Switched from TensorFlow → PyTorch last month 😳 PyTorch: ✅ Dynamic graphs = debug heaven ✅ 85% research papers ✅ GitHub exploding (DeepSeek-V3 99k⭐) TensorFlow: ❌ Static = production only ❌ Verbose AF ❌ Research dying Which do YOU use? 👇 #PyTorch #TensorFlow #PythonML #MachineLearning

CrewAI Agents: 16-Wk 2026 Roadmap to Master Multi-Agent AI! From foundations to deployment vs LangChain/AutoGen. Build teams that automate forever! Who's in? 👇 #CrewAI #AIAgents #PythonML #MLRoadmap #AI2026

Closed the module with the Transformer architecture and the innovations behind modern language models. From Shakespeare-style text generation to Neural Machine Translation — this chapter was a perfect finish. On to the next phase 💪 I'm not stopping.. #pythonml #learningeveryday

Day 23/40 of python-ML. Still on Training models- today i learnt how to improve model performance through: .Learning Curves .Regularized Linear Models — Ridge, Lasso, and Elastic Net .Early Stopping to prevent overfitting and save training time. #pythonML

Day 10/40 of my Python journey. After learning the basics, I’ve started my first real project — Alien Invasion 🛸 using Pygame. So far, I’ve built the game window, background, and ship display. Excited to make it come alive! ⚡ #PythonML

Day 5/40 of my Python-ML journey Today was all about functions... Learned how to write and use functions — defining, passing info, returning values, default parameters and keeping code clean & reusable. Feeling more confident with each line I write 💪 #PythonMl #40DaysOfCode

Day 4/40 of Python -ML Today I explored while loops — how to keep programs running until a condition is met. I learned about using flags, break and continue, avoiding infinite loops, and looping through lists and dictionaries. It’s amazing learning new things ... #PythonML

Building machine learning models is just one part of the journey... Understanding them is the real challenge. Shapash is a Python library that brings transparency to machine learning by making model predictions understandable for everyone technical or not. 🛠️ Key Features of Shapash: - Generates a WebApp for navigating global and local explainability - Makes predictions easily interpretable with clear visualizations and labels - Supports regression, binary, and multiclass classification - Compatible with CatBoost, XGBoost, LightGBM, Scikit-learn, Linear Models, SVMs - Helps summarize and export local explanations - Offers metrics to evaluate the quality of explanations - Allows filtering subsets (correct/wrong predictions, feature values) for deeper analysis - Can be deployed via API or in batch mode for production use 🔍 Whether you're working on risk models, customer churn, or trading strategies if your ML model makes decisions, Shapash can help you explain why. GitHub: github.com/MAIF/shapash?tab=… Worth checking out if interpretability matters in your work. Are you someone who wants to apply AI in trading? Curious how GenAI, LLMs, and machine learning are changing the trading landscape? Then this conference is for YOU. 🎯 QuantInsti’s Algorithmic Trading Conference 2025 📅 Date: 23 September, 2025 🕒 Time: 6:00 PM IST | 8:30 PM SGT | 8:30 AM EDT 💻 Free | Online | Global What’s happening? Workshop by Tucker Balch (Emory University) Explore real-world use of AI, LLMs & price data in trading strategies. See how AI models are predicting inter-stock relationships, with live Q&A! Topics include: - How AI is transforming trading desks - Emerging skills for quants - GenAI's role in quant education - What the future of finance looks like with AI 👥 Who should attend? - Aspiring Quants - Traders & Finance Professionals - Coders & ML Engineers - Students & Career Switchers 🎟️ Ready to see how AI is shaping the future of trading? 🔗 Register now - spots are limited! 👉 quantinsti.com/algorithmic-t… #AlgoTrading #AIinTrading #QuantConference #QuantFinance #GenAI #LLM #FinanceCareers #QuantLearning #QuantInsti #EPAT #MachineLearning #ExplainableAI #MLInterpretability #XAI #Shapash #QuantFinance #AITransparency #PythonML #QuantInsti #MLTools

🧰 Scikit-learn = the Swiss Army knife of ML! From classification to clustering, it streamlines tasks with just a few lines of code. Seamless with NumPy, Pandas, & Matplotlib—your ML sidekick is here! 🔗 linkedin.com/in/octogenex/re… #ScikitLearn #ML #AI365 #PythonML

Free Resources to harness the power of AI: Embrace the wealth of free AI resources, Dive in, BUT remember, transforming knowledge into AI expertise requires dedication and consistent effort. 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 • mattwolfe • dirkzee • csdojo • analyticsvidhya • twominutepapers 𝗕𝗹𝗼𝗴 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀: • towardsdatascience • machinelearningisfun • machinelearningmastery • fastml • Ai.googleblog 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀: • paperswithcode • huggingfacedatasets • openml • machinehackdatasets • Googleplatformdatasets 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀: • mygreatlearning • classcentral • dirkzee • simplilearn • Edx 𝗣𝗼𝗱𝗰𝗮𝘀𝘁𝘀: • alunleashed • theneuralnexus • bytesofintelligence • mindsandmachines • Algorithmalley 𝗕𝗼𝗼𝗸𝘀: • almodernapproach - Russell • deeplearning - Goodfellow • mlprobabilisticperspective - Murphy • pythonml - Raschka • aianewsynthesis - Nilsson 𝗔𝗜 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆: • rmachinelearning (Reddit) • stackoverflowal • kaggleforums • towardsdatascience • alforumdscentral What is your favorite? What would you add?

