle, Google Dataset Search)
🔹 Engage with the community (Kaggle, Stack Overflow, LinkedIn)
👍3
Hope you guys know the basics of python so you can start with data handling and https://news.1rj.ru/str/MikeDevThoughts/134 you can simply start from this
Telegram
Mike's DevThoughts
https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-2-data-science-and-ml-tools/numpy-exercises.ipynb
👍2
Forwarded from Tech Nerd (Tech Nerd)
KiNFiSH Farms
how in the world i would answer this ... this things are getting out of control
Imagine the captcha is too complicated you need ai to help you prove you're not an ai 😁
@selfmadecoder
@selfmadecoder
🤣5
I want to take a moment to thank someone who inspired my journey into machine learning and AI. He introduced me to the field, constantly encouraged me, and pushed me to think bigger. Shoutout to this guy ,He’s the most humble and hardworking person I know, and he continues to be a huge inspiration. I truly appreciate his guidance—thank you for everything
@kalkin_21
@kalkin_21
👏7👍1
## 🚢 Titanic Survival Prediction – Final Model!
I trained and fine-tuned a RandomForestClassifier to predict Titanic survivors. After tweaking the model, i tried to implemet the things that i was talking here tbh it feels so nice actually this is the first dataset i handled on my own , and i'm sure i'll do more. here are the final results:
✅ Accuracy: 83.24%
✅ Precision: 0.84 (Fewer false positives)
✅ Recall: 0.73 (Decent survivor detection)
✅ F1 Score: 0.78
✅ AUC Score: 0.814
🔹 False positives dropped to 12 (from 15)
🔹 False negatives stayed at 19
📌 Next? Maybe test other models like XGBoost or ensemble methods. Let me know if you want a breakdown! 🚀
#MachineLearning #Titanic #DataScience
I trained and fine-tuned a RandomForestClassifier to predict Titanic survivors. After tweaking the model, i tried to implemet the things that i was talking here tbh it feels so nice actually this is the first dataset i handled on my own , and i'm sure i'll do more. here are the final results:
✅ Accuracy: 83.24%
✅ Precision: 0.84 (Fewer false positives)
✅ Recall: 0.73 (Decent survivor detection)
✅ F1 Score: 0.78
✅ AUC Score: 0.814
🔹 False positives dropped to 12 (from 15)
🔹 False negatives stayed at 19
📌 Next? Maybe test other models like XGBoost or ensemble methods. Let me know if you want a breakdown! 🚀
#MachineLearning #Titanic #DataScience
👏6
and Mid-exam in a week so time to study before I get cooked again😅
😢4😁1😭1
Forwarded from Dave Dumps
living means choosing. But do we always choose freely and consciously ? is our choice not frequently forced on us by circumstances, by our faint-heartedness, our habits or even our guilts ?
~ Dr. Paul Tournier ( Guilt & Grace - Matters of Time)
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