What Data Scientists Use Every Day A Practical Percentage Guide to Core Tech Skills | Learnhub4u #Learnhub4u #DataScience #TechSkills #DataScientist #MachineLearning #Python #RStats #SQL #DataAnalysis #Statistics #BigData #DataVisualization #AI #DeepLearning

Back to basics: The Iris dataset is the 'Hello World' of Machine Learning. I used it to demonstrate how clear-cut decision boundaries can be when features are perfectly separated. What was the first dataset that made you fall in love with Machine Learning? Tech Stack: Python | Scikit-Learn | Pandas | Matplotlib | Plotly | Machine Learning #DataScience #Python #MachineLearning #ArtificialIntelligence #Portfolio

In Data Science choose model based on data nature . > Gradual Relationship = Logistic Regression > Step like decisions = Decision Tree > Complex interactions = Tree-based Models #datascience #DataAnalyst #python

⚡ Why is NumPy faster than Python lists? NumPy uses optimized C code internally. It stores data in continuous memory blocks. This allows fast mathematical operations on large datasets. That’s why NumPy is essential for AI and data science. #Python #NumPy #AI

Decision Tree is an ML model used in Data Science. > Works like human rules. > Asks step by step questions. > Splits Data into conditions. #MachineLearning #DataScience #Python #DataAnalysis

I tried bootstrapping the data before and after cleaning and compared it with the data after using Python to create a machine learning model. There was a change in the standard deviation and a narrowing of the P10 and P90 values. Data source: Alysa Suydam #python #datascience #machinelearning #geoscientist #geoscience #tds 

🚀 Pandas vs Polars — What Are You Choosing? Pandas is stable, widely adopted, and perfect for ML workflows. Polars is faster, memory-efficient, and built for large-scale data. The smart approach? Use the right tool for the right workload. Are you sticking with Pandas or exploring Polars? 👇 #dataScience #infividhya #bigdata #python #dataEngineering #AI

Day 14 – Weekly reflection ✅ This week I focused on: • Understanding AI vs ML vs DL • Data basics with Python • Maintaining daily consistency Next week: more practice and mini projects. #WeeklyReflection #AIJourney #Consistency

🐍 Python dominates data science in 2026, but success isn't just about knowing the language—it's about mastering the RIGHT libraries. After working with countless datasets and models, I've identified the 5 essential Python libraries every data scientist needs in their toolkit: 📊 Pandas - Data manipulation powerhouse 🔢 NumPy - Numerical computing foundation   📈 Matplotlib/Seaborn - Visualization storytelling 🤖 Scikit-learn - Machine learning workhorse 🚀 Polars - The speed game-changer 💡 Pro tip: Don't just learn syntax—understand WHEN to use each tool. What's YOUR essential Python library? 👇 #DataScience #Python #MachineLearning #DataAnalytics #AI #DataScientist #PythonProgramming #Analytics

🚀 Day 43/100 – Python, Data Analytics & Machine Learning Journey 🤖 Started Module 3: Machine Learning 📚 Today I learned: 7. Train Test Split 8. Correlation 9. Feature Selection Machine Learning is the core of AI systems, and I’m excited to explore algorithms, models, and real-world applications in the coming days. 📌 Code & Notes: https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic #DataScience