1.1M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
#Machine Learning Training
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405.7K
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
102.6K
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
3.0M
ITcomment ‘AI’ and I’ll send you the link in your DMs
this is such a great resource to guide you on your AI/ML journey!
#techcareer #ai #machinelearning #careergrowthtips #datascience #coding
@itsallykrinsky
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
335.6K
PI2025 machine learning roadmap - it’s time to start prepping for AI’s takeover 💡🤖 resources mentioned:
VIDEO:
Full Applied AI Lectures by Cassie Kozyrkov
Neural Networks: Zero to Hero by Andrej Karpathy
Machine Learning Specialization by Andrew Ng
BOOKS:
An Introduction to Statistical Learning
Mathematics for Machine Learninf
Artificial Intelligence: A Modern Approach
FOR PRACTICE:
Machine Learning with PyTorch and Scikit-Learn
AIML.com
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#machinelearning #ai #resources #tech #programming #womenintech #coder #programacao #latinasintech #swe
@pikacodes
3.4K
MILearning math for machine learning is essential because:
1. **Foundation of Algorithms**: Many machine learning algorithms are grounded in mathematical concepts such as linear algebra, calculus, and probability. Understanding these helps you grasp how algorithms work and their underlying assumptions.
2. **Model Evaluation**: Math provides the tools for analyzing and interpreting model performance, helping you choose the right metrics and techniques for evaluation.
3. **Optimization**: Many machine learning tasks involve optimization problems. Knowledge of calculus and linear algebra enables you to effectively minimize loss functions and improve model accuracy.
4. **Feature Engineering**: A solid mathematical background aids in creating effective features, allowing you to transform raw data into meaningful inputs for your models.
5. **Problem-Solving Skills**: Math enhances your analytical and critical thinking abilities, which are crucial for tackling complex problems in machine learning.
Overall, a strong math foundation equips you to understand, develop, and refine machine learning models effectively
Book: mathematics for machine learning
#machinelearning #machinelearningalgorithms #machinelearningengineer #machinelearningtraining #machinelearningmaster #machinelearningcourse #machinelearningjobs #machinelearningwithpython
@michaellin250
347.6K
PRhere’s a full roadmap for anyone who wants to get into machine learning but doesn’t know where to start. covers the math, tools, courses, and projects that actually matter— no fluff, just what’ll get you from zero to real-world skills.
if you want the actual roadmap doc itself written up, either comment below or shoot me a DM, i’ll send it ASAP.
hope that helps. 🤝
#study #viral #education #math #advice #university #studyhelp #cs #exam #leetcode #research #machinelearning #deeplearning
@pranavpatnaik_
14.3K
SUHere are some machine learning courses that are actually worth it and will make you learn very quickly. These courses don’t have the same fluff and excessive theoretical jargon that doesn’t help you. So this list actually compiles the best ones that I have found.
And if you want to check out the courses for yourself,make sure to follow @sujar.tech and comment “Courses” and I’ll send you the links to all of these
#coding #computerscience #cs #machinelearning
@sujar.tech
211.0K
CHCourses take up too much time. I’d probably just start learning ML by doing it.
Comment “link” and I’ll send the resource.
#tech #explore #fyp #ml #ai
@chrisoh.zip
23.6K
CO🚀 Machine Learning Roadmap (2025 Edition)
Unlock your journey into AI, Machine Learning & Deep Learning with this step-by-step guide designed for beginners to advanced learners.
📌 What You’ll Learn in This Video:
⚙️ Phase 1 – Core Foundation
📐 Math Basics | 🐍 Python Programming
🧹 Phase 2 – Data Preparation
🧽 Data Cleaning | 🎛 Feature Engineering | 📊 Visualization
🤖 Phase 3 – Machine Learning Concepts
🎯 Supervised & Unsupervised Learning | 🔍 Key Algorithms
🧪 Phase 4 – Model Optimization
📈 Cross-Validation | 🛠 Hyperparameter Tuning | 📍 Metrics
🧠 Phase 5 – Advanced ML
🌀 Neural Networks | 👁 Computer Vision | 💬 NLP
🚀 Phase 6 – Deployment & Real-World Use
🗃 Model Serialization | 🌐 APIs | ☁ Cloud | 🧩 MLOps
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💡 Whether you're a beginner, student, or career switcher, this roadmap will help you become job-ready in AI and ML.
📚 Save this video and start learning step by step.
👇 Comment "ROADMAP" if you want a downloadable PDF version.
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🔍 Keywords:
Machine Learning Roadmap 2025, AI learning path, Deep Learning, Data Science Roadmap, Python for ML, Best way to learn AI, MLOps, Cloud AI skills.
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🔥 Hashtags:
#MachineLearning #AI #ArtificialIntelligence #DeepLearning #DataScience #Python #MLRoadmap #LearnML #TechCareers #Programming #NLP #ComputerVision #MLOps #DataEngineer #FutureSkills #Roadmap2025 #AIEducation #AIRevolution #CodingJourney
@codewithprashantt
33.7K
_P🐍Learning Python with AI
🔸️In this class, we're training students to learn Python faster with AI collaboration!
🔸️Here, Aidan uses ChatGPT to recreate a version of the classic arcade game Asteroids.
🔸️This is Aidan's 12th day of Python programming.
🔸️"But WAIT, if students don't learn procedural and syntax fundamentals, they'll never be able to troubleshoot their own code!"
🔸️Yes. I agree with you. I'm teaching them the basics and not overlooking the critical fundamentals. You're right.
🔸️Also, it's important to show them the capabilities offered through collaborating with a powerful tool and how to use it as a learning aid, ather than a shortcut. This is critical!
@cvcc.va
@a3_automate
🔸️Do you think programming is still a valuable skill given modern technology?
@_papamurph
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