🔥 Important Python Functions Every Developer Should Know Here is a list of most important built-in Python functions that every developer must know 💪: ✅ Basic Functions Function Description print() =>Console output (data display) input() =>Take user input type() =>Check data type len() =>Count the number of elements id() =>Returns memory address of object ------------------------------------------ 🔢 Number Functions Function Description abs() =>Returns absolute value round() =>Round off the number pow() =>Power of a number pow(x,y) = x^y min() =>Minimum value max() =>Maximum value ------------------------------------------ 🔥 List Functions Function Description append() =>Add item at end insert() =>Add item at index remove() =>Remove specific item pop() =>Remove item from end sort() =>Sort list reverse() =>Reverse list ------------------------------------------ 🎯 String Functions Function Description lower() =>Convert to lowercase upper() =>Convert to uppercase strip() =>Remove whitespace replace() => Replace substring split() =>Split into list join() =>Join list into string ------------------------------------------ 🔑 Dictionary Functions Function Description keys() =>Returns all keys values() =>Returns all values items() =>Returns key-value pairs get() =>Get value by key pop() =>Remove item by key ------------------------------------------ 🌶️ Advanced Functions Function Description map() =>Apply function to all items filter() =>Filter elements based on condition lambda =>Anonymous function zip() =>Combine two lists enumerate() =>Add counter to list ------------------------------------------ 🧠 Important Built-in Functions Function Description isinstance() =>Check if object is instance of class sum() =>Sum of elements all() =>Check if all elements True any() =>Check if any element True sorted() =>Sort elements ------------------------------------------ 🔥 File Handling Functions Function Description open() =>Open file read() =>Read file content write() =>Write to file close() =>Close file ------------------------------------------ Bonus Tips 💪 zip() – Combine multiple lists into tuples map() – Apply a function on every element filter() – Filter elements based on condition enumerate() – Add counter to list items #Python #PythonTutorial #PythonProgramming #PythonCourse #PythonFullCourse #PythonInOneVideo #PythonProject #LearnPython #PythonForBeginners #PythonDeveloper #PythonProject2025 #PythonMiniProject #PythonFinalYearProject #PythonAI #PythonCRUD #PythonAI #PythonML #ChatbotWithPython #AIProjectPython #PythonOpenAI #javascript #PythonInHindi #PythonTutorialHindi #coder #PythonCourseFree #Python2025 #pushpendratips #PythonWithProjects

Why is Python so popular in machine learning? Its flexibility and powerful libraries make it a go-to for small businesses tapping into AI potential. Learn more now! #PythonML #TechForBusiness #MachineLearning #AI #Python brainerhub.com/blog/why-is-p…

I have found a way to Power Jupyter Notebook on Visual Studio code. Trust me the experience is seamless #100daysofcode #python #jupyternotebook #pythonml #pythonuyo #datascience #100daysofai

Experience the future of Forex trading with PythonML, a multicurrency Expert Advisor from forexroboteasy.com. Customisable and operational on H1/H2 intervals, it uses machine learning for cutting-edge strategies. #PythonML #ForexTrading

@CDACINDIA is organizing online certificate courses with internship opportunities, starting from 18th March 2024 @GoI_MeitY #ethicalhacking #businessanalyticsAI #pythonML #NLP #cybersecurity #ICT

🤖✨ PowerBi streaming integration with Python for Machine Learning Regressor Problem: ⿡ 1. Develop the Model and save as Pickle ⿢ 2. Create a Local Server using Flask ⿣ 3. Create a PowerBi account in the web application ⿤ 4. Integrate with the Local server ⿥ 5. Get the User input through the Local Server ⿦ 6. Use Power-Bi to stream the value and plot the graphs ⿧ 7. Use the model to predict the value Effortlessly automate Power-Bi Streaming with Python and predict values! 🚀 #PythonAutomation #PowerBi #MachineLearning #DataVisiulization #Python #Automation #MachineLearning #DeepLearning #TensorFlow #PyTorch #AI #DataScience #NeuralNetworks #TechInnovation #MLFrameworks #ArtificialIntelligence #Regression #Staticstic #DataAnalysis #DataVisualization #Flask #MachineLearningIntegration #RegressionAnalysis #StreamingAnalytics #PowerBiDashboard #FlaskServer #PredictiveModeling #DataScience #PythonDevelopment #DataAnalysis #PowerBiIntegration #MLRegressor #webdevelopment #PythonML #PowerBiReports

4/ Hands-On Machine Learning with Python 🐍 🎓 Course: Machine Learning with Python: A Practical Introduction 📚 Platform: edX 📖 What you'll learn: Practical Python applications in machine learning, data preprocessing, and model building. #PythonML #HandsOnLearning

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Dei um tapinha no algoritmo de unificação cada dia mais perto do PythonML

Python use case to practice! Measure just how much impact the global economy has on the Amazon rainforest by using Python's GeoPandas and Folium packages. 👉 ow.ly/V5Mk50GzK49 #100daysofcode #learnpython #PythonML