Correlation Heatmaps with Python

🐍 Day 93 — Linear Regression (Implementation) Day 93 of #python365ai 🧑💻 Example: from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X, y) 📌 Why this matters: This is your first real ML model. 📘 Practice task: Fit a simple regression model. #python365ai #MLModel #Python #DataScience

🐍 Day 83 — Correlation Day 83 of #python365ai 🔗 Correlation measures the relationship between variables. Values range from: -1 → strong negative relationship 0 → no relationship +1 → strong positive relationship Example: df.corr() 📌 Why this matters: Correlation helps identify useful predictive variables. 📘 Practice task: Calculate correlation between two numeric columns. #python365ai #Correlation #DataScience #Python

🐍 Day 95 — Model Evaluation (Mean Squared Error) Day 95 of #python365ai 📏 Evaluate models using metrics like MSE. Example: from sklearn.metrics import mean_squared_error 📌 Why this matters: We need to measure how good a model is. 📘 Practice task: Compute error for predictions. #python365ai #ModelEvaluation #ML #Python

🚀 I built linear regression from scratch. Then I wrote about it. No libraries. Just Python, NumPy, and gradient descent. 📖 Read the full blog on Medium → https://lnkd.in/gcq7t6CW This is what deep learning really looks like, starting with one variable and understanding every line of code. #MachineLearning #FromScratch #Python #LinearRegression #Blog

🐍 Day 89 — Features and Labels Day 89 of #python365ai 📌 Features (X) → input variables Labels (y) → output Example: X = [size, rooms] y = price 📌 Why this matters: Clear distinction is essential for building ML models. 📘 Practice task: Identify features and labels in a dataset. #python365ai #Features #MachineLearning #Python

🐍 Day 92 — Linear Regression (Concept) Day 92 of #python365ai 📈 Linear regression models relationships between variables. Equation: y = mx + c 📌 Why this matters: It’s one of the simplest and most important ML models. 📘 Practice task: Think of predicting salary based on experience. #python365ai #LinearRegression #MachineLearning #Python

🚀 Day 5 of My Generative & Agentic AI Journey! Today’s focus was on understanding Tuples in Python and how they work. Here’s what I learned: 🔗 Tuples in Python: • Tuples are denoted using () brackets • They are immutable — once created, they cannot be changed • Useful for storing fixed data 🔄 Swapping Values: • Learned a very clean Python trick to swap values • Example: A, B = 2, 1 • Swap using: A, B = B, A 🔍 Checking Elements: • Used the “in” keyword to check if an element exists in a tuple 👉 Key takeaway: Tuples are simple, efficient, and useful when you don’t want your data to change. Slowly building strong Python fundamentals step by step 💪 #Day5 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

Recently I attended a webinar on Python 🐍 with AI 🧠 by TOPS Technologies 🧑💻 . Honestly, it was a really interesting session 👌. 👉 I got to understand how Python is actually used in AI and real-world applications. ✍ Still learning, but moving step by step 👍 #Python #AI #LearningJourney

What is smolagents? smolagents is an open-source Python library designed to make it extremely easy to build and run agents using just a few lines of code. https://lnkd.in/d2ukC5aR #Agents #AIAgents #Smolagents #HF #AngeloSorte #2026 #AI #AIEngineering

Applying ANOVA analysis in Python 📊 Used Statsmodels and Pandas to examine whether Total Liabilities significantly differ based on TAD (Total Asset Dummy) in the financial dataset. Learning how statistical techniques help uncover meaningful financial insights. #Python #DataAnalytics #ANOVA #FinancialAnalysis #LearningByDoing 🚀