Machine Learning
Learn core machine learning concepts and how to apply them with Scikit-Learn, from data preprocessing to clustering and boosting.
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Now playing: Classification in Machine Learning | Machine Learning with Scikit Learn | Intellipaat
This path walks through the Scikit-Learn library and the main algorithms it supports. It starts with what Scikit-Learn is and how a typical workflow looks, then covers preprocessing, classification, regression, clustering, and ensemble methods. Each lesson includes theory and, in most cases, a hands-on demo. The videos come from Intellipaat's Scikit-Learn tutorial playlist and are best followed in order, though you can jump to specific algorithms if you already know the basics.
Beginners who want a structured free introduction before deeper practice.
Lesson 1: What Is Scikit-Learn | Introduction To Scikit-Learn | Machine Learning Tutorial | Intellipaat
Introduces Scikit-Learn and explains why it is a popular library for machine learning in Python. Covers its importance and the types of problems it can solve.
Open on YouTubeLesson 2: Scikit Learn Tutorial | Scikit-Learn Workflow | Data Preprocessing In Machine Learning | Intellipaat
Explains data preprocessing in machine learning and shows how to do it with Scikit-Learn. The session builds programming intuition for Scikit-Learn algorithms and covers both theory and implementation.
Open on YouTubeLesson 3: Classification in Machine Learning | Machine Learning with Scikit Learn | Intellipaat
Covers classification in machine learning, including key terminology and Scikit-Learn classification algorithms. Provides a foundation before diving into specific classifiers.
Open on YouTubeLesson 4: KNN Algorithm in Machine Learning | K Nearest Neighbor | Scikit Learn Tutorial | Intellipaat
Explains the K-Nearest Neighbor (KNN) algorithm, how it works, and how to implement it with Scikit-Learn. Part of the Scikit-Learn tutorial series.
Open on YouTubeLesson 5: SVM Algorithm in Machine Learning | Support Vector Machine | Scikit Learn Tutorial | Intellipaat
Introduces the Support Vector Machine (SVM) algorithm for classification and regression. Includes a hands-on demo of SVM in Scikit-Learn.
Open on YouTubeLesson 6: Linear Regression Algorithm | Linear Regression in Scikit Learn | Intellipaat
Covers linear regression in machine learning, including features, labels, and the math behind it. Shows how linear regression is used in Scikit-Learn.
Open on YouTubeLesson 7: Logistic Regression in Machine Learning | Logistic Regression Demo | Sklearn Tutorial | Intellipaat
Explains logistic regression in machine learning, including the math behind it. Includes a demo of logistic regression using Scikit-Learn.
Open on YouTubeLesson 8: K Means Algorithm in Machine Learning | K Means Clustering | Scikit Learn Tutorial | Intellipaat
Introduces the K-Means algorithm and clustering concepts. Covers how K-Means works and includes a demo.
Open on YouTubeLesson 9: What is Gaussian Naive Bayes Algorithm | Gaussian Naive Bayes Implementation | Intellipaat
Explores the Gaussian Naive Bayes algorithm, a variant of the Naive Bayes classifier. Covers its implementation.
Open on YouTubeLesson 10: K Means Clustering Algorithm | K Means Clustering Python | Machine Learning Tutorial | Intellipaat
Covers K-Means clustering with a Python implementation. Part of the machine learning tutorial series.
Open on YouTubeLesson 11: Mean Shift Clustering | How Mean Shift Clustering Works | Scikit Learn Tutorial | Intellipaat
Explains Mean Shift clustering and how it works. Part of the Scikit-Learn tutorial series.
Open on YouTubeLesson 12: DBSCAN Clustering Algorithm | Density Based Clustering | DBSCAN Implementation | Intellipaat
Covers the DBSCAN clustering algorithm, a density-based method. Includes implementation details.
Open on YouTubeLesson 13: Decision Tree Machine Learning | Decision Tree Algorithm | Machine Learning | Intellipaat
Dives into decision trees, one of the most popular algorithms in machine learning. Covers how decision trees work and how to use them.
Open on YouTubeLesson 14: Random Forest Explained | Random Forest Algorithm in Machine Learning | Data Science | Intellipaat
Explains the Random Forest algorithm, an ensemble method used in machine learning and data science. Covers its concepts and application.
Open on YouTubeLesson 15: AdaBoost Algorithm In Machine Learning - Theory | AdaBoost Step-By-Step Explanation | Intellipaat
Covers the theory behind the AdaBoost algorithm, a boosting technique in machine learning. Provides a step-by-step explanation.
Open on YouTubeLesson 16: AdaBoost Algorithm Python Implementation | AdaBoost Python Tutorial | Machine Learning | Intellipaat
Hands-on implementation of the AdaBoost algorithm in Python. Follows the theory video with practical coding.
Open on YouTubeLesson 17: Gradient Boosting Machine Learning | Gradient Boosting for Regression Explained | Intellipaat
Explains Gradient Boosting for regression. Covers the concepts behind this ensemble technique.
Open on YouTubeLesson 18: Ensemble Learning in Machine Learning | Ensemble Learning Tutorial | Machine Learning | Intellipaat
Introduces ensemble learning in machine learning. Covers the basic idea of combining models.
Open on YouTubeLesson 19: GridSearchCV | Grid Search - Hyper Parameter Tuning | Scikit Learn Tutorial | Intellipaat
Focuses on hyperparameter tuning using GridSearchCV in Scikit-Learn. Explains how grid search works and how to use it.
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