Introduction to Scikit-learn in Python | Foundations for Machine Learning

Learn Scikit-learn from Scratch | Complete Beginner-Friendly ML Project Walkthrough with Iris Dataset Colab: https://colab.research.google.com/drive/1jYZP_x3s_uUtIAt8jEhTg_wvrVHxsfm5?usp=sharing In this lecture, we dive deep into Scikit-learn, one of the most popular machine learning libraries in Python. Whether you are just starting out or revisiting the basics, this video offers a complete and intuitive introduction to building machine learning models using Scikit-learn. We begin with a gentle overview of what machine learning is—contrasting it with traditional rule-based programming—and then walk through the different types of learning: supervised, unsupervised, reinforcement, and self-supervised learning. With that foundation, we focus on supervised learning and build a full machine learning pipeline using the classic Iris dataset. The Iris dataset is a simple yet powerful dataset that includes measurements of different types of iris flowers—Setosa, Versicolor, and Virginica. This dataset is perfect for beginners because it allows you to focus on the process and logic of ML without getting lost in data complexity. In this lecture, we cover: Loading and exploring the dataset Visualizing class distribution and feature relationships using Seaborn and Matplotlib Correlation heatmaps to understand feature importance Preprocessing: Train-test split and standard scaling Implementing two ML models from scratch: K-Nearest Neighbors (KNN) Decision Tree Classifier Understanding model performance using confusion matrices Interpreting results using precision, recall, accuracy, and F1-score Making sense of overfitting and underfitting using simple tweaks in model parameters Along the way, we discuss core ML concepts like training and testing splits, normalization, evaluation metrics, and more—all using Scikit-learn’s clean and readable API. This is not just a coding tutorial. It is an end-to-end walkthrough of how to think about machine learning as a beginner. By the end of this video, you will not only know how to build models in Scikit-learn but also how to reason about them and improve them through better data understanding and preprocessing. If you are a student, intern, or early-stage ML practitioner—this video will give you the hands-on foundation you need to start building real machine learning solutions. Tools Used: Python Scikit-learn Pandas Seaborn Matplotlib Google Colab Suggested Exercises: Try using other classifiers like Logistic Regression or SVM on the Iris dataset Modify the number of neighbors in KNN and depth of the Decision Tree to observe changes in accuracy Use a more complex dataset and apply the same pipeline If you enjoyed this video, don’t forget to like, share, and subscribe for more hands-on, intuitive, and practical AI/ML content. #MachineLearning #ScikitLearn #IrisDataset #PythonML #MLFromScratch #BeginnerMachineLearning #DataScience #KNN #DecisionTree #MLPipeline