Python Classes for Machine Learning Fundamentals

🚀 How Python Handles Data Better Than I Expected (Python Learning Journey - Day 17) When I started learning Python, I thought data was just numbers and text. Store it. Use it. Move on. But Python showed me there’s more depth to it. 👉 How data is stored matters 👉 How data is accessed matters 👉 How data is structured changes everything That realization came slowly. 🌿 What Python Taught Me About Data Python doesn’t treat data as raw values. It treats data as meaning. Lists group related items. Tuples protect fixed information. Dictionaries explain data through keys. Each structure exists for a reason. Each one communicates intent. Instead of forcing one approach everywhere, Python asks you to choose wisely. What kind of data is this? Will it change? Does it need a name? That question-first approach changed my mindset. ✔️ Data isn’t just stored → it’s designed ✔️ Structure affects clarity ✔️ Clear data leads to clear logic Once I respected data structures, my code felt calmer. Fewer guesses. Fewer errors. More confidence. 🙌 Why It Matters Most problems are data problems at their core. If data is messy, logic becomes messy. If data is clear, solutions appear faster. This lesson goes beyond Python. How we organize information shapes how we think. Python didn’t just teach me syntax. It taught me to respect data. 🔗 Now Your Turn When solving problems, do you think first about the data or the logic? #PythonLearning #Day17 #DeveloperJourney #Python #CodingMindset #DataHandling

Today’s Python focus was 𝗗𝗶𝗰𝘁𝗶𝗼𝗻𝗮𝗿𝗶𝗲𝘀 and 𝗧𝘂𝗽𝗹𝗲𝘀. I spent time understanding how Python handles structured data using key value pairs and fixed collections, and how this differs from lists. 𝗪𝗵𝗮𝘁 𝗜 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝗱 𝘁𝗼𝗱𝗮𝘆: • Creating dictionaries to store related data using meaningful keys • Accessing values using keys and using get() to avoid runtime errors • Updating existing values and adding new key value pairs • Deleting entries and checking for key existence • Iterating through dictionaries using keys and items() • Extracting only keys and only values when needed • Working with nested dictionaries to represent structured data • Iterating through nested dictionaries for multi level data • Using dictionaries to model real examples like contact details and revenue by region 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: • Dictionaries store data as key value pairs, making lookups fast and clear • Dictionaries are mutable, so values can be updated without recreating the structure • get() is safer than direct key access when keys may not exist • Nested dictionaries are useful for representing hierarchical data • Iterating through dictionaries helps process structured datasets efficiently I also revisited 𝘁𝘂𝗽𝗹𝗲𝘀 conceptually and understood where they fit: • Tuples are ordered and immutable • They are useful when data should not change • Often used for fixed records, configuration values, or safe data grouping Working with dictionaries made it clear how real world data like contacts, configurations, and reports are represented in Python. If you are learning Python as well, which data structure are you currently focusing on? #Python #PythonLearning #DictionariesInPython #TuplesInPython #ProgrammingBasics #LearningInPublic #DataAnalytics #Upskilling

"Python Mini-Series Wrap-Up: What writing production-ready Python really looks like" Over the last few posts, I shared a short Python mini-series focused on how Python is actually used in analytics and data engineering — beyond tutorials and toy examples. The core idea across the series was simple: Python becomes valuable when it’s structured, trusted, and built to scale. Here’s what I covered: • Post 1 – Structure: Treat Python work like a pipeline, not a one-off notebook • Post 2 – Unstructured data: Turning PDFs and messy text into structured datasets with regex • Post 3 – Trust: Making data quality a first-class citizen through validation and checks • Post 4 – Scale: Writing faster, more memory-efficient code with vectorization and smart data types • Post 5 – Maturity: Early mistakes that taught me why reproducibility and structure matter None of this is flashy — and that’s the point. These are the habits that turn Python scripts into workflows teams can rely on, and analyses into outputs stakeholders actually trust. If you’re early in your data career, you don’t need advanced tricks to stand out. Focus on writing Python that is: ✔ reproducible ✔ configurable ✔ readable by someone else ✔ safe to run more than once That’s what moves your work closer to production. I’ll be shifting next into SQL, using the same practical, real-world lens. 👉 Follow along — more coming soon.

