Udemy - Learn Data Mining and Machine Learning With Python

  • Category Other
  • Type Tutorials
  • Language English
  • Total size 3.0 GB
  • Uploaded By tutsnode
  • Downloads 274
  • Last checked 1 week ago
  • Date uploaded 5 years ago
  • Seeders 8
  • Leechers 11

Infohash : CF8645DA26A2DC6174C2D51CC4491E8EC8A1F48F




Description

If you need to learn how to understand and create Machine Learning models used to solve business problems, this course is for you. You will learn in this course everything you need about Data Mining process, Machine Learning and how to implement Machine Learning algorithms in Data Mining. This course was designed to provide information in a simple and straight forward way so ease learning methods. You will from scratch and keep building your knowledge step by step until you become familiar with the most used Machine Learning algorithms.
Who this course is for:

Anyone who need to use machine learning algorithms in data mining for business implementation

Requirements

Basic knowledge in Statistics and operating systems

Last Updated 10/2020

Files:

Learn Data Mining and Machine Learning With Python [TutsNode.com] - Learn Data Mining and Machine Learning With Python 3. Supervised Learning Algorithms
  • 24. Create Multiple Linear Regression Model in Python-Part 1.mp4 (172.6 MB)
  • 1. Introduction to Supervised Learning Algorithms.mp4 (6.3 MB)
  • 1. Introduction to Supervised Learning Algorithms.srt (1.3 KB)
  • 2. Types of Variables.mp4 (10.3 MB)
  • 2. Types of Variables.srt (2.6 KB)
  • 3. Data Types.html (0.2 KB)
  • 4. Introduction to Regression Model.mp4 (21.3 MB)
  • 4. Introduction to Regression Model.srt (4.9 KB)
  • 5. Regression Model.html (0.2 KB)
  • 6. Regression Model Slope.mp4 (31.2 MB)
  • 6. Regression Model Slope.srt (7.4 KB)
  • 7. Regression Slope.html (0.2 KB)
  • 8. The Intercept Value.html (0.2 KB)
  • 9. R-Squared.mp4 (29.1 MB)
  • 9. R-Squared.srt (8.2 KB)
  • 10. P-Value.mp4 (17.4 MB)
  • 10. P-Value.srt (4.2 KB)
  • 11. Simple Linear Regression.mp4 (3.7 MB)
  • 11. Simple Linear Regression.srt (1.0 KB)
  • 12. Concepts used in Machine Learning (Important).html (0.2 KB)
  • 13. Overview on the dataset.mp4 (7.9 MB)
  • 13. Overview on the dataset.srt (1.5 KB)
  • 13.1 Study_Hours.csv (0.3 KB)
  • 14. Create Simple Linear Regression Model in Python-Part 1.mp4 (30.9 MB)
  • 14. Create Simple Linear Regression Model in Python-Part 1.srt (5.5 KB)
  • 14.1 SLR.py (1.2 KB)
  • 15. Create Simple Linear Regression Model in Python-Part 2.mp4 (129.3 MB)
  • 15. Create Simple Linear Regression Model in Python-Part 2.srt (13.2 KB)
  • 16. Create Simple Linear Regression Model in Python-Part 3.mp4 (66.1 MB)
  • 16. Create Simple Linear Regression Model in Python-Part 3.srt (6.4 KB)
  • 17. Create Simple Linear Regression Model in Python-Part 4.mp4 (70.3 MB)
  • 17. Create Simple Linear Regression Model in Python-Part 4.srt (6.9 KB)
  • 18. Multiple Linear Regression.mp4 (14.2 MB)
  • 18. Multiple Linear Regression.srt (2.9 KB)
  • 19. Dummy Variables.mp4 (36.4 MB)
  • 19. Dummy Variables.srt (5.8 KB)
  • 20. Dummy Variables Trap.html (0.6 KB)
  • 21. Step-wise Approach.mp4 (28.8 MB)
  • 21. Step-wise Approach.srt (7.0 KB)
  • 22. Assumptions of Multiple Linear Regression.mp4 (31.3 MB)
  • 22. Assumptions of Multiple Linear Regression.srt (9.4 KB)
  • 23. Overview on the business problem data.mp4 (8.7 MB)
  • 23. Overview on the business problem data.srt (1.5 KB)
  • 23.1 Companies spends and profits.csv (3.2 KB)
  • 24. Create Multiple Linear Regression Model in Python-Part 1.srt (16.9 KB)
  • 24.1 MLR.py (2.1 KB)
  • 25. Create Multiple Linear Regression Model in Python-Part 2.mp4 (141.3 MB)
  • 25. Create Multiple Linear Regression Model in Python-Part 2.srt (12.9 KB)
  • 26. Create Multiple Linear Regression Model in Python-Part 3.mp4 (138.1 MB)
  • 26. Create Multiple Linear Regression Model in Python-Part 3.srt (11.6 KB)
  • 27. Create Multiple Linear Regression Model in Python-Part 4.mp4 (82.3 MB)
  • 27. Create Multiple Linear Regression Model in Python-Part 4.srt (8.0 KB)
  • 28. Polynomial Regression.mp4 (10.6 MB)
  • 28. Polynomial Regression.srt (2.3 KB)
  • 29. Overview on the business problem data.mp4 (7.2 MB)
  • 29. Overview on the business problem data.srt (1.3 KB)
  • 29.1 Reward_system.csv (0.2 KB)
  • 30. Create Polynomial Regression Model in Python-Part 1.mp4 (79.4 MB)
  • 30. Create Polynomial Regression Model in Python-