Probability in Machine Learning with Python

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After a short break from posting, I spent some time strengthening my Machine Learning fundamentals by implementing core algorithms from scratch using Python and NumPy. So far I’ve implemented: • Linear Regression • Logistic Regression • Perceptron • K-Nearest Neighbors (KNN) • A simple Deep Neural Network Writing these algorithms from scratch helped me understand what actually happens behind libraries like scikit-learn and TensorFlow — things like gradient descent, weight updates, loss functions, and decision boundaries. It’s interesting to see how much clearer ML concepts become when you build them yourself instead of just importing a library. Links: Github: https://lnkd.in/gWEPa3v7 Next step: I’m planning to apply these implementations to small datasets and build mini projects around them. Always open to feedback and suggestions from the community. #MachineLearning #Python #AI #LearningInPublic #StudentDeveloper

🚀 A Roadmap to Machine Learning Using Python Machine Learning is transforming industries—from healthcare and finance to recommendation systems and scientific computing. However, many beginners find it difficult to understand where to start and how to progress. To make this journey clearer, I have written a short blog that outlines a step-by-step roadmap for learning Machine Learning using Python. The blog highlights key stages in the learning process: 🔹 Python programming fundamentals 🔹 Mathematical foundations for ML 🔹 Data analysis and visualization 🔹 Core machine learning algorithms 🔹 Model evaluation and optimization 🔹 Introduction to deep learning 🔹 Building real-world projects Following a structured roadmap can make the learning process more effective and less overwhelming for students and early researchers. I hope this guide will help beginners build a strong foundation in machine learning and Python-based data analysis. #MachineLearning #Python #ArtificialIntelligence #DataScience #DeepLearning #LearningRoadmap #Technology #Research #SRU #SRUMaths #SRUCSAI https://lnkd.in/ghMBAZrV

How do you explain Machine Learning to a 5-year-old? 🧠 You tell them to think about a math exam. · Training Data: The practice problems you solve at home (with the answers in the back of the book). · Model: Your brain, learning the method. · Testing: The final exam, where you see new problems you’ve never seen before. That’s it. That is Supervised Learning in a nutshell. Once you understand the concept, the code becomes much easier to understand. You stop fighting the "why" and can focus on the "how." It starts with simple analogies (like the one above) and transitions directly into a working Linear Regression model in Python. It includes: ✅ The "why" behind the code. ✅ The "what" (actual scikit-learn syntax). ✅ A plot so you can actually see the line of best fit. documented on the special request from Muhammad Junaid Jadoon #LearnToCode #ArtificialIntelligence #DataAnalytics #PythonProgramming #ML

🚀 Day 2 of My Artificial Intelligence Learning Journey Continuing my Python learning journey for AI and Machine Learning, today I explored some important data structures and concepts in Python. Here’s what I learned today: 🔹 Stacks and Queues – Understanding how data can be organized and processed using LIFO (Stack) and FIFO (Queue). 🔹 Queue Implementation – Practiced using Python’s queue module and collections.deque. 🔹 Lists – Learned how lists store collections of items and explored common methods like append(), insert(), remove(), and pop(). 🔹 Dictionaries – Key-value data structure used to store and access data efficiently. 🔹 Sets – Unordered collection of unique elements and useful methods like add(), remove(), and discard(). 📌 Key Takeaway: Understanding data structures in Python is essential because they help organize and process data efficiently—an important skill for building AI and machine learning models. Excited to continue learning and building a strong foundation in Python for AI. #Python #ArtificialIntelligence #MachineLearning #DataStructures #LearningInPublic #AIJourney

🌸 Practice ML Project on Iris Dataset Recently, I practiced a Machine Learning classification project using the famous Iris dataset. 🔹 Performed data preprocessing 🔹 Handled missing values 🔹 Applied feature scaling 🔹 Trained classification model 🔹 Evaluated model accuracy This project helped me strengthen my understanding of supervised learning and model evaluation techniques. Tools & Libraries: #Python #Pandas #ScikitLearn #MachineLearning #DataScience

inskilld's Python + Machine Learning for Engineering Students. 8 modules: Python foundations, data handling/visualization, math essentials, ML fundamentals, supervised/unsupervised algorithms (Linear/Logistic Regression, KNN, Decision Trees, Random Forest, K-Means, Hierarchical Clustering, PCA), model optimization/deployment. Build Netflix-style recommenders. Beginner-friendly, real datasets, industry pro instructor. Enroll: https://lnkd.in/gu9i3Rwu Referral: [YOUR CODE] = ₹2000 voucher (₹500 off + ₹250 per 6 friends). #Python #MachineLearning #AI #DataScience #inskilld

A comprehensive machine learning roadmap involves mastering Python, essential mathematics (linear algebra, calculus, probability), data analysis, and core algorithms (supervised/unsupervised) using libraries like Scikit-Learn, NumPy, and Pandas. Advanced steps include deep learning with TensorFlow/PyTorch, model deployment, and portfolio building via Kaggle or GitHub

AI is not magic* Python is powerful because it’s simple. Grateful for learning coding the right way — fundamentals first, AI second. It’s about problem-solving discipline. I learned how to combine core Python fundamentals with AI tools like Google Colab and Replit to accelerate execution. What truly mattered: • Python is case-sensitive → print() works, Print() fails. • input() always returns a string → Type casting is non-negotiable. • = assigns. == compares. • range(1,10) excludes 10. • Every loop must have an exit strategy. Python Using AI | Be10X Learning #Be10x #skillbuilding

Your Machine Learning model is 95% accurate. But it might still be a bad model. When I first started learning Machine Learning as a Computer Science student, I thought: Higher accuracy = Better model. But I was wrong. While working with Scikit-learn in Python, I realized something important: Accuracy alone doesn’t tell the full story. A model can: • Overfit the training data • Underperform on new data • Fail in imbalanced datasets That’s when I started understanding: ✔ Precision & Recall ✔ F1-Score ✔ Confusion Matrix ✔ Bias vs Variance ✔ Proper validation techniques Machine Learning is not about getting one big number. It’s about: → Generalization → Data quality → Strong fundamentals → Continuous experimentation Still learning. Still building. 🚀 What metric do you focus on most when evaluating a model? #MachineLearning #ArtificialIntelligence #DataScience #Python #AIStudent #ComputerScience #TechGrowth #LearningJourney