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Day 3 — DSA with Python Solved Group Anagrams today. Yesterday was about understanding what makes two words anagrams. Today was about applying that concept at scale. Key Insight: Sorting transforms all anagrams into the same representation. "eat" → "aet" "tea" → "aet" "ate" → "aet" Same sorted key → same group. A simple hash map does the job efficiently. What stood out today: DSA isn’t about isolated problems. It’s a chain. Concepts compound. Yesterday’s understanding becomes today’s solution. That’s when learning shifts from memorizing to thinking. On to Day 4. #DSA #Python #100DaysOfCode #PlacementPrep #LearningInPublic

AI Engineer: Day 2/100 Extract text from a PDF document using Python. Remove stop words (useless words like "and," "with," or "or"). Convert the remaining text into tokens (tokenization). Library: PyPDF2 Benefit: Document parsing Comment below if you want the code!

🐍 Python just hit rank #1 globally — and it's not slowing down. From rank 26 in 2001 to the top of the TIOBE index in 2026, Python now holds 22%+ of all programming language interest worldwide. What's driving this? → 80%+ of ML/AI projects run on Python → Python 3.14 dropped in Oct 2025 with major JIT compiler upgrades → Over 178,000 active packages on PyPI, growing 47% annually If you're in tech and not learning Python, you're leaving opportunities on the table. Are you using Python in your current role? Drop a comment 👇 #Python #Programming #AI #Tech2026

Human > narrative > database > LLM interpretation > Python libraries (et al.) actions. Recall we said this many years ago: RL’s Q-matrix and the likes are lazy ways to code via trial and error. The matrix itself is a large if-then. We are just seeing more ways to code. Great yet no intel on sight. Don’t you call it intel when you want to say knowledge.

Week 1 Report – ML in Python 05/04: Data Preprocessing in Python Started my Machine Learning journey in Python today by diving into the most important foundation step, Data Preprocessing. In real-world scenarios, datasets are rarely clean or ready to use. They often contain missing values, inconsistent formats, or features with different scales. Before training any model, we need to prepare the data properly. This process includes: -Importing essential Python libraries -Loading the dataset and splitting it into feature matrix (X) and target variable (y) -Handling missing values using statistical methods like mean, median, or mode -Encoding categorical variables into numerical format so models can process them -Applying feature scaling to ensure all features contribute equally, especially when values vary in magnitude

Learn how to build a Python recommendation system with TensorFlow, including data preprocessing, model training, and model evaluation https://lnkd.in/gS7dTJ84 #PythonRecommendationSystem Read the full article https://lnkd.in/gS7dTJ84

Learn how to build a Python recommendation system with TensorFlow, including data preprocessing, model training, and model evaluation https://lnkd.in/gS7dTJ84 #PythonRecommendationSystem Read the full article https://lnkd.in/gS7dTJ84

Learn how to build a Python recommendation system with TensorFlow, including data preprocessing, model training, and model evaluation https://lnkd.in/gS7dTJ84 #PythonRecommendationSystem Read the full article https://lnkd.in/gS7dTJ84

Learn how to build a Python recommendation system with TensorFlow, including data preprocessing, model training, and model evaluation https://lnkd.in/gS7dTJ84 #PythonRecommendationSystem Read the full article https://lnkd.in/gS7dTJ84

🐍 Day 117 — Hyperparameter Tuning Day 117 of #python365ai ⚙️ Tune model settings to improve performance. Example: from sklearn.model_selection import GridSearchCV 📌 Why this matters: Small changes can significantly improve results. 📘 Practice task: Tune one parameter in a model. #python365ai #HyperparameterTuning #ML #Python