Giannis Tolios’ Post

This week, I continued my learning journey into a deeper level: Advanced Python and an introduction to NumPy as a fundamental tool for data processing. At this stage, I started to understand how Python goes beyond simple scripting and can efficiently handle more complex operations—especially when working with large-scale data. With NumPy, numerical computations become faster and more structured, from handling multidimensional arrays to performing optimized mathematical operations. This learning experience has broadened my perspective on how data is processed behind the scenes, particularly in data science and machine learning. I’ve summarized these materials into a slide deck for easier understanding. Feel free to check out the PPT here 👇 Digital Skola #DigitalSkola #LearningProgressReview #DataScience

I turned my NumPy notes into a clean visual cheat sheet for data cleaning & preprocessing 🧠 If you're learning data science, this is what you actually need: ✔ Remove NaN values ✔ Filter messy data ✔ Normalize datasets ✔ Prepare arrays for ML No theory. Just practical commands. I’ve compiled everything into a simple, visual format 👇 If you're learning Python/AI, save this for later. #Python #NumPy #DataScience #AI #MachineLearning #Coding

As part of my continuous learning journey in Python, Data Analysis, and Artificial Intelligence (AI), I documented and published my Python Libraries notes on GitHub. These notes cover key libraries: NumPy for numerical computing, Pandas for data manipulation and analysis, Matplotlib and Seaborn for data visualization and creating meaningful insights from data. 💻 Python Libraries Notes 🔗 HTML version: https://lnkd.in/dUV83GYF 🔗 PDF version: https://lnkd.in/deJvpWPi Continuing to build my skills in Data Analysis and AI by learning and sharing knowledge. 🚀 #Python #DataAnalysis #ArtificialIntelligence #NumPy #Pandas #DataVisualization #LearningJourney

📊 Understanding Joint Distributions in Probability Ever wondered how to model the relationship between two random variables? A joint distribution is the key! It describes the probability of two (or more) events happening simultaneously, giving us a complete picture of their interaction. In my latest Python experiment, I created a simple joint distribution table for two discrete variables, X and Y, representing the number of heads and tails in two coin flips. Here’s what I learned: Joint distribution tells us the probability of both X and Y taking specific values. Marginal distributions help us understand each variable independently. Conditional distributions show how one variable behaves given a specific value of the other. This concept is foundational in statistics, machine learning, and data science. It’s amazing how much insight we can gain from just a few lines of code! 🔗 Check out the code snippet in the comments if you’re curious to try it yourself. #Probability #Statistics #DataScience #Python #MachineLearning #Coding

🚀 Hands-on Machine Learning Project: Decision Tree Classifier Recently, I worked on a small but insightful project where I implemented a Decision Tree Classifier using Python and Scikit-learn. 📊 What I did: Created a structured dataset with features like Age, Salary, and Experience Applied data preprocessing techniques Built and trained a Decision Tree model Evaluated performance using Confusion Matrix & Classification Report Visualized patterns using Seaborn 📈 Key Learnings: How Decision Trees split data based on feature importance Importance of handling data properly before modeling Understanding evaluation metrics like precision, recall, and F1-score 💡 This project helped me strengthen my fundamentals in machine learning and model evaluation. 🔗 I’ll be sharing the GitHub repository soon! #MachineLearning #DataScience #Python #ScikitLearn #DecisionTree #DataAnalytics #LearningJourney

🚀 Machine Learning Journey (Prime 2.0) : Day-2 Continuing my Python learning journey, today I focused on control flow and problem-solving concepts that are essential for building logic in Machine Learning 🧠💻 I covered: • Conditional statements (if-else, nesting, and match-case) • Solving problems like checking odd/even numbers • Loops in Python (while & for loops) • Practicing loop-based problems like multiplication table and sum of N numbers • Understanding break and continue statements • Using the range() function effectively • Solving string-based problems like vowel count • Introduction to functions in Python One interesting insight from today: Loops and conditionals are the core of logical thinking in programming—most real-world ML problems rely heavily on these fundamentals. This session helped me improve my problem-solving approach using Python. Still need more practice to write optimized logic, but the basics are getting stronger 📈 Excited to move closer to actual Machine Learning concepts soon 🚀 #MachineLearning #Python #AI #DataScience #LearningInPublic #DeveloperJourney #ApnaCollege #MLJourney #prime2.0

🐍📈 Math for Data Science In this learning path, you'll gain the mathematical foundations you'll need to get ahead with data science #python #learnpython

Been learning Data Analytics for the past few months. One thing is clear: numbers aren’t optional — they are the core. Everything in analytics revolves around how efficiently you can process, manipulate, and extract meaning from data. That’s where NumPy comes in. Built on C, it’s significantly faster and more efficient than plain Python for numerical operations — often by huge margins. If you’re still relying only on Python loops, you’re doing it wrong. Sharing a quick NumPy cheat sheet I’ve been using to level up my workflow. Stop writing slow code. Start thinking in arrays. #DataAnalytics #DataScience #Python #NumPy #MachineLearning #AI #Programming #DataAnalysis #LearnDataScience #Upskilling #CareerGrowth #CodingLife #BuildInPublic

Day 06 of Python + Data Science + GenAI 🚀 at @SkillVex.in from @Harshith V V sir 🧠 What I learned today: - Functions for writing reusable and organized code - Difference between local and global variables - Using *args and **kwargs for flexible function arguments - Lambda functions for short anonymous functions 💡 Key Takeaway: Functions improve code structure, and flexible arguments make programs more dynamic and reusable. @Skillvex @Harshith V V #Python #DataScience #AI #GenerativeAI #LearningInPublic #Consistency #CareerGrowth

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