Hi! Want to analyze data? Good news: Python is the leading language in the data world. Libraries like NumPy and Pandas make it easy to load, clean, analyze, and visualize your data. But wait: If your colleagues aren't coders, how can they explore your data? The answer: A data dashboard, which uses UI elements (e.g., sliders, text fields, and checkboxes). Your colleagues get a custom, dynamic app, rather than static graphs, charts, and tables. One of the newest and hottest ways to create a data dashboard in Python is Marimo. Among other things, Marimo offers UI widgets, real-time updating, and easy distribution. This makes it a great choice for creating a data dashboard. In the upcoming (4th) cohort of HOPPy (Hands-On Projects in Python), you'll learn to create a data dashboard. You'll make all of the important decisions, from the data set to the design. But you'll do it all under my personal mentorship, along with a small community of other learners. The course starts on Sunday, February 1st, and will meet every Sunday for eight weeks. When you're done, you'll have a dashboard you can share with colleagues, or just add to your personal portfolio. If you've taken Python courses, but want to sink your teeth into a real-world project, then HOPPy is for you. Among other things: Go beyond classroom learning: You'll learn by doing, creating your own personal product Live instruction: Our cohort will meet, live, for two hours every Sunday to discuss problems you've had and provide feedback. You decide what to do: This isn't a class in which the instructor dictates what you'll create. You can choose whatever data set you want. But I'll be there to support and advise you every step of the way. Learn about Marimo: Get experience with one of the hottest new Python technologies. Learn about modern distribution: Use Molab and WASM to share your dashboard with others Want to learn more? Join me for an info session on Monday, January 26th. You can register here: https://lnkd.in/dC9crid8 Ready to join right now? Get full details, and sign up, at https://lnkd.in/di6QpzXT

🐍 Master the Language: The Building Blocks of Python!📚 If you want to master Python, you have to speak its language. These 33 keywords are the reserved words that form the skeletal structure of every script, automation, and AI model you build. 💻🚀 🔍 Keyword Spotlight: The "in" Keyword The in keyword is one of Python's most versatile tools. It serves two main purposes: Membership Testing: It checks if a value exists within a sequence (like a list, string, or dictionary). Example: 'a' in 'apple' returns True. Iteration: It is used in for loops to iterate over an iterable object. Example: for item in list: 📂 Full Categorization: Logic & Truth: and, or, not, True, False, None Flow Control: if, elif, else, for, while, break, continue, pass Functions & Classes: def, return, lambda, class, yield Exception Handling: try, except, finally, raise, assert Structure & Scope: import, from, as, with, global, nonlocal, del, in, is 💡✨Pro-Tip for Beginners: You don't need to memorize these all at once! As you build projects, you’ll find yourself using if, for, and def 90% of the time. The others, like nonlocal or yield, are your "level-up" tools for more advanced logic. Which keyword gave you the most trouble when you first started learning? Let’s discuss in the comments! 👇 #PythonProgramming #CodingTips #DataScience #SoftwareEngineering #LearnToCode #PythonKeywords #TechEducation #Programming101

Python with Machine Learning — Chapter 10 📘 Topic: Python Class - Constructors 🔍 Hey there! 👋 Let’s talk about a special part of Python classes called *constructors*. They set up your objects when you first create them—like getting a new phone ready to use. Why does this matter? 🤔 Because starting objects with the right data helps your code work smoothly, especially in machine learning where every object needs correct values to make predictions. Now, let’s look at two types of constructors: 1. DEFAULT CONSTRUCTORS 🔄 - These have NO parameters (except self). - They set up basic values automatically. Example: [CODE] class Car: def __init__(self): self.color = "red" # default value print("Car created!") my_car = Car() print(f"My car is {my_car.color}") [/CODE] Here, every Car object starts as red—no extra info needed. 2. PARAMETERIZED CONSTRUCTORS 🎯 - These take parameters so you can give each object unique values. Example: [CODE] class Car: def __init__(self, color): self.color = color print(f"Car created: {self.color}") car1 = Car("blue") car2 = Car("green") [/CODE] Now each car can have its own color. Perfect for customizing objects! A QUICK NOTE: 📝 Unlike Java or C++, Python doesn’t allow “constructor overloading” (multiple versions of __init__). If you define more than one, only the LAST one works. So plan your parameters carefully! Remember, starting simple is okay. You’re doing great! 💪 Stay curious, Your coding mentor ✨

