| 1. Conclusion.mp4 | 46.3 MB | ||
| 1. Conclusion.srt | 2.9 KB | ||
| 1. Introduction to Clustering.mp4 | 57.8 MB | ||
| 1. Introduction to Clustering.srt | 3.1 KB | ||
| 1. Setting up the Environment.mp4 | 46.4 MB | ||
| 1. Setting up the Environment.srt | 3.1 KB | ||
| 1. Understanding the Problem Statement.mp4 | 53.5 MB | ||
| 1. Understanding the Problem Statement.srt | 3 KB | ||
| 1. Why High Dimensional Datasets are a Problem.mp4 | 79.3 MB | ||
| 1. Why High Dimensional Datasets are a Problem.srt | 4.3 KB | ||
| 10. Introduction the Boruta Algorithm.mp4 | 52.5 MB | ||
| 10. Introduction the Boruta Algorithm.srt | 3 KB | ||
| 10. Introduction to Hierarchical Clustering.mp4 | 88.4 MB | ||
| 10. Introduction to Hierarchical Clustering.srt | 4.8 KB | ||
| 10. Summarizing the Key-Points.mp4 | 40.5 MB | ||
| 10. Summarizing the Key-Points.srt | 2.3 KB | ||
| 11. Implementing the Boruta Algorithm.mp4 | 43.2 MB | ||
| 11. Implementing the Boruta Algorithm.srt | 4.5 KB | ||
| 11. Introduction to Dendrograms.mp4 | 41.8 MB | ||
| 11. Introduction to Dendrograms.srt | 3.9 KB | ||
| 12. Implementing Hierarchical Clustering.mp4 | 52.4 MB | ||
| 12. Implementing Hierarchical Clustering.srt | 3.6 KB | ||
| 12. Introduction to Principal Component Analysis.mp4 | 73.7 MB | ||
| 12. Introduction to Principal Component Analysis.srt | 4.1 KB | ||
| 13. Implementing PCA.mp4 | 55.5 MB | ||
| 13. Implementing PCA.srt | 4.3 KB | ||
| 13. Introduction to DBSCAN Clustering.mp4 | 75.6 MB | ||
| 13. Introduction to DBSCAN Clustering.srt | 4.5 KB | ||
| 14. Implementing DBSCAN Clustering.mp4 | 47.8 MB | ||
| 14. Implementing DBSCAN Clustering.srt | 3.6 KB | ||
| 14. Introduction to t-SNE.mp4 | 81.2 MB | ||
| 14. Introduction to t-SNE.srt | 4.5 KB | ||
| 15. Implementing t-SNE.mp4 | 36.1 MB | ||
| 15. Implementing t-SNE.srt | 2.3 KB | ||
| 16. Introduction to Linear Discriminant Analysis.mp4 | 48.8 MB | ||
| 16. Introduction to Linear Discriminant Analysis.srt | 2.7 KB | ||
| 17. Implementing LDA.mp4 | 36.7 MB | ||
| 17. Implementing LDA.srt | 2.7 KB | ||
| 18. Difference between PCA, t-SNE, and LDA.mp4 | 64.8 MB | ||
| 18. Difference between PCA, t-SNE, and LDA.srt | 3.4 KB | ||
| 2. Methods to solve the problem of High Dimensionality.mp4 | 57.1 MB | ||
| 2. Methods to solve the problem of High Dimensionality.srt | 3.3 KB | ||
| 2. Setting up the Environment.mp4 | 28.8 MB | ||
| 2. Setting up the Environment.srt | 2.1 KB | ||
| 2. Types of Clustering.mp4 | 65.3 MB | ||
| 2. Types of Clustering.srt | 3.9 KB | ||
| 2. Understanding the Dataset.mp4 | 55.1 MB | ||
| 2. Understanding the Dataset.srt | 3.2 KB | ||
| 3. Applications of Clustering.mp4 | 56 MB | ||
| 3. Applications of Clustering.srt | 3.3 KB | ||
| 3. Data Analysis and Visualization.mp4 | 77.7 MB | ||
| 3. Data Analysis and Visualization.srt | 17.3 KB | ||
| 3. Solving a Real World Problem.jpeg | 192.5 KB | ||
| 3. Solving a Real World Problem.mp4 | 98.8 MB | ||
| 3. Solving a Real World Problem.srt | 8.4 KB | ||
| 3. Understanding the Problem Statement.mp4 | 35.4 MB | ||
