1.8M
MADo you think we can build a solid model at the end of this year? #formula1 #machinelearning #programming
@mar_antaya
#Module Masters
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1.3K
KA#fullstack #java #python #reactjs
@kannababubanna
1.0M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
7.1K
PEI have a particular favourite property of the inner product (but they’re all pretty useful) 📚
References and resources:
- Deisenroth at al, “Mathematics for Machine Learning”, 2020
- The course I’m following: “Mathematics for Machine Learning” by MathAcademy
@petal.byte
236.7K
NOYou don’t need a £50,000 motorsport degree. These 3 free courses give you the exact skills F1 teams want - all with certificates.
THE 3 FREE COURSES F1 TEAMS VALUE:
⚠️ UPDATE: Course #1 Alternative - The Helmut Schmidt course is currently unavailable.
Use this instead: Motorsport Engineer FREE Intro Course
Link: motorsportengineer.net/introductory-course
(FREE + Certificate, taught by ex-Mercedes F1 Engineer)
💻 COURSE #2: MATLAB Onramp (MathWorks)
→ 100% FREE from MathWorks
→ 2-hour interactive tutorial
→ ALL F1 teams use MATLAB for simulations
→ Red Bull, Ferrari require this skill
→ Get certificate immediately after completion
Link: matlabacademy.mathworks.com
🔧 COURSE #3: Fusion 360 CAD (YouTube + Autodesk)
→ Software FREE for students/hobbyists
→ Learn via YouTube (10-15 hours)
→ Gateway to CATIA/NX used by F1
→ Shows CAD competency teams want
Download: autodesk.com/fusion-360
YOUR ACTION PLAN:
1. Pick ONE course
2. Complete in 2-4 weeks
3. Get your certificate
4. Add to CV with project
HOW TO ADD TO YOUR CV:
* “Vehicle Dynamics - Helmut Schmidt University (2025)”
* ”MATLAB Certified - MathWorks Onramp”
* “CAD: Fusion 360, learning CATIA V6”
WHY THIS WORKS:
✅ Shows initiative (self-taught = valued)
✅ Proves you’re serious about F1
✅ No cost barrier = no excuses
✅ Real skills teams actually use
Start TODAY. All course links above!
Comment “TEMPLATES” for F1 CV templates showing exactly how to list these certifications.
#F1Careers #FreeOnlineCourses #F1Jobs #MATLAB #VehicleDynamics #CAD #Formula1 #EngineeringCourses #F1Education
@novoresumecreative
176.8K
PR1. Sentiment Analysis API
Description: Train a text sentiment classifier and serve it via an API for real-time predictions.
Tech Stack: FastAPI, Flask, Scikit-learn, Hugging Face, Docker, Python
2. Data Versioning and Model Reproducibility
Description: Use DVC to version datasets, models, and pipelines for reproducible ML experiments.
Tech Stack: DVC, Git, Scikit-learn, Python
3. Model Packaging with Docker
Description: Containerize a trained ML model to ensure consistent deployment across environments.
Tech Stack: Docker, Python, FastAPI, Flask
4. CI/CD for ML Models (GitHub Actions)
Description: Automate model training, testing, and deployment using GitHub Actions.
Tech Stack: GitHub Actions, Docker, DVC, pytest, Python
5. Model Registry & Deployment Pipeline
Description: Manage multiple model versions and automate promotion from staging to production.
Tech Stack: MLflow Model Registry, FastAPI, Docker Compose, GitHub Actions
6. Model Monitoring with Evidently AI
Description: Track model performance and detect data drift using monitoring dashboards.
Tech Stack: Evidently AI, Prometheus, Grafana, Python
7. Real-Time Inference with Kafka
Description: Serve model predictions on streaming data for real-time applications.
Tech Stack: Apache Kafka, FastAPI, Docker, Python
#datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency
@priyal.py
476.1K
ENas a controls engineer, getting comfortable with matlab/simulink was a must. From basic to advanced matlab to industry-level simulink modeling, these people saved me:
Simulink Tutorial
Coursovie
Learning Vibes
VDEngineering
for more controls + matlab resources comment “matlab”
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#engineering engineer #ingenieria #matlab #aerospace #engineeringstudent engineeringstudent stem aerospace
@engineersteatime
65.7K
SOespecially when I was studying probabilistic machine learning
#questions: which topics you find challenging when studying machine learning/deep learning
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#computerscience #datascience #girlwhocodes #codinglife #coding #softwareengineer #studygram #data #machinelearning #womenintech #womenwhocode #tech #learningdiary #ai #researchlife #deeplearning
@sopi.iscoding
43.5K
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
177.7K
JUQuando eu estava começando… 💡 Por aqui sempre rolam boas dicas de desenvolvimento de hardware!
Quer conhecer as ferramentas da Mouser? Digita MOUSER aqui nos comentários que eu te mando o link! ⚙️🔧
#hardware #eletrônica #desenvolvimentohardware #Mouser #dicasdeeletronica #engenharia #makers #JULIALABS #componenteseletrinicos
@juliallabs
273.1K
DALearning ML is WAY EASIER than you think. Theres are the YouTubers you need.
First, Andrej Karpathy. If you’re serious about understanding ML at a deep level this man is the one. He doesn’t just teach you what to do he teaches you why it works.
Then Sentdex. Super practical, gets straight to the point. If you want to just start building things and figure it out as you go, start here.
3Blue1Brown for the math side. I know math sounds scary but the way he visualizes everything makes it feel less like math and more like art. Neural networks finally made sense to me after watching him.
And StatQuest with Josh Starmer. Anytime I hit a concept I didn’t understand I went straight to him. He breaks things down so simply it almost feels too easy. #cs #machinelearning #python #datascience #ai
@darshcoded
34.9K
DAThe exact framework I’d use to learn ML from scratch in 2026. Save this if you’re actually trying to build - not just collect tutorials.
#machinelearning #artificalintelligence #datascience #learntocode #coding
@dandoesdata.ai
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