Day 1/6 Python Is Not Hard, You’re Just New Quick reminder for anyone learning Python for data analytics: Python isn’t hard. It just feels hard because it’s new, and new things always feel uncomfortable at first. At first, learning Python appears to be very simple. Like this: name = "Data Analyst" print(name) Here’s what’s happening: name is called a variable A variable is simply a name we assign to a piece of data so we can use it later "Data Analyst" is the value stored inside that variable print() is a built-in function that tells Python to display the result on the screen Nothing complex, just understanding how data is stored and shown. Now this: numbers = [10, 20, 30] print(sum(numbers)) What’s happening here: numbers is a list, meaning a collection of values sum() is another built-in function that adds all the values together print() shows us the final result This is data thinking. Small code. Simple logic. Real progress. You don’t start data analytics with dashboards or machine learning. You start by understanding how data is stored, calculated, and interpreted. If your code breaks sometimes, that’s okay. If you get errors, that’s normal. It means you’re learning. 👉 Follow me for Day 2 👉 Comment “I’m in” if you’re starting Python for data analytics Let’s keep it simple and consistent #Python #LearningPython #DataAnalytics #PythonForDataAnalysis #BeginnerInTech #LearningInPublic

Starting Python again felt… brand new✨ I went back to my Python learning, and honestly? It felt like the first time I ever opened the tutorial. I was hearing terms I swear I never heard the first time. But I guess that’s what returning to learning feels like you don’t just repeat it, you understand it differently. One thing I like currently about Python is the print() function. Simple, but powerful. It talks back to you. Literally. I also revisited the sources of functions, and it finally clicked: 📍Built-in functions — they come with Python straight out of the box (print(), input(), len()… the OGs) 📍Third-party functions — from external libraries like Pandas, NumPy, PySpark, etc. Created by someone else, but still very usable in your own code 📍User-defined functions— the ones I create That moment when you realize you can tell Python exactly what to do? Yeah… I smiled 😌 I even re-learnt escape sequences like: \" \ ' \n \t \b Small things, but they make your code speak clearly. Another beautiful thing I’m appreciating now: There’s more than one way to get the same result in Python. And the print() function? It’s not just for “Hello World.” It helps us: • Communicate with users • Display results • Debug and test our code • Understand what’s really happening behind the scenes Going back to learning actually feels good. Less pressure. More clarity. Deeper understanding. Sometimes restarting isn’t going backwards, it’s going stronger Back to learning. Again. And this time, it makes more sense. it's Day 7 of my consistency 30 days consistency challenge already 😊 #gwo_linkedin30dayschallenge #pythonlearning #dataanalytics #discipline #consistency

Python doesn’t have a “character” type. Even 'A' is a string. Same type as 'Hello'. Small detail — but it explains a lot of “why does this behave like that?” moments when you’re starting out. I put together a complete guide on Python literals: integers, floats, booleans, complex numbers, strings, underscores, scientific notation, and the rules that actually matter. Link in the first comment (or below). Read it here: https://lnkd.in/d8xvpV7h #Python #Coding #Programming

A realistic Python roadmap (that most people miss) Most of people don’t struggle with Python because the language is hard. They struggle because they don’t know what to learn next. This roadmap helped me see Python as a progressive skill, not a single topic to finish. You don’t start with frameworks or data science. You start with the basics syntax, variables, loops, and functions. This is where you understand how Python actually works. Then comes DSA. Not just for interviews, but to build problem solving and logical thinking ? After that, ! Python starts feeling practical through automation file handling, web scraping, and small scripts that save real time. OOP teaches structure. It helps you write cleaner, reusable, and scalable code. Only after this foundation do advanced concepts make sense decorators, generators, regex, and functional programming. From there, you can choose a direction: Web frameworks (Django, Flask, FastAPI) Testing (unit, integration, end-to-end) Data science " NumPy, Pandas, visualization, ML libraries " The biggest lesson here is simple. Python is not about learning everything fast. It’s about learning the right thing in the right order. Save this roadmap. Build layer by layer. That’s how confidence comes. #PythonRoadmap #PythonLearning #LearningInPublic #ProgrammingJourney #AspiringDataScientist #Consistency #BuildInPublic