Видео 78
Просмотров 3 247

Principal Component Analysis: Advanced Factor Analysis Techniques

5:03

Understanding Factor Analysis in Data Mining

4:47

Understanding Hierarchical Clustering

4:19

Detecting Outliers Using DBSCAN

4:48

Understanding K-means: A Deep Dive into Centroid-Based Clustering

4:48

Discovering Patterns: An Introduction to Unsupervised Learning

5:28

Exploring Travel Review Data: An Unsupervised Learning Case Study

Learn data mining and unsupervised learning techniques through a practical case study analyzing travel review data! This tutorial covers Principal Component Analysis (PCA), K-means clustering, and outlier detection using DBSCAN, all implemented in Python with scikit-learn.
In this comprehensive tutorial, you'll discover:
- How to choose the right unsupervised learning technique for your data
- Methods for exploring patterns using clustering algorithms
- Techniques for identifying anomalies through outlier detection
- Ways to reduce dimensionality with PCA
- Practical implementation using scikit-learn
Perfect for data science students, aspiring machine learning engineers, and anyone interested in...

Видео

Principal Component Analysis: Advanced Factor Analysis Techniques

5:03

Principal Component Analysis: Advanced Factor Analysis Techniques

4 часа назад

Discover Principal Component Analysis (PCA) fundamentals and implementation in Python! This comprehensive tutorial covers advanced factor analysis techniques, eigenvalue interpretation, and practical PCA implementation using scikit-learn. Perfect for data scientists and analysts looking to master dimensionality reduction techniques. Key topics covered: - PCA methodology and fundamentals - Compo...

Understanding Factor Analysis in Data Mining

4:47

Understanding Factor Analysis in Data Mining

Просмотров 54 часа назад

🔍 Factor Analysis | Data Mining | Machine Learning | Statistical Analysis | Feature Reduction | Data Science Dive into the world of factor analysis in this comprehensive tutorial! Learn how this powerful statistical method can help you reduce data dimensionality and uncover hidden patterns in your datasets. Perfect for data science students, analysts, and professionals looking to enhance their ...

4:19

Understanding Hierarchical Clustering

Просмотров 134 часа назад

Explore hierarchical clustering in this in-depth tutorial! Learn about agglomerative and divisive clustering, understand how to measure cluster similarity with single linkage, complete linkage, and centroid linkage. Dive into dendrograms for visualizing clustering processes and discover how to determine the optimal number of clusters. Get hands-on with Python using SciPy for practical implement...

4:48

Detecting Outliers Using DBSCAN

4 часа назад

Learn how to use DBSCAN, a powerful density-based clustering algorithm, to detect outliers and analyze data with irregularly shaped clusters. In this video, we compare DBSCAN with k-means clustering, explore core points, border points, and outliers, and demonstrate how to implement DBSCAN using Python's scikit-learn library. Perfect for data science and machine learning enthusiasts looking to e...

Understanding K-means: A Deep Dive into Centroid-Based Clustering

4:48

Understanding K-means: A Deep Dive into Centroid-Based Clustering

Просмотров 14 часа назад

Dive into the world of k-means clustering with this comprehensive guide. Learn about centroid-based clustering, euclidean distance, and how to implement machine learning algorithms using Python and scikit-learn. This video covers data science essentials like cluster analysis, WCSS, and the elbow method for optimizing clusters. Discover how to analyze real-world data like the Old Faithful datase...

Discovering Patterns: An Introduction to Unsupervised Learning

5:28

Discovering Patterns: An Introduction to Unsupervised Learning

Просмотров 24 часа назад

Learn unsupervised learning, clustering algorithms, and data mining techniques in this comprehensive tutorial. Discover how machine learning can uncover hidden patterns in your data without prior knowledge. Perfect for data science students and professionals looking to understand customer segmentation, outlier detection, and latent variable models. In this video, we cover: • The fundamentals of...

Case Study: Classifying Cells Using Machine Learning

4:36

Case Study: Classifying Cells Using Machine Learning

Просмотров 79 часов назад

Discover how machine learning techniques like k-nearest neighbors (kNN) and support vector machines (SVM) are used in real-world applications, such as breast cancer diagnosis. In this video, we explore decision boundaries, hyperparameters, and dataset evaluation using Python's scikit-learn library. Learn how to classify cells as benign or malignant with practical examples from the Wisconsin Bre...

3:33

Introduction to Support Vector Machines

Просмотров 19 часов назад

Delve into the world of support vector machines (SVM) and understand how they revolutionize data mining and classification with hyperplanes. This video explains the concept of margin maximization and introduces you to kernel functions like linear, polynomial, and RBF kernels for handling non-linear data. Learn how to implement SVMs in Python using scikit-learn with practical examples. Ideal for...

Naive Bayes Classification: Concepts and Applications

4:58

Naive Bayes Classification: Concepts and Applications

Просмотров 29 часов назад

Learn about Naive Bayes classification, a powerful supervised learning algorithm based on Bayes' theorem. This video explains how Naive Bayes works, its applications in text classification, the importance of Laplace smoothing, and its advantages and limitations. You’ll also learn how to implement Naive Bayes in Python using scikit-learn, with a practical example to get you started. Perfect for ...

k-Nearest Neighbors: Classification and Regression

4:09

k-Nearest Neighbors: Classification and Regression

Просмотров 139 часов назад

Explore the power of k-nearest neighbors (kNN) in this deep dive into machine learning techniques for both classification and regression. Learn how to calculate distances with metrics like Euclidean distance, and understand when to use kNN for predictive modeling. We implement kNN using Python with scikit-learn, showing you how to select the optimal value of k and apply cross-validation. Perfec...

4:32

Introduction to Supervised Learning

Просмотров 312 часов назад

Discover the essentials of supervised learning in this educational video. Learn about machine learning concepts like regression and classification, and understand the difference between interpretable and predictive models. We'll cover real-world applications such as predicting house prices, classifying spam emails, and medical diagnosis. Dive into how input features and output features work, an...

3:40

Application: Predicting Home Prices

12 часов назад

Learn how to predict home prices using regression models in this data science tutorial! This video explores simple linear regression, multiple regression, and how to evaluate models using metrics like MSE, R-squared, and cross-validation. Using Python and scikit-learn, we’ll guide you through implementing these models and comparing their performance. Perfect for anyone interested in data mining...

Model Selection and Evaluation in Data Mining

3:53

Model Selection and Evaluation in Data Mining

Просмотров 212 часов назад

Explore model selection in data mining with this comprehensive guide! Learn how to avoid overfitting and underfitting using cross-validation techniques and statistical measures like MSE, adjusted R-squared, AIC, and BIC. This video demonstrates model evaluation in Python, specifically how to select the best linear regression model with polynomial features using sklearn. Perfect for data scienti...

Introduction to the Bootstrap Method and Model Evaluation

3:44

Introduction to the Bootstrap Method and Model Evaluation

12 часов назад

Dive into the world of bootstrap method and model evaluation with this informative video! Learn how to apply resampling techniques in statistics using Python for data analysis. Discover how bootstrap sampling can enhance your machine learning models, especially when dealing with small datasets. This video covers confidence intervals, variability estimation, and error distribution using practica...