Linear Regression Model with Python and Scikit-learn

Exploring one of the fundamental concepts in Machine Learning — Linear Regression . Currently trying to understand how data can be used to predict outcomes and identify relationships between variables.   What seems like a simple concept actually plays a crucial role in building intelligent systems.   Interesting to see how models learn from data and improve over time.   What ML concept are you currently exploring?  #AIML #LearningInPublic #Python #DataScience #Consistency

🚀 Learning Update: Python (Week Progress) Continuing my Python journey as part of my path toward AI, Machine Learning, and Data Science. This week, I focused on understanding some important concepts: • Lambda Functions • Nested Functions • Class Methods (like str, len) • Basics of Polymorphism (Function Overloading concept) --- 💡 What made the difference this time: Instead of just learning theory, I focused on small practical implementations. For example: → Using lambda for quick one-line operations → Understanding how nested functions control scope → Customizing class behavior using built-in methods → Exploring how polymorphism changes function behavior --- 🧠 The key realization: Concepts make more sense when applied — even in small examples. --- 🔥 Step by step, building the foundation. More practical learning updates coming soon. --- 💬 What concept helped you understand Python better? comment ✍️ #Python #LearningJourney #AI #MachineLearning #DataScience #Programming #BuildInPublic #DeveloperJourney #TechLearning #Consistency

Built a Machine Learning obesity prediction app in Python and Flask, reaching 76.6% accuracy. This project was a strong exercise in model building, evaluation, and deploying ML in a practical application. Repo: https://lnkd.in/dnq6kirn #MachineLearning #Python #Flask #DataScience #AI #ModelDeployment #HealthTech

Machine learning sounds intimidating. It really isn't. Here's how I like to think about it — You know how you get better at spotting bad fruit at the grocery store over time? You've seen enough bad bananas to just... know. ML models do the same thing. You show them thousands of examples, they learn the pattern, and then they start making their own calls. That's it. That's the magic. What part of ML have you always found confusing? Drop it below #MachineLearning #DataAnalytics #Python #DataScience #MLforBeginners

Most people jump straight into GenAI tools without the foundations. They guess at prompts. They can't debug outputs. They can't improve anything. The **AI Fundamentals** Bundle changes that. 4 courses. Built in sequence. Designed to make you a contributor — not just a user — of GenAI applications. 🐍 Course 1 — Python Essentials for Data Science / ML The operating language of AI. Core Python, NumPy, Pandas, visualization, and an intro to scikit-learn and deep learning libraries. #AIFundamentals #GenAI #MachineLearning #DataScience #Python #LearningAndDevelopment #Upskilling #Grokkers

To make a decision tree, all data has to be numerical. We have to convert the non numerical columns 'Nationality' and 'Go' into numerical values. Pandas has a map() method that takes a dictionary with information on how to convert the values. {'UK': 0, 'USA': 1, 'N': 2} Means convert the values 'UK' to 0, 'USA' to 1, and 'N' to 2. #MachineLearning #DataScience #Python #ArtificialIntelligence #AI #ScikitLearn #DataAnalysis #ML

Day 7 of becoming an AI/ML Engineer 💻 Today’s topic: Dictionaries, methods, and functions in Python Learned how to store and access data using key–value pairs. Building strong fundamentals every day! #Python #AI #ML #LearningInPublic #StudentJourney

Want to build your first machine learning model? Start with Scikit-learn. 🤖 Scikit-learn is the most beginner-friendly and widely used machine learning library in Python — and for good reason. Here is what makes it special: 1️⃣ Clean, consistent API that is easy to learn 2️⃣ Covers everything from regression to clustering to classification 3️⃣ Used by data scientists at companies of every size worldwide I am currently working with Scikit-learn as part of my Data Science and analytics studies and it has made machine learning feel genuinely accessible.      #ScikitLearn #MachineLearning #Python #DataScience #AI #Analytics #Tech

🚀 Day 15 of My Generative & Agentic AI Journey! Today’s focus was on understanding Variable Scope in Python — how the same variable name can behave differently depending on where it is defined. Here’s what I learned: 🌍 Global vs Local Scope: • Variables defined outside a function are global • Variables inside a function are local 👉 Even if the variable name is the same (like student_name), the one inside the function is completely different from the one outside. 🔁 Nested Function Scope: • Functions can be defined inside other functions • Inner functions can have their own variables, even with the same name 👉 Example use case: A student_name defined in the outer function can be different from the one inside the inner function, and both don’t affect each other. 💡 Key takeaway: Scope controls where a variable can be accessed — understanding this avoids confusion and helps write bug-free code. Going deeper into how Python handles variables behind the scenes 🚀 #Day15 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

PyCaret is a low-code Python library that makes machine learning much faster and easier. With just a few lines of code, you can handle preprocessing, compare models, and tune performance in a single workflow. It supports tasks like classification, regression, clustering, and time-series analysis, making it a practical choice for many real-world projects. The book Simplifying Machine Learning with PyCaret by Giannis Tolios is currently available for free: https://lnkd.in/eVFjfGKQ The book guides you step by step through typical PyCaret use cases, from setting up experiments to building, evaluating, and deploying models. It includes practical examples and clear explanations to help you apply PyCaret effectively in real projects. If you want a structured and hands-on introduction to PyCaret, this is a great resource. #machinelearning #python #datascience #ai #pycaret #lowcode #mlworkflow #datatools #analytics #statistics