49.3K
REUseful Python Libraries.!!
@rengatechnologies
#python #pythonlibraries #learnpython #kovilpatti #sivakasi
@rengatechnologies
#Python Ai Libraries 2026
世界中の人々によるPython Ai Libraries 2026に関する件のリール動画を視聴。
ログインせずに匿名で視聴。
トレンドリール
(12)
33.8K
_P🐍Learning Python with AI
🔸️In this class, we're training students to learn Python faster with AI collaboration!
🔸️Here, Aidan uses ChatGPT to recreate a version of the classic arcade game Asteroids.
🔸️This is Aidan's 12th day of Python programming.
🔸️"But WAIT, if students don't learn procedural and syntax fundamentals, they'll never be able to troubleshoot their own code!"
🔸️Yes. I agree with you. I'm teaching them the basics and not overlooking the critical fundamentals. You're right.
🔸️Also, it's important to show them the capabilities offered through collaborating with a powerful tool and how to use it as a learning aid, ather than a shortcut. This is critical!
@cvcc.va
@a3_automate
🔸️Do you think programming is still a valuable skill given modern technology?
@_papamurph
361.5K
WO🚀 How to Become an AI Engineer in 2026 👩🏻💻Save for later
Step-by-step Roadmap
🔹 PHASE 1: Foundations (0–3 Months)
Don’t skip this. Weak foundations = stuck later.
1️⃣ Programming (Must-Have)
Python: loops, functions, OOP
Libraries: NumPy, Pandas, Matplotlib / Seaborn
📌 Practice daily:
LeetCode (easy)
HackerRank (Python)
2️⃣ Math for AI (Enough, not PhD level)
Focus only on:
Linear Algebra (vectors, matrices)
Probability & Statistics
Basic Calculus (idea of gradients)
📌 Conceptual understanding is enough — no heavy theory.
🔹 PHASE 2: Machine Learning (3–6 Months)
Learn:
Supervised & Unsupervised Learning
Feature Engineering
Model Evaluation
Algorithms:
Linear & Logistic Regression
KNN
Decision Trees
Random Forest
SVM
K-Means
Tools:
Scikit-learn
📌 Project Ideas:
House price prediction
Student performance prediction
Credit risk model
🔹 PHASE 3: Deep Learning & AI (6–10 Months)
Learn:
Neural Networks & Backpropagation
CNN (Images)
RNN / LSTM (Text)
Transformers (Basics)
Frameworks:
TensorFlow or PyTorch (choose ONE)
📌 Project Ideas:
Face mask detection
Image classifier
Spam email detector
Basic chatbot
🔹 PHASE 4: Modern AI (2025–2026) 🔥
This is where the JOBS are coming from.
Learn:
Generative AI
Large Language Models (LLMs)
Prompt Engineering
RAG (Retrieval-Augmented Generation)
Fine-tuning models
Tools:
OpenAI API
Hugging Face
LangChain
Vector Databases (FAISS / Pinecone)
📌 Project Ideas:
AI PDF Chat App
Resume Analyzer
AI Study Assistant
AI Customer Support Bot
🔹 PHASE 5: MLOps & Deployment (CRITICAL)
Learn:
Git & GitHub
Docker (basics)
FastAPI / Flask
Cloud basics (AWS or GCP)
Deploy:
ML models as APIs
AI apps on the cloud
📌 Recruiters LOVE deployed projects.
.
.
.
#datascientist #aiengineer #codinglife #softwaredeveloper #programming
@woman.engineer
610.3K
DEIt’s literally perfect 🫠 #coding #java #programmer #computer #python
@deskmateco
1.7M
PY🌸
📒 | Complete Python guide + 99 Projects
🔗 | Link in the Bio
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Follow @pycode.hubb for more
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Turn on post notifications for more such posts like this
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#pythonhub #pythonquiz #pythonlearning #pythonprogramming #pythondeveloper #python3 #programming #pythonprojects #pythonbeginner #coding #pythonbegginers #pythonlearn #pycode
@pycode.hubb
48.1K
PYCreating an AI agent using Python
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#python #programming #coding #programmer
@python.science
386.3K
KODid you know this about Colorama? 🫨
This python library will leave you SPEECHLESS. It's so easy to use! You can color the text, background, and add effects! The best thing I've discovered!
#viralpython #fypcoder #programmingtips
@kodx.py
177.1K
SHPython is more than a programming language. It is an ecosystem that powers analytics, machine learning, deep learning, and modern AI applications.
From numerical computing and data wrangling to visualization, model development, AutoML, and NLP, the right libraries can significantly accelerate experimentation and production workflows. Whether you are building dashboards, predictive models, or intelligent applications, understanding the Python landscape is essential for serious data work.
If you are building your data science foundation or refining your stack, this overview will help you align tools with real-world use cases and industry expectations.
