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Machine Learning

Machine Learning with Scikit-Learn: A Beginner's Path

Learn core machine learning concepts and how to apply them with Scikit-Learn, from data preprocessing to clustering and boosting.

Learn from: the original creator. Original content published on YouTube. DigitalSkillX organizes these public resources into a structured learning path. DigitalSkillX does not claim ownership or partnership.

Lessons
19
Time
About 10 hr 44 min
Level
beginner

Now playing: Classification in Machine Learning | Machine Learning with Scikit Learn | Intellipaat

About this path

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.

What you will learn

  • Explain what Scikit-Learn is and why it is widely used for machine learning in Python
  • Prepare data for machine learning using Scikit-Learn's preprocessing tools
  • Apply classification algorithms such as KNN, SVM, and Naive Bayes
  • Build regression models with linear and logistic regression
  • Use clustering methods including K-Means, Mean Shift, and DBSCAN
  • Understand ensemble techniques like Random Forest, AdaBoost, and Gradient Boosting
  • Tune hyperparameters with GridSearchCV

Who this is for

Beginners who want a structured free introduction before deeper practice.

Curriculum

Section 1: Getting Started with Scikit-Learn

  1. 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 YouTube
  2. Lesson 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.

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Section 2: Classification Algorithms

  1. Lesson 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.

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  2. Lesson 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 YouTube
  3. Lesson 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.

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Section 3: Regression Algorithms

  1. Lesson 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 YouTube
  2. Lesson 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.

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Section 4: Clustering Algorithms

  1. Lesson 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 YouTube
  2. Lesson 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 YouTube
  3. Lesson 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 YouTube
  4. Lesson 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 YouTube
  5. Lesson 12: DBSCAN Clustering Algorithm | Density Based Clustering | DBSCAN Implementation | Intellipaat

    Covers the DBSCAN clustering algorithm, a density-based method. Includes implementation details.

    Open on YouTube

Section 5: Tree-Based and Ensemble Methods

  1. Lesson 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.

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  2. Lesson 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 YouTube
  3. Lesson 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.

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  4. Lesson 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.

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  5. Lesson 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 YouTube
  6. Lesson 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 YouTube

Section 6: Hyperparameter Tuning

  1. Lesson 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.

    Open on YouTube

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