1.1M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
#Machine Learning Frameworks Comparison
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715.7K
SEComment “Stat” and I’ll send you the link.
A visual, beginner-friendly site that explains machine learning statistics with clear examples and live probability experiments.
@sebintel
42.6K
COProgramming Languages, Frameworks & Tools Explained
A quick overview of the core technologies used across Web Development, Software Engineering, and Machine Learning.
🔹 Web Development 🌐
Languages: HTML, CSS, JavaScript, TypeScript, Python, Ruby, PHP
Frameworks & Tools: React, Node.js, Express, Django, Bootstrap
🔹 General Software Development 💻
Languages: Python, C, C++, C, Java, Ruby, Swift, Kotlin, Go, TypeScript
Frameworks & Tools: .NET, Spring, Flask, Electron
🔹 Machine Learning & AI 🤖
Languages: Python, R, Julia, Scala, Java, MATLAB, C++, C
Frameworks & Tools: TensorFlow, PyTorch, Keras, scikit-learn
✨ Whether you're a beginner exploring tech or a developer sharpening your stack, this roadmap highlights the tools powering modern software.
📌 Save & share if this helps your learning journey!
Programming Languages, Software Development, Web Development, Machine Learning, AI Tools, Developer Roadmap, Tech Stack, Coding Skills, Frameworks, Full Stack Development
#programming
#softwaredevelopment
#webdevelopment
#machinelearning
#coding
@codewithprashantt
381.0K
CHHere’s your full roadmap on how to get into machine learning. Comment “Roadmap” to get the pdf.
Save and follow for more.
#ai #machinelearning #coding #programming #cs
@chrispathway
91.2K
VOComment “ML” and I’ll send you the links!
You don’t need expensive AI or machine learning bootcamps to understand how ML models and large language models actually work. Some of the best machine learning tutorials, deep learning resources, and AI courses online are completely free — and often better than paid programs.
📌 3 High-Impact Resources to Actually Learn Machine Learning & AI:
1️⃣ All Machine Learning Concepts Explained in 22 Minutes – Infinite Codes
A fast-paced breakdown of core machine learning concepts including supervised vs unsupervised learning, regression, classification, neural networks, and deep learning. Perfect for quickly understanding how ML models work without getting lost in theory.
2️⃣ Stanford CS229: Machine Learning – Building Large Language Models (LLMs)
A more advanced lecture covering how modern AI systems and LLMs are built. It explains key concepts like training data, model architecture, optimization, and how large-scale machine learning systems power tools like ChatGPT.
3️⃣ Machine Learning for Beginners (GitHub Repository)
A structured, hands-on resource that walks through machine learning step by step. Includes real projects, explanations, and practical implementations so you can actually apply ML concepts and build your own models.
These resources cover essential machine learning concepts like supervised learning, unsupervised learning, neural networks, deep learning, large language models (LLMs), training data, model optimization, and real-world AI applications.
Whether you’re a developer getting into AI, preparing for machine learning interviews, or building intelligent systems, understanding machine learning is a must-have skill.
Save this, share it, and start learning how AI actually works. 🤖
@volkan.js
1.3M
SAI’ve been asked many times where to start learning ML, so after talking to so many experts in this field, this is a good place to start.
Comment down below “TRAIN” and I’ll send you a more in-depth checklist along with the best GitHub links to help you start learning each concept.
If you don’t receive the link you either need to follow first then comment, or your instagram is outdated. Either way, no worries. send me a dm and I’ll get it to you ASAP.
#cs #ai #dev #university #softwareengineer #viral #advice #machinelearning
@sambhav_athreya
527.8K
EQThe Secret to Perfect Data Models #MachineLearning #PolynomialRegression #Statistics #Math #Manim Ever wondered why your machine learning model isn't performing as expected? In this video, we break down polynomial curve fitting, a fundamental concept in data science and statistics. We explore the visual differences between Degree 1 (Underfitting), Degree 3 (Good Fit), and Degree 11 (Overfitting). Learn how increasing the degree of a polynomial affects how it captures data trends and why the optimal model is crucial for accurate predictions.
@equationsinmotion
131.9K
KRVisualizing the architecture of intelligence. 🕸️✨
Every neural network is built on the same fundamental concept: Layers.
🟡 Input Layer: Receives the raw data (pixels, text, numbers).
🟢 Hidden Layers: Where the magic happens—processing features and finding patterns.
🟠 Output Layer: Delivers the final prediction or decision.
From the simple Perceptron to the complex loops of an RNN, these structures are the blueprints for how machines learn. 📐
#NeuralNetworks #MachineLearning #DeepLearning #DataScience #AI #Education #Visualized
@kreggscode
647.8K
WOSteve brunton is sooo GOATEDDD !!!
#machinelearning #datascience #stem #artificialintelligence
@workiniterations
794.4K
COWant to excel in machine learning? Start with a solid understanding of statistics! 📈
This website features interactive lessons and practical exercises to help you learn effectively.
Dive in and elevate your skills today!
#DataDriven #MLSkills
@codewitharjit
110.8K
TEIf you’re a visual learner, these tools can make ML way easier to understand. Save this for later. 👋
1. Ostralyan: ostralyan. com
2. ML Visualizer: mlvisualizer.org
3. Interactive ML: interactive-ml.com
4. ML-Visualiser: ml-visualiser.vercel.app
5. TensorFlow Playground: playground.tensorflow.org
👉 Follow @techviz_thedatascienceguy for more AI content!
#interactivecontent #learnai #aicontent #datascience #datascience visual machine learning
@techviz_thedatascienceguy
141.3K
ARIf you’re just getting into machine learning, this is the best place to start.
R2D3 is a free, interactive website that teaches you how machine learning works through animated visualizations. No equations upfront. No wall of theory. You scroll, and the model builds itself in front of you.
What the video couldn’t cover:
The site walks you through a decision tree, one of the most foundational algorithms in ML, using a real dataset of homes in San Francisco and New York. You watch the model draw boundaries on the data, test them, and adjust when they are wrong.
The concept it ends on is overfitting, what happens when a model learns the training data too well and fails on anything new. Seeing it visually is the moment a lot of things in ML suddenly click.
Built by Stephanie Yee and Tony Chu. Completely free, no sign-up required.
Link in the pinned comment.
#ai #artificialintelligence #technews #algorithm #fyp
@arnitly
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