1.8M
MADo you think we can build a solid model at the end of this year? #formula1 #machinelearning #programming
@mar_antaya
Mega Popular
#Machinelearning
世界中の人々によるMachinelearningに関する15M件のリール動画を視聴。
ログインせずに匿名で視聴。
15M posts
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トレンドリール
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801.5K
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
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
3. Cybersecurity Engineer – $140K–$300K
4. Cloud Architect – $160K–$350K
5. Blockchain Developer – $150K–$300K
🩺 Medical & Health (Recession-Proof Money)
6. Surgeon – $350K–$700K+
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8. Psychiatrist – $250K–$500K
9. Dentist / Orthodontist – $200K–$450K
10. Travel Nurse / Nurse Anesthetist (CRNA) – $180K–$350K
⚖️ Law, Finance & Corporate Power Roles
11. Corporate Lawyer – $200K–$500K+
12. Investment Banker – $200K–$600K
13. Hedge Fund Manager – $500K–$Millions
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20. Elevator Technician / Union Trades – $120K–$200K+
@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
847.1K
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
✨ #Machinelearning発見ガイド
Instagramには#Machinelearningの下に15 million件の投稿があり、プラットフォームで最も活気のあるビジュアルエコシステムの1つを作り出しています。
Instagramの膨大な#Machinelearningコレクションには、今日最も魅力的な動画が掲載されています。@sopi.iscoding, @equationsinmotion and @nardosedailyや他のクリエイティブなプロデューサーからのコンテンツは、世界中で15 million件の投稿に達しました。
#Machinelearningで何がトレンドですか？最も視聴されたReels動画とバイラルコンテンツが上部に掲載されています。
人気カテゴリー
📹 ビデオトレンド: 最新のReelsとバイラル動画を発見
📈 ハッシュタグ戦略: コンテンツのトレンドハッシュタグオプションを探索
🌟 注目のクリエイター: @sopi.iscoding, @equationsinmotion, @nardosedailyなどがコミュニティをリード
#Machinelearningについてのよくある質問
Pictameを使用すれば、Instagramにログインせずに#Machinelearningのすべてのリールと動画を閲覧できます。あなたの視聴活動は完全にプライベートです。ハッシュタグを検索して、トレンドコンテンツをすぐに探索開始できます。
パフォーマンス分析
12リールの分析
💡 トップ投稿は平均2.1M回の再生（平均の2.0倍）
週3-5回、活動時間に定期的に投稿
コンテンツ作成のヒントと戦略
💡 トップコンテンツは10K以上再生回数を獲得 - 最初の3秒に集中
✍️ ストーリー性のある詳細なキャプションが効果的 - 平均長536文字
✨ 多くの認証済みクリエイターが活動中（42%） - コンテンツスタイルを研究
📹 #Machinelearningには高品質な縦型動画（9:16）が最適 - 良い照明とクリアな音声を使用
#Machinelearning に関連する人気検索
🎬動画愛好家向け
Machinelearning ReelsMachinelearning動画を見る
📈戦略探求者向け
Machinelearningトレンドハッシュタグ最高のMachinelearningハッシュタグ
🌟もっと探索
Machinelearningを探索#what is machinelearning