Niaz Chowdhury, PhD’s Post

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐜𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐦𝐚𝐝𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐦𝐨𝐫𝐞 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐦𝐞 While exploring datasets in Python recently, I spent some time understanding how correlation works between variables. Using pandas, it’s surprisingly easy to calculate a correlation matrix and see how different columns relate to each other. Sometimes two variables move together strongly, and sometimes there’s almost no relationship at all. What I found interesting is that correlations can quickly highlight patterns that might not be obvious just by looking at raw numbers. Still learning how to interpret these relationships properly, but it’s definitely making the analysis process more insightful. #Python #Pandas #DataAnalytics

I recently worked on a small machine learning project where I tried predicting housing prices using Decision Tree Regression. I used the California Housing dataset and went through the full process — cleaning the data, exploring patterns, building the model, and evaluating how well it performs. It was interesting to see how different factors like income and location influence house prices, and how decision trees handle these relationships. This project gave me a better understanding of how regression models work in practice and the importance of avoiding overfitting while tuning the model. 🔗 Link:- https://lnkd.in/gzwVU_dn #MachineLearning #DataScience #Python #LearningJourney

🐍 Day 80 — Sampling and Population Day 80 of #python365ai 🧪 Population → entire dataset Sample → subset of data 📌 Why this matters: We usually analyse samples to infer properties of a population. 📘 Practice task: Take a small sample from a dataset and compute its mean. #python365ai #Sampling #Statistics #Python

Today, I focused on working with NumPy arrays. Building a solid foundation for data manipulation and analysis. Here’s what I practiced: 🔹 Created a 1D array with values from 1 to 15 🔹 Built a 2D array (3×4) filled with ones 🔹 Generated a 3×3 identity matrix 🔹 Explored key array properties like shape, type, and dimensions 🔹 Converted a regular Python list into a NumPy array This session helped me better understand how data is structured and handled in numerical computing. Getting comfortable with arrays is definitely a crucial step toward more advanced data analysis and machine learning tasks. Looking forward to building on this momentum 💡 #AI #MachineLearning #Python #NumPy #DataAnalysis #M4ACE

Excited to share my latest project: LinearRegression-ML This is a beginner-friendly Machine Learning project focused on understanding and implementing Linear Regression from scratch. It includes practical notebooks like profit analysis and medical data predictions, along with clear explanations of loss and cost functions. ???What I learned =>Fundamentals of Linear Regression =>Cost & loss function implementation =>Real-world dataset analysis using Python #https://lnkd.in/guCQQdNe #MachineLearning #Python_Jupyter_Notebook #DataScience

🐍 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

Claude just diagnosed me with a classic developer bug 😂 After hours of learning Python — functions, loops, dictionaries, if/else, and AI agent architecture — I started asking the same questions twice. Claude's response? ``` while awake == True:   ask_questions()       if questions == repeat:     print("Go to sleep Anil! 😄")     break ``` Turns out even humans need a break statement. 😄 The grind is real. But so is the progress. 💪 #Python #AI #MachineLearning #CareerChange #AIAgent #LearningToCode #Claude #100DaysOfCode

Tree Depth: Recursive Decomposition Mirrors Tree Structure Depth calculation decomposes naturally — tree depth = 1 + max(left depth, right depth). The recursive structure matches the data structure, making recursion the clean solution. Problem structure alignment is when recursion shines. When Recursion Wins: Problems where solution composes directly from subproblem solutions (divide-and-conquer, optimal substructure). Implementation mirrors mathematical definition. Time: O(n) | Space: O(h) recursion #Recursion #TreeDepth #DivideAndConquer #AlgorithmSimplicity #Python #SoftwareEngineering

I’ve been spending time lately diving deeper into NumPy to master efficient data manipulation. From understanding N-dimensional arrays to implementing linear algebra operations like matrix inversion and eigenvalues, it's fascinating to see how these fundamentals power the most complex Machine Learning models. Current focus: Optimizing array slicing and indexing. Exploring data preprocessing and synthetic dataset generation. Bridging the gap between mathematical theory and Python implementation. Onwards and upwards! 🚀 #DataScience #Python #NumPy #MachineLearning #ContinuousLearning #WebDevelopment

Day 2 of learning Machine Learning. Today I worked on a simple linear regression model using Python in Jupyter Notebook. The idea was straightforward: - Input (x): house size - Output (y): price Model used: f(x) = wx + b I understood how: - Training data is structured (x_train, y_train) - Parameters (w, b) define the relationship - The model uses this to make predictions on new inputs Also got hands-on with NumPy and basic plotting using Matplotlib. Still very early, but it's becoming clearer how data is converted into predictions. #MachineLearning #AI #Python #LearningInPublic