#Algorithms In Machine Learning

If you want to learn Al 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 Al systems. #machinelearning #deeplearning #statistics #explorepage #viral

📍6 Pillar Machine Learning Algorithms (Episode 88 of 100): DM to download the Free PDF👇 1. Support Vector Machine (SVM): SVM is a commonly applied supervised machine learning algorithm that searches hyperplane with maximal separation from each data class. 2. Naive Bayes (NB): Naive Bayes, another supervised ML algorithm, is a probabilistic method based on Bayes’ law. 3. Logistic regression: Logistic regression is a classification algorithm utilized for probability prediction of target class by logistic function. 4. K-Nearest Neighbors: The K-Nearest Neighbors is a distance-based algorithm as it first finds all the closest points around new unknown data point and calculates the distance between them to determine the class of new data points. 5. Decision Trees: Decision tree, a supervised machine learning algorithm, is a tree-structured classifier that continuously divides the data based on specific parameters. 6. Random Forest: The random forest comprises multiple decision trees and can provide more accurate predictions by combining all of them. ⏰ Like this Post? Go to our bio, click subscribe button and subscribe to our page. Join our exclusive subscribers channel ✨ Hashtags (ignore): #datascience #python #python3ofcode #programmers #coder #programming #developerlife #programminglanguage #womenwhocode #codinggirl #entrepreneurial #softwareengineer #100daysofcode #programmingisfun #developer #coding #software #programminglife #codinglife #code

Day 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. . . . . #MachineLearning #ArtificialIntelligence #CodeLoopa #LearnAI #TechExplained

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

I’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

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

What is an algorithm? 🤯 It’s not magic. It’s just step-by-step logic. Search, sort, recommend… this is what runs everything. Wether you are CS student, a junior or a senior engineer, learning algorithms + DSA is how you learn to actually solve real-world problems. 🚀

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

focus on understanding the inner workings here not just the code associated — the code is the easy part the math is what’s worth spending time on #techcareer #machinelearning #ai #coding #python #algorithm #datascience

How Machines Learn - Part 1 This is gradient descent. The algorithm behind every AI you’ve ever used. In this series, we’ll go over the basics of machine learning and AI. Slowly building our intuition and foundation, understanding the math, and finally taking on tougher projects. This is all in effort of my mission; providing the best education I can give for free. Thanks for watching! #ai #machinelearning #software #manim #engineering

If 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

Follow @cloud_x_berry for more info #MachineLearning #MLAlgorithms #DataScience #AI #LearnML machine learning algorithms explained, linear regression model, logistic regression classification, decision tree algorithm, support vector machine svm, knn algorithm explained, dimensionality reduction techniques, random forest algorithm, k means clustering algorithm, naive bayes classifier, supervised learning algorithms, unsupervised learning algorithms, classification vs regression, ml basics for beginners, data science concepts, ai model types, feature engineering basics, model selection techniques, ml interview preparation, machine learning fundamentals