Machine Learning
A free learning path that walks you through Scikit-learn, from linear models to data preprocessing, using a practical YouTube playlist.
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.
Now playing: 02. Linear Regression in Scikit-learn | Machine Learning With Scikit-Learn | Sklearn | Codersarts
This learning path is built from a YouTube playlist by CodersArts. It starts with an introduction to Scikit-learn, then covers key machine learning algorithms like linear regression, logistic regression, K-nearest neighbors, decision trees, and random forests. After that, it moves into essential data preprocessing techniques, including handling missing data, categorical and numerical values, scaling, normalization, and polynomial features. Each lesson includes code examples and practical explanations, so you can follow along and build your skills step by step.
Beginners who want a structured free introduction before deeper practice.
Lesson 1: 01. Introduction to Scikit-learn | Machine Learning With Scikit-Learn | Sklearn | Codersarts
This introductory video covers the prerequisites, features, and uses of the Scikit-learn library. It sets the foundation for the rest of the course.
Open on YouTubeLesson 2: 02. Linear Regression in Scikit-learn | Machine Learning With Scikit-Learn | Sklearn | Codersarts
This video dives into linear regression using Scikit-learn. You will learn how to implement a simple linear regression model and understand the key concepts behind it.
Open on YouTubeLesson 3: 03. Multiple Linear Regression in Scikit-learn | Machine Learning With Scikit-Learn
This video covers multiple linear regression, extending simple linear regression to multiple features. You will see how to build and evaluate a multiple linear regression model.
Open on YouTubeLesson 4: 04. Logistic Regression Using Scikit-learn | Machine Learning With Scikit-Learn
This video explains logistic regression for classification tasks. You will learn the theory and see code examples using Scikit-learn.
Open on YouTubeLesson 5: 05. K-Nearest Neighbor(KNN) Algorithm Using Scikit-learn | Machine Learning With Scikit-Learn
This video discusses the K-Nearest Neighbors (KNN) algorithm and its implementation in Scikit-learn. It also highlights the importance of preprocessing and improving model performance.
Open on YouTubeLesson 6: 06. Decision Tree in Scikit-learn | Machine Learning With Scikit-Learn | Sklearn | Codersarts
This video covers decision trees and how to implement them using Scikit-learn. You will see how a decision tree is formed and how to use it for classification or regression.
Open on YouTubeLesson 7: 07. Random Forest in Scikit-learn | Machine Learning With Scikit-Learn | Sklearn | Codersarts
This video discusses the random forest algorithm and its implementation in Scikit-learn. It includes plotting a confusion matrix and evaluating accuracy.
Open on YouTubeLesson 8: 08. Dealing with Missing Data in Scikit-Learn - sklearn.preprocessing | Scikit-learn Tutorial
This video focuses on handling missing data using the sklearn.preprocessing package, specifically the Imputer class. You will learn how to clean your data for machine learning.
Open on YouTubeLesson 9: 09. Categorical Data Preprocessing with Scikit-Learn - sklearn.preprocessing | Scikit-learn Tutorial
This video covers categorical data preprocessing. You will learn about different encoding techniques to convert categorical values into a format suitable for machine learning.
Open on YouTubeLesson 10: 10. Handling Numerical Values - sklearn.preprocessing | Scikit-learn Tutorial
This video discusses handling numerical values using discretization or binarization. Sample code is provided for these preprocessing techniques.
Open on YouTubeLesson 11: 11. Standardization and Scalers - sklearn.preprocessing | Scikit-learn Tutorial
This video explains feature scaling using sklearn.preprocessing. You will learn what standardization is and the different scalers available in Scikit-learn.
Open on YouTubeLesson 12: 12. Normalization and Norms - sklearn.preprocessing | Scikit-learn Tutorial l Codersarts
This video covers normalization and the available norms in Scikit-learn. You will see sample code for each norm and an alternative approach.
Open on YouTubeLesson 13: 13. Polynomial Features and Custom Transformers - sklearn.preprocessing | Scikit-learn Tutorial
This video discusses polynomial features and custom transformers in sklearn.preprocessing. You will see how polynomial interpolation works along with code examples.
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