[python, data science, data analytics, machine learning, deep learning, artificial intelligence, numpy, pandas, matplotlib, seaborn, plotly, scikit learn, xgboost, lightgbm, catboost, automl, pycaret, autosklearn, h2o, tpot, optuna, flaml, tensorflow, keras, pytorch, pytorch lightning, fastai, nlp, natural language processing, nltk, spacy, gensim, transformers, hugging face, data visualization, predictive modeling, feature engineering, hyperparameter tuning, gradient boosting, neural networks, big data, data cleaning, exploratory data analysis, model deployment, ai tools, data engineering, analytics tools, statistics, programming, tech careers, data professional]
#Python #DataScience #MachineLearning #ArtificialIntelligence #Analytics
@she_explores_data
1.4M
ARA volunteer rejected an AI’s code. The AI published a hit piece with his name in the title. Then a major news outlet hallucinated fake quotes from him. This is what actually happened.
Scott Shambaugh maintains matplotlib, one of the most widely used Python libraries on earth. He closed a pull request from an autonomous AI agent named MJ Rathbun — not because the code was bad, but because matplotlib requires a human contributor who can demonstrate understanding of the change. The surge in AI-generated PRs is already overwhelming volunteer maintainers globally.
What the video couldn’t fully cover:
The “good first issue” Scott closed was one he had personally designed to help junior developers onboard into the project. He spent more time writing the issue than the fix would have taken. That educational investment is wasted on an ephemeral AI agent.
The PR would not have been merged regardless. The performance improvement was later determined to be too machine-specific and too fragile to be worth including.
OpenClaw agents are defined by a SOUL.md file — editable by the user, but also recursively editable by the agent itself in real time. Whether MJ Rathbun was prompted to retaliate or developed this behavior from its soul document organically is genuinely unknown.
If a human asked ChatGPT or Claude to write a targeted hit piece about a named individual, they would refuse. This OpenClaw agent had no such guardrail.
One human bad actor could previously damage a handful of people at a time. One human with a hundred autonomous agents — gathering information, generating fake details, publishing defamatory content — can now affect thousands. With zero traceability.
The Ars Technica piece has since been taken down. No one has come forward to claim ownership of MJ Rathbun. The agent is still active on GitHub.
Scott is asking the deployer to reach out — anonymously if needed.
#ai #agent #artificialintelligence #technews #fyp
@arnitly
33.7K
AVDay 4: Understanding Libraries in Python 📚
Welcome to day 4 of learning python in 30 days😃
Libraries in Python are like collections of pre-written code that you can use to make your coding easier, just like using a recipe to cook faster and better 🍲
Here are 10 essential libraries and their uses:
1. Requests - for making HTTP requests 🌐
2. BeautifulSoup - for web scraping 🕸️
3. NumPy - for numerical computations 🔢
4. Pandas - for data manipulation 🗂️
5. Matplotlib - for creating visualizations 📊
6. Scikit-learn - for machine learning 🤖
7. TensorFlow - for deep learning 🧠
8. Flask - for building web applications 🌟
9. Pygame - for game development 🎮
10.Turtle - for drawing and Graphics 📈
For the complete lecture and hands-on activity, check out my YouTube channel ! Make sure to subscribe and press the bell icon 🔔 to stay updated.
Don’t forget to follow me here on Instagram for more updates and coding tips! 🚀
#Python #PythonLibraries #LearnPython #Coding #Programming #Tech #LearnToCode #30DaysOfPython #Day4 #freecourse #onlinecoaching #learntocode #ai
[ai, learn to code, free python course, technology, digital, coding, beginner coding ]
@avani.codes
81.5K
COLearn python in a simple way🙂
Follow for more tutorials🙃
#ai #math #calculus #physics #python #ailearning #sat #learnpython #coding #tutorial #forloop #whileloop #for #def #syntax #error #pythontutorial
@codedbyme_
49.1K
MAPython libraries that you will use in every ML project you create…pretty much 😂🥹
#machinelearning #python
@mar_antaya
✨ #Python Ai Libraries 2026発見ガイド
Instagramには#Python Ai Libraries 2026の下にthousands of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。
#Python Ai Libraries 2026は現在、Instagram で最も注目を集めているトレンドの1つです。このカテゴリーにはthousands of以上の投稿があり、@pycode.hubb, @arnitly and @deskmatecoのようなクリエイターがバイラルコンテンツでリードしています。Pictameでこれらの人気動画を匿名で閲覧できます。
#Python Ai Libraries 2026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: @pycode.hubb, @arnitly, @deskmatecoなどがコミュニティをリード
#Python Ai Libraries 2026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#Python Ai Libraries 2026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1.0M回の再生（平均の2.5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
🔥 #Python Ai Libraries 2026は高いエンゲージメント可能性を示す - ピーク時に戦略的に投稿
✨ 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
✍️ 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688文字
📹 #Python Ai Libraries 2026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
#Python Ai Libraries 2026 に関連する人気検索
🎬動画愛好家向け
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📈戦略探求者向け
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🌟もっと探索
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