1.0M
CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
#Intuitive Machine Learning
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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If you don’t receive the link you either need to follow first then comment, or your instagram is outdated.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
801.6K
AIK-Nearest Neighbours (KNN) is a simple and intuitive supervised machine learning algorithm that makes predictions based on how similar things are to each other. They can be used for classification and regression.
Imagine you have a scatter plot with red and blue points, where red points represent one class and blue points represent another class. Now, let’s say you get a new data point you haven’t seen before, and want to know if it should be red or blue.
KNN looks at the “K” closest points (a hyperparameter that you set) to this new one — say, the 3 nearest points.
If 2 out of those 3 are red and 1 is blue, the new point is classified as red. It’s like asking your closest neighbors what they are and choosing the majority answer. Although simple, KNN performs surprisingly well based on the principle of proximity.
Want to get better at machine learning? Accelerate your ML learning with our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗).
C: visually explained
Join our AI community for more posts like this @aibutsimple 🤖
#machinelearning #statistics #mathematics #math #physics #computerscience #coding #science #education #datascience #knn
@aibutsimple
349.2K
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
208.7K
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
264.9K
MAWhat is Machine leaning part 1.
#machinelearning #ai
@machine_learning_samane
42.7K
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
67.6K
AIIf you want to learn AI in 2026, here's where to start:
First, build a strong foundation in machine learning before moving into deep learning.
Begin with supervised methods like linear and logistic regression to understand optimization and decision boundaries, then explore KNN, Naive Bayes, decision trees, random forests, gradient boosting, and SVMs to see different modeling assumptions and performance trade-offs.
Next, study unsupervised techniques such as k-means and hierarchical clustering, Gaussian mixture models, and dimensionality reduction methods like PCA, t-SNE, and UMAP to learn how structure can be discovered without labels.
With this in mind, transition to deep learning by learning neural networks and autoencoders, then more specialized architectures like CNNs for vision, RNNs for sequences, transformers and LLMs for language, and diffusion models for generative tasks.
This progression builds intuition step by step, from classical algorithms to modern AI systems.
If you want to commit to learning AI, Join 7000+ Others in our Visually Explained AI Newsletter. It's easy to understand, with math included—it's also completely free. The link is in our bio 🔗.
Join our AI community for more posts like this @aibutsimple 🤖
#machinelearning #deeplearning #statistics #computerscience #coding #mathematics #math #physics #science #education
@aibutsimple
40.6K
VOComment “ML” and I’ll send you the links👇
Machine learning doesn’t have to feel overwhelming. With the right guidance, complex topics like models, training, and prediction start making real sense 🧠
📌 Check out these beginner-friendly ML videos:
1️⃣ Learn Machine Learning Like a Genius – by InfiniteCodes
2️⃣ All ML Concepts Explained in 22 Minutes – by InfiniteCodes
3️⃣ ML for Everybody (Full Course) – by FreeCodeCamp
If terms like neural networks, supervised learning, or algorithms have ever confused you, these tutorials simplify everything into clear, practical explanations you can actually follow.
Instead of getting stuck in heavy math or abstract theory, you’ll build strong intuition around how machine learning works — from foundational concepts to real-world AI applications.
Whether you're interested in artificial intelligence, data science, Python projects, or future-proof tech skills, this is a powerful place to begin.
⭐ Save this so you don’t lose it, share it with someone learning AI, and start making machine learning finally click.
@volkan.js
40.0K
HE📌 “Confused about how to start your Machine Learning & AI journey? Here’s your complete roadmap from zero to job-ready! 💻✨”
No more scrolling through 100 videos — this 30 sec guide has everything you need to start & grow in ML!
Save 🔖 | Share 🤝 | Follow @helloworld_avani for more!
#machinelearning #artificialintelligence #pythonforbeginners #datasciencelearning #mlroadmap #techreels #codingjourney #learnwithme #careerinttech #reelsforstudents #studygramindia #trending #explorepage
@helloworld_avani
216.7K
IThow to learn ml with no experience - been getting asked a ton about this #techcareer #ai #machinelearning #careergrowthtips #careerdevelopment #datascience
@itsallykrinsky
34.3K
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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