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Udemy - Data Science Methods and Techniques 2025 2025-8
- Enrolled students: 1
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Course content
Sections:
4
•
Activities:
1
•
Resources:
31
- Announcements
- 1 Introduction
- 2 Setup of the Anaconda Cloud Notebook
- 3 Download and installation of the Anaconda Distribution optional
- 4 The Conda Package Management System optional
- 1 Regression Prediction and Supervised Learning Section Overview I
- 2 The Traditional Simple Regression Model II
- 3 The Traditional Simple Regression Model III
- 4 Some practical and useful modelling concepts IV
- 5 Some practical and useful modelling concepts V
- 6 Linear Multiple Regression model VI
- 7 Linear Multiple Regression model VII
- 8 Multivariate Polynomial Multiple Regression models VIII
- 9 Multivariate Polynomial Multiple Regression models VIIII
- 10 Regression Regularization Lasso and Ridge models X
- 11 Decision Tree Regression models XI
- 12 Random Forest Regression XII
- 13 Voting Regression XIII
- 1 Classification and Supervised Learning overview
- 2 Logistic Regression Classifier
- 3 The Naive Bayes Classifier
- 4 K Nearest Neighbor Classifier KNN Extra Video
- 5 The Decision Tree Classifier
- 6 The Random Forest Classifier
- 7 Linear Discriminant Analysis LDA Extra Video
- 8 The Voting Classifier
- 1 Overview
- 2 K Means Cluster Analysis
- 3 Auto updated K Means Cluster Analysis introduction and simulation
- 4 Density Based Spatial Clustering of Applications with Noise DBSCAN
- 5 Four Hierarchical Clustering algorithms
- 6 Principal Component Analysis PCA