| 3. Understanding the Problem Statement.srt | 1.9 KB | ||
| 4. Introduction to Correlation using Heatmap.mp4 | 71.4 MB | ||
| 4. Introduction to Correlation using Heatmap.srt | 5.4 KB | ||
| 4. KMeans Clustering Analysis.mp4 | 61.8 MB | ||
| 4. KMeans Clustering Analysis.srt | 9.1 KB | ||
| 4. Performing Descriptive Statistics.mp4 | 73.4 MB | ||
| 4. Performing Descriptive Statistics.srt | 6.3 KB | ||
| 4. Using the Elbow Method for Choosing the Best Value for K.mp4 | 67 MB | ||
| 4. Using the Elbow Method for Choosing the Best Value for K.srt | 3.6 KB | ||
| 5. Analyzing Agricultural Conditions.mp4 | 39.1 MB | ||
| 5. Analyzing Agricultural Conditions.srt | 2.9 KB | ||
| 5. Applying Hierarchical Clustering.mp4 | 40.8 MB | ||
| 5. Applying Hierarchical Clustering.srt | 1.8 KB | ||
| 5. Introduction to K Means Clustering.mp4 | 49.3 MB | ||
| 5. Introduction to K Means Clustering.srt | 3.8 KB | ||
| 5. Removing Highly Correlated Columns using Correlation.mp4 | 48.9 MB | ||
| 5. Removing Highly Correlated Columns using Correlation.srt | 4 KB | ||
| 6. Clustering Similar Crops.mp4 | 63.6 MB | ||
| 6. Clustering Similar Crops.srt | 4.1 KB | ||
| 6. Introduction to Variance Inflation Filtering.mp4 | 48.7 MB | ||
| 6. Introduction to Variance Inflation Filtering.srt | 2.3 KB | ||
| 6. Solving a Real World Problem.mp4 | 71.1 MB | ||
| 6. Solving a Real World Problem.srt | 4.9 KB | ||
| 6. Three Dimensional Clustering.mp4 | 36.7 MB | ||
| 6. Three Dimensional Clustering.srt | 1.8 KB | ||
| 7. Implementing K Means on the Mall Dataset.mp4 | 71.6 MB | ||
| 7. Implementing K Means on the Mall Dataset.srt | 6.2 KB | ||
| 7. Implementing VIF using statsmodel.mp4 | 47.9 MB | ||
| 7. Implementing VIF using statsmodel.srt | 3.6 KB | ||
| 7. Visualizing the Hidden Patterns.mp4 | 27.8 MB | ||
| 7. Visualizing the Hidden Patterns.srt | 2.6 KB | ||
| 8. Building a Machine Learning Classification Model.mp4 | 40.4 MB | ||
| 8. Building a Machine Learning Classification Model.srt | 3.2 KB | ||
| 8. Introduction to Recursive Feature Selection.mp4 | 56.7 MB | ||
| 8. Introduction to Recursive Feature Selection.srt | 3.1 KB | ||
| 8. Using Silhouette Score to analyze the clusters.mp4 | 96.3 MB | ||
| 8. Using Silhouette Score to analyze the clusters.srt | 6.9 KB | ||
| 9. Clustering Multiple Dimensions.mp4 | 50 MB | ||
| 9. Clustering Multiple Dimensions.srt | 307.2 B | ||
| 9. Implementing Recursive Feature Selection.mp4 | 50.9 MB | ||
| 9. Implementing Recursive Feature Selection.srt | 4.2 KB | ||
| 9. Real Time Predictions.mp4 | 27.7 MB | ||
| 9. Real Time Predictions.srt | 2.1 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 101 total files | |||
Unsupervised Machine Learning with 2 Capstone ML Projects
Created by Data Is Good Academy | Published 7/2021
Duration: 3h 0m | 6 sections | 51 lectures | Video: 1280x720, 44 KHz | 2.678 GB
Genre: eLearning | Language: English + Sub
Learn Complete Unsupervised ML: Clustering Analysis and Dimensionality Reduction
What you'll learn
Understand the Working of K Means, Hierarchical, and DBSCAN Clustering.
