Visual guide to scikit-learn functions and classes

#Day29 of #100DaysOfCode Today I learned about the Bias–Variance Tradeoff in Machine Learning. High Bias → Underfitting (model too simple) High Variance → Overfitting (model too complex) The goal is to find the right balance for best accuracy ✅ Understanding this tradeoff helps in building models that generalize well on unseen data. #MachineLearning #DataScience #AI #Python #LearningJourney #100DaysOfCode

Day 45 of #100DaysOfML Random Forest Implementation 🌲 Concept Recap: Random Forest is an ensemble of Decision Trees trained using bagging — each tree learns from a random subset of data and features. The final output is decided by majority voting (classification) or averaging (regression). It improves accuracy and reduces overfitting compared to a single Decision Tree #100DaysOfML #MachineLearning #RandomForest #DecisionTree #EnsembleLearning #DataScience #Python #MLAlgorithms #FeatureImportance #AI #MLProject #DataVisualization #LearnMachineLearning #MLJourney #TechLearning

📶 Experiment 12: Random Forest Algorithm using Python 🤖 In this lab, I explored the Random Forest Algorithm, a powerful ensemble learning technique that builds multiple decision trees and combines their outputs for more accurate and stable predictions. 🔍 Key learning outcomes: • Understanding the concept of bagging and ensemble averaging • Implementing Random Forest using scikit-learn • Evaluating model performance using metrics like accuracy and feature importance • Learning how Random Forest reduces overfitting and improves generalization • Visualizing feature contributions to model decisions This experiment strengthened my grasp on how ensemble models enhance predictive power and reliability, making Random Forests a go-to choice for many real-world machine learning tasks. 📁 Explore the repository here : 👉 https://lnkd.in/epWys7e7 #DataScience  #MachineLearning  #Python  #ScikitLearn  #EnsembleLearning  #PredictiveModeling  #DataAnalysis  #AI  #LearningJourney  #JupyterNotebook Ashish Sawant sir

Excited to share our recent project on Random Forest – Machine Learning 🌳🤖 We explored how ensemble learning can boost accuracy, reduce overfitting, and provide better feature insights using Python (scikit-learn, TensorFlow). A great hands-on experience in building and optimizing ML models with an amazing team! 🚀 #MachineLearning #RandomForest #AI #Python #DataScience #Innovation #Tech

When I started my ML journey, I rushed into TensorFlow and PyTorch... But soon realized that without strong Python fundamentals, everything feels like a black box. 🧠 Here are the Top 3 Python libraries every ML beginner should master before diving into deep learning. These are the same libraries that helped me understand how models really work. 💭 Comment what’s the first library you learned as an ML beginner? 🔁 Save this post for later — it’s your quick roadmap to mastering the basics. #MachineLearning #Python #AI #DataScience #LearningJourney #CareerGrowth #MLBeginners

Want to code Logistic Regression from scratch without relying on libraries? In my latest video, I break down the math, derive the gradient descent update rules, and implement the entire model step by step in Python. Perfect for anyone looking to understand the core logic behind ML algorithms or preparing for interviews. Video Link: youtu.be/cT_U40djaww   Channel Link: youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

Taught how to build AI Agents and Agentic AI using the Crew AI Framework! In this YouTube session, I explained how to create Agentic AI systems using Python + Crew AI, with practical implementation and real-world use cases. 📺 : https://lnkd.in/g76Vh2gT #AIwithThiru #CrewAI #AIAgents #AgenticAI #Python #GenerativeAI #YouTubeLearning

Fake News Detection using Machine Learning  I built a Fake News Detection model that classifies articles as Real or Fake using Python ,Scikit-learn and TF-IDF Vectorizer.   – Data preprocessing & feature extraction using TF-IDF   – Logistic Regression for classification   – Achieved ~95 % accuracy on test data   – Implemented in Google Colab and uploaded on GitHub   Project Link: [https://lnkd.in/gEqUfWfc)   #MachineLearning #AI #Python #DataScience #FakeNewsDetection #MLProjects #GitHub

🚀 Built Multiple Linear Regression from Scratch using NumPy! Implemented the model both with and without Gradient Descent, without using any ML libraries — just pure math, NumPy, and logic 🧠💻 This project helped me deeply understand how linear regression works under the hood, from matrix operations to optimizing weights using gradient descent. #MachineLearning #LinearRegression #NumPy #Python #DataScience #FromScratch #AI