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MADo you think we can build a solid model at the end of this year? #formula1 #machinelearning #programming
@mar_antaya
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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
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#machinelearning #statistics #mathematics #math #physics #computerscience #coding #science #education #datascience #knn
@aibutsimple
2.2M
EQThe Secret Behind Every Trend Line ! #LeastSquares #LinearRegression #DataScience #Math #Statistics #MachineLearning Ever wondered how software finds the perfect line through messy data points? This short animation explains the Least Squares Method, the backbone of linear regression. We visualize the difference between data points and the trend line as physical squares, showing exactly what it means to minimize the sum of squared errors. Watch as the line adjusts its slope and intercept until it finds the optimal fit for the data set.
@equationsinmotion
196.8K
ILk-Nearest Neighbors 📍
#ia #machinelearning #ciencia #knearestneighbors #math
@illariy.ai
190.7K
SMLearning something new everyday 📚
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#softwareengineer #codingisfun #growthmindset #machinelearning #sydneyengineers #algorithms #deeplearning
@smith.iscoding
1.8M
REPython wasn't built to be trendy - it was built to be useful.
Guido van Rossum created Python because C was powerful but unsafe, and shell scripts were too limited in scope.
He wanted a language that was easier to use, safer than C, and smart enough to handle things like memory management and bounds checking - without slowing developers down.
That decision is why Python powers Al, machine learning, data science, automation, and startups worldwide today.
Sometimes the best tech isn't invented to impress it's invented to solve a real problem.
Do you think Python is still the best beginner-friendly language in 2026?
Follow @realbigbrainai to stay up to date with the latest Al news.
@realbigbrainai
2.0M
NA💻 Tech & AI (Top Earners)
1. AI / Machine Learning
Engineer – $150K–$400K+
2. Data Scientist – $130K–$250K
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@nardosedaily
259.0K
DAMain Challenges in Machine Learning:
1. Insufficient or Poor-Quality Data
Lack of labeled data for supervised learning.
Noisy, incomplete, or biased data can lead to poor models.
2. Overfitting and Underfitting
Overfitting: Model performs well on training data but poorly on new data.
Underfitting: Model is too simple to capture the underlying pattern.
3. High Computational Cost
Training complex models (e.g., deep learning) requires powerful hardware and GPUs.
4. Scalability
Models trained on small datasets may not scale well to real-world data.
5. Model Interpretability
Many powerful models (like deep neural networks) act as "black boxes" with low transparency.
6. Data Privacy and Security
Legal and ethical concerns around collecting and using personal data (e.g., GDPR).
7. Bias and Fairness
Models can inherit or amplify biases present in training data, leading to unfair outcomes.
8. Deployment and Maintenance
Moving from prototype to production can be complex (MLOps needed).
Continuous monitoring and updating are essential.
9. Choosing the Right Algorithm
Selecting the most suitable model and tuning it can be time-consuming and non-trivial.
10. Domain Knowledge
Understanding the domain is crucial to feature selection, data preparation, and result interpretation.
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#datascience #machinelearning #python #ai #dataanalytics #artificialintelligence #deeplearning #bigdata #agenticai #aiagents #statistics #dataanalysis #datavisualization #analytics #datascientist #neuralnetworks #100daysofcode #genai #llms #datasciencebootcamp
@datasciencebrain
39.0K
PRComplete machine learning handwritten notes 📝
@programmer_talkz
847.5K
SOResearcher name: Guangting Yu, Dailey Labs
The next wave of AI isn’t just chat. It’s simulation.
As models get better at learning dynamics, differential equations, and world models, AI moves closer to running experiments, testing ideas in simulated environments, and helping engineer real systems before they’re built.
Simulation engineering may be one of the biggest frontiers in AI. 🌊
#startuplife #founder #tech #machinelearning #ai
@sorhan.hq
6.0K
KAWhat is Machine Learning?
#ml #datascientist #machinelearning
@karinadatascientist
2.4M
SOme every day 🥹🥲
#research #computerscience #datascience #computervision #girlwhocodes #codinglife #softwareengineer #artificialintelligence #studygram #machinelearning #womenintech #womenwhocode #tech #learningdiary #researchpaper
@sopi.iscoding
✨ Guia de Descoberta #Machinelearning
O Instagram hospeda 15 million postagens sob #Machinelearning, criando um dos ecossistemas visuais mais vibrantes da plataforma.
Descubra o conteúdo mais recente de #Machinelearning sem fazer login. Os reels mais impressionantes sob esta tag, especialmente de @sopi.iscoding, @equationsinmotion and @nardosedaily, estão ganhando atenção massiva.
O que está em alta em #Machinelearning? Os vídeos Reels mais assistidos e o conteúdo viral estão destacados acima.
Categorias Populares
📹 Tendências de Vídeo: Descubra os últimos Reels e vídeos virais
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🌟 Criadores em Destaque: @sopi.iscoding, @equationsinmotion, @nardosedaily e outros lideram a comunidade
Perguntas Frequentes Sobre #Machinelearning
Com o Pictame, você pode navegar por todos os reels e vídeos de #Machinelearning sem fazer login no Instagram. Sua atividade permanece completamente privada - sem rastros, sem conta necessária. Basta pesquisar a hashtag e começar a explorar conteúdo trending instantaneamente.
Análise de Desempenho
Análise de 12 reels
💡 Posts top têm média de 2.1M visualizações (2.0x acima da média)
Publique regularmente 3-5x/semana em horários ativos
Dicas de Criação de Conteúdo e Estratégia
🔥 #Machinelearning mostra alto potencial de engajamento - publique estrategicamente nos horários de pico
✨ Muitos criadores verificados estão ativos (42%) - estude o estilo de conteúdo deles
✍️ Legendas detalhadas com história funcionam bem - comprimento médio 536 caracteres
📹 Vídeos verticais de alta qualidade (9:16) funcionam melhor para #Machinelearning - use boa iluminação e áudio claro
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Explorar Machinelearning#what is machinelearning