Implement K Means, Hierarchical, and DBSCAN Clustering using Sklearn.
Learn Evaluation Metrics for Clustering Analysis.
Learn Techniques used for Treating Dimensionality.
Implement Correlation Filtering, VIF, and Feature Selection.
Implement PCA, LDA, and t-SNE for Dimensionality Reduction.
Analyze the Climatic Factors Best to Grow Certain Crops.
Recommend Crops by looking at Certain Climatic Factors.
Categorize the data into n number of relevant groups which are useful for Marketing Purposes.
Identify the Target Group of Customers.
Requirements
Python and Jupyter Notebook installed in your System.Knowledge about Basic Concepts of Python and its functions.Familiarity with Concepts of Data Analysis.Understanding of Data Visualizations.Understanding of Data Processing.Knowledge of Unsupervised Algorithms.Knowledge of K Means Clustering Algorithm.Good if you have interest in Agricultural Domain.
Description
Crazy about Unsupervised Machine Learning?
This course is a perfect fit for you.
This course will take you step by step into the world of Unsupervised Machine Learning.
Unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets.
These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information make it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, and image recognition.
This course will give you theoretical as well as practical knowledge of Unsupervised Machine Learning.
This Unsupervised Machine Learning course is fun as well as exciting.
It will cover all common and important algorithms and will give you the experience of working on some real-world projects.
This course will cover the following topics:-
K Means Clustering
Hierarchical Clustering
DBSCAN Clustering
Evaluation Metrics for Clustering Analysis
Techniques used for Treating Dimensionality
Different algorithms for clustering
Different methods to deal with imbalanced data.
Correlation filtering
Variance filtering
PCA & LDA
t-SNE for Dimensionality Reduction
\n
We have covered each and every topic in detail and also learned to apply them to real-world problems.
\n
There are lots and lots of exercises for you to practice and also 2 bonus Unsupervised Machine Learning Project "Optimizing Crop Production" and "Customer Segmentation Engine".
In this Optimizing Crop Production project, you will learn about Precision Farming using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend the best Crops to Farmers to Increase their Productivity.
In this Customer Segmentation Engine project, you will divide the customer base into several groups of individuals that share a similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits.
\n
You will make use of all the topics read in this course.
You will also have access to all the resources used in this course.
\n
Enroll now and become a master in Unsupervised machine learning.
Who this course is for:Anyone who want to start a career in Unsupervised Machine Learning.Any people who want to level up their Unsupervised Machine Learning Knowledge.Software developers or programmers or Tech lover who want to change their career path to Unsupervised machine learning.
,
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
|
Udemy - Cluster Analysis Unsupervised Machine Learning Course Bundle Posted by
freecoursewb in Other
|
3.2 GB | freecoursewb | 2 years | 0 | 2 |
| 685.2 MB | freecoursewb | 4 years | 0 | 0 | |
| 4.2 GB | tutsnode | 5 years | 0 | 0 | |
| 1.72 GB | tutsnode | 5 years | 0 | 1 | |
| 1.75 GB | tutsnode | 5 years | 0 | 0 |
All Comments