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CHMachine learning relies heavily on mathematical foundations.
#tech #ml #explore #fyp #ai
@chrisoh.zip
#Unsupervised Machine Learning
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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
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If you don’t receive the link you either need to follow first then comment, or your instagram is outdated.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
702
CODay 3 of our Machine Learning series 🚀
Today we broke down the three main types of Machine Learning:
Supervised, Unsupervised, and Reinforcement Learning.
Understanding these foundations makes everything ahead much easier.
From tomorrow, we start diving deep — beginning with Supervised Learning.
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#MachineLearning
#ArtificialIntelligence
#SupervisedLearning
#ReinforcementLearning
#CodeLoopa
@codeloopaa
67.8K
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
562.5K
THNeural networks and machine learning.
@theartificialintelligence
49.6K
DATypes of Machine Learning (ML) Systems:
1. Based on Learning Style:
a. Supervised Learning
Learns from labeled data.
Goal: Predict output for new inputs.
Common tasks: Classification, Regression.
Examples: Linear Regression, Decision Trees, SVM.
b. Unsupervised Learning
Learns from unlabeled data.
Goal: Discover hidden patterns.
Common tasks: Clustering, Dimensionality Reduction.
Examples: K-Means, PCA, DBSCAN.
c. Semi-Supervised Learning
Uses a small amount of labeled data + large unlabeled data.
Goal: Improve learning accuracy.
Example: Self-training algorithms.
d. Reinforcement Learning
Learns by interacting with the environment.
Goal: Maximize reward over time.
Examples: Q-Learning, Deep Q-Networks.
2. Based on Output Type:
a. Classification
Output is categorical.
Example: Spam detection.
b. Regression
Output is continuous.
Example: House price prediction.
3. Based on Training Approach:
a. Batch Learning (Offline)
Model is trained on the entire dataset at once.
Needs retraining for new data.
b. Online Learning
Learns incrementally from new data.
Suitable for real-time applications.
4. Based on Model Generalization:
a. Instance-Based Learning
Memorizes examples, makes decisions based on similarity.
Example: K-Nearest Neighbors.
b. Model-Based Learning
Builds a model from training data, generalizes patterns.
Example: Linear Regression, Neural Networks.
#datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist #neuralnetworks #100daysofcode #genai #llms #datasciencebootcamp
@datasciencebrain
214
TELearn AI Simplified - Machine Learning (Part 3) Unsupervised learning
Learn what is unsupervised learning, the types and also its applications.
#learnaisimplified #aimadesimple #techdude2K26
@tech_dude999
288.7K
ENMachines learn in two very different ways.
One is taught the answers.
The other has to figure everything out.
Supervised Learning
The model learns from labeled data where the correct answer is already known.
Example: Emails labeled as spam or not spam.
The model studies these examples and learns to predict for new emails.
Used for:
Classification and regression.
Unsupervised Learning
The model learns from unlabeled data.
No correct answers are given.
The model tries to discover hidden patterns in the data.
Example: Grouping customers based on similar behavior.
Used for:
Clustering and dimensionality reduction.
Simple way to remember:
Supervised → learning with answers
Unsupervised → discovering patterns.
Follow @engineerbeatsai to master AI
#MachineLearning #AI #SupervisedLearning #UnsupervisedLearning #DataScience GenAI AIEngineering EngineerBeatsAI
@engineerbeatsai
609.7K
WOSteve brunton is sooo GOATEDDD !!!
#machinelearning #datascience #stem #artificialintelligence
@workiniterations
46.3K
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
8.1K
BAMachine Learning has three main types.
Supervised Learning
→ The model learns from labeled data.
Unsupervised Learning
→ The model finds patterns in unlabeled data.
Reinforcement Learning
→ The model learns through rewards and penalties.
Different approaches.
Same goal: learning from data.
Understand these three,
and the ML world becomes much clearer.
SAVE this before diving deeper into ML.
#machinelearning
#artificialintelligence
#aiml
#datascience
#mlbasics
#supervisedlearning
#techreels
#typographyinspired
#typographydesign
@bakwaso_pedia
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