1.2M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
#Machine Learning Solutions
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455.7K
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
59.1K
EMThese are some of the best beginner-friendly resources I’ve found to actually understand machine learning.
Nothing overly complicated, just what you need to get the concepts and start building.
Comment ML and I’ll send you all the resources.
@emrcodes
1.5K
CODay 1 of our Machine Learning series 🚀
We started with the basics — what machine learning really is and how it works.
This series is for anyone who wants to understand ML without confusion.
Next up: AI vs Machine Learning.
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#MachineLearning
#ArtificialIntelligence
#CodeLoopa
#LearnAI
#TechExplained
@codeloopaa
53.3K
LILet’s see if I can cover the ML pipeline in 60 seconds ⏰😅
Machine learning isn’t just training a model. A production ML lifecycle typically looks like this:
1️⃣ Define the problem & objective
2️⃣ Collect and (if needed) label data
3️⃣ Split into train / validation / test sets
4️⃣ Data preprocessing & feature engineering
5️⃣ Train the model (forward pass + backpropagation in deep learning)
6️⃣ Evaluate on held-out data to measure generalization
7️⃣ Hyperparameter tuning (learning rate, architecture, etc.)
8️⃣ Final testing before release
9️⃣ Deploy (batch inference or real-time serving behind an API)
🔟 Monitor for data drift, concept drift, latency, cost, and reliability
1️⃣1️⃣ Retrain when performance degrades
Training updates weights.
Evaluation measures performance.
Deployment serves predictions.
Monitoring keeps the system healthy.
It’s not linear. It’s a loop.
And once you move beyond a single experiment, that loop becomes a systems problem.
At scale, the challenge isn’t just modeling … it’s building reliable, scalable infrastructure that supports the entire lifecycle.
Curious if this type of content is helpful! Lmk in the comments & as always Happy Building! 🤍
@lindavivah
511.7K
ITthis is the software side of robotics of course there’s a whole other piece to make the robots work #ai #machinelearning #datascientist #machinelearningengineer #robotics #techcareer #careergrowthtips
@itsallykrinsky
34.5K
GIMaths for Machine Learning
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#maths #ai #ml #tech #aiengineering
@girlwhodebugs
336.1K
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
473.9K
THWant to become a Machine Learning Engineer in 2025?
Build real projects that reflect how ML is done in the industry:
1 → End-to-End ML Pipeline
Predict something useful (like student dropout risk). Clean with Pandas, train with LightGBM, deploy with FastAPI + Docker + AWS.
2 → RAG Chatbot
Build a chatbot that answers from your course notes. Use LlamaIndex + FAISS + Llama 3.1. This is how GenAI apps work today.
3 → Fine-Tune LLMs
Take an open-source LLM and fine-tune it on your own dataset. Use QLoRA with PEFT. Example: medical Q&A bot.
4 → Model Monitoring
Build a fraud detection model and track drift post-deployment using Evidently AI + Weights & Biases. Shows you think beyond training.
5 → Multimodal AI App
Photo → nutrition info + recipe. Use CLIP or Florence-2 for vision-text, connect to LLaVA or Qwen-VL, deploy with Streamlit.
This stack hits every part of the ML lifecycle—from classic ML to GenAI to production monitoring.
[mlprojects, machinelearningengineer, genai, fine-tuning, ragchatbot, mlportfolio, endtoendpipeline, multimodalai, ai2025, llmengineer, mljobs, mlworkflow, productionai]
@the.datascience.gal
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
348.2K
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_
265.6K
MAWhat is Machine leaning part 1.
#machinelearning #ai
@machine_learning_samane
✨ #Machine Learning Solutions Discovery Guide
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