Niaz Chowdhury, PhD’s Post

🐍 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 67 — Correlation Heatmaps Day 67 of #python365ai 🔥 Heatmaps visualise correlation between variables. Example: sns.heatmap(df.corr()) 📌 Why this matters: Correlation analysis is key in feature selection. 📘 Practice task: Generate a correlation matrix and visualise it. #python365ai #Heatmap #FeatureEngineering #Python

Recently started exploring Python in the AI ecosystem. One thing I really like about Python is how quickly you can move from idea to implementation. Example: A simple model predicting output from input data. from sklearn.linear_model import LinearRegression X = [[1], [2], [3]] y = [2, 4, 6] model = LinearRegression() model.fit(X, y) print(model.predict([[4]])) Just a small experiment, but it shows how machines can learn relationships from data. Excited to keep learning and building more with Python and AI. #Python #AI #MachineLearning #DeveloperLife

SkillCourse Day 5/30: Mastering User Interaction in Python I just wrapped up Day 5 of the "30 Days of Python with AI" challenge by Satish Dhawale sir! Today was all about making programs interactive. Key takeaways: The input() function: Learning how to capture user data. Type Casting: Why converting strings to int() or float() is crucial for calculations (no more 1 + 1 = 11 errors!). Data Integrity: Understanding how Python handles different data types during input. #Python #CodingChallenge #AI #LearningInPublic #SatishDhawale #DataAnalyst

🐍 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 76 — Standard Deviation Day 76 of #python365ai 📏 Standard deviation shows the typical distance from the mean. Example: np.std(data) 📌 Why this matters: Standard deviation is widely used in statistics and machine learning. 📘 Practice task: Compare standard deviation for two datasets. #python365ai #StandardDeviation #Statistics #Python

Exploring KNN Imputation in Machine Learning 🤖 Built a Kaggle notebook to learn how KNN Imputer can handle missing values by using patterns from similar data points. A simple yet powerful technique for smarter missing value handling. Kaggle notebook 👇 [https://lnkd.in/gvpirkKn] #MachineLearning #DataScience #Kaggle #Python

Ever feel your Python loops are a bit clunky? You often calculate a value. Then you immediately check it in the next line. This trick lets you assign and check a variable *right inside* your condition. It makes data processing cleaner and more direct for AI/ML tasks. 💡 Do you use the walrus operator? Or what's your favorite Python trick for cleaner loops? #Python #AI #MachineLearning #CodingTips #Tech

Day 2 of my AI/ML journey Today was all about going deeper into the basics of Python — and honestly, it made me realize how important these fundamentals are. Worked on concepts like: User input and formatted output Arithmetic operations Data types and type conversion Working with numbers and averages Extracting integer and fractional parts What stood out today: Even simple problems start making more sense when you actually sit down and solve them instead of just reading. Trying to stay consistent and build real understanding step by step. #Python #LearningInPublic #DataScienceJourney #AI #MachineLearning #Consistency #100DaysOfCode

Mastering machine learning sounds cool until you're buried in math, lost in algorithms, and wondering what Python package you're supposed to install next. If you've ever: - Opened a tutorial and closed it 10 minutes later - Felt like everyone else already gets it - Wondered where you were supposed to start... This blog post can help you. It breaks down the real path to getting started with machine learning using Python. #MachineLearning #Python #AI #DataScience #RheinwerkComputingBlog #RheinwerkComputingInfographic Take your first (or next) step here: https://hubs.la/Q047Wntr0