#Hands On Machine Learning

Aurelien Gerons Hands-on ML book is by far the best when it comes to learning Machine Learning hands-on way. I have already shared 🆓 PDFs of this books previous versions with our exclusive instagram subscribers. Do note that buying this latest edition is an investment that's worth every penny. ⚠️ Before you read the book make sure you have your basics covered that includes, Python & Basic Theory of Machine Learning. Did you like the post? if yes then kindly 🏆Follow @datasciencebrain #dsbrain for more amazing Data Science resources and News. Also 📌Tag your friends who would like to know about this • • • • #data #datascience #dataanalytics #dataanalysis #dataanalyst #datascientist #datacleaning #practicedatascience #python #sql #dataengineering #pacticesql #pandas #datavisualization #machinelearning #deeplearning #datasciencejobs #datascienceinternship #datascienceroadmap #learndatascience #learndataanalytics #datascienceinterview #chatgpt

Machine learning relies heavily on mathematical foundations. #tech #ml #explore #fyp #ai

Steve brunton is sooo GOATEDDD !!! #machinelearning #datascience #stem #artificialintelligence

Putting machine learning mathematics prerequisites into context, to better appreciate their significance. Resources used: - Deisenroth at al, Mathematics for Machine Learning, 2020 - Goodfellow et al, Deep Learning, 2016 - MathAcademy

Comment “ML” and I’ll send you the links 👇 🚀 3 Machine Learning Videos Every Beginner Should Watch If you’re getting into machine learning, AI, data science, or Python for ML, these resources can help you avoid confusion and learn the right way from the start. No unnecessary fluff. No heavy math. Just clear, practical explanations of core machine learning concepts and how things actually work. 1️⃣ Infinite Codes – Learn Machine Learning Like a GENIUS A structured roadmap that shows you how to approach learning machine learning and AI efficiently without feeling lost. 2️⃣ Infinite Codes – All Machine Learning Concepts in 22 Minutes A fast-paced overview of key topics like supervised vs. unsupervised learning, neural networks, and essential ML ideas. 3️⃣ freeCodeCamp – Machine Learning for Everybody A beginner-friendly full course covering Python, machine learning models, and real-world applications in a simple way. If machine learning feels confusing or too theoretical, this is a great starting point to build clarity. 💾 Save this for later 👥 Share it with someone learning AI, ML, or data science

Arduino Virtual Button - artificial intelligence activities for kids - Arduino ile Sanal Buton Çocuklar İçin Yapay Zeka Uygulamaları #makineöğrenmesi #arduinoproject #teachablemachine #kidseducation #rasberrypi #python3 #mblock #robotikkodlama #robotikkodlamaeğitimi

I was so lazy and only collected 16 episodes to train the model 😂 Next, I need to set up Google Colab to train VLAs😬, and I’m not really looking forward to recording more data… Check out this paper to learn more about ACT! ⬇️ Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware 🔗 https://arxiv.org/abs/2304.13705 #machinelearning #robotics #womenintech #tech #electronics

These 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.

computer vision pt 2 | how does Mediapipe’s hand-tracking model work? How can a computer tell when an object is there? We gotta understand the basics to build cool stuff. So here’s a quick summary. Excited to create something fun!!! . #machinelearning #ml #coding #programming #womenintech #coder #python

Here is my full tutorial on how you can get started with machine learning from 0 and land a job in big tech It’s definitely not the easiest thing to do,but if you follow the steps in the video carefully you can get closer to your goals Make sure to save this video for later,so you can continue to revisit these steps so you can become a Machine Learning Engineer #coding #computerscience #ml #machinelearning

Follow for Ai/Robotics content Dm for link ⬇️⬇️⬇️⬇️ Beginner Level Python & ML Foundations https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgMaz0Mu-SjCPZNUjz6-6tN Mathematics for Machine Learning https://www.youtube.com/playlist?list=PLPTV0NXA_ZSiR4_XoR1wy-3bv6J0oZ9Zs Machine Learning Fundamentals https://www.youtube.com/playlist?list=PLMrJAkhIeNNR3sNYvfgiKgcStwuPSts9V Deep Learning Basics https://www.youtube.com/playlist?list=PLMrJAkhIeNNT14qn1c5qdL29A1UaHamjx Introduction to Robotics (Conceptual) https://www.youtube.com/watch?v=FGnAeUXRZ4E Robot Kinematics & Motion (Beginner-friendly) https://www.youtube.com/@ArticulatedRobotics ROS & Robotics Fundamentals https://www.youtube.com/playlist?list=PLLSegLrePWgJudpPUof4-nVFHGkB62Izy Intermediate Level Machine Learning (Reinforcement & Applied ML) https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgMaz0Mu-SjCPZNUjz6-6tN Reading & Understanding AI Research Papers https://www.youtube.com/@aipapersacademy/videos Applied Deep Learning & Vision https://www.youtube.com/playlist?list=PLMrJAkhIeNNQe1JXNvaFvURxGY4gE9k74 Practical Robotics Engineering https://www.youtube.com/@kevinwoodrobotics Neural Networks from First Principles https://www.youtube.com/@AndrejKarpathy Advanced Level Advanced Robotics & Control Systems https://www.youtube.com/playlist?list=PLMrJAkhIeNNR20Mz-VpzgfQs5zrYi085m Deep Learning & AI Systems (Stanford-level) https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM Reinforcement Learning & Advanced ML https://www.youtube.com/playlist?list=PLZnJoM76RM6IAJfMXd1PgGNXn3dxhkVgI #learnings #ML #education #study #engineering

Let’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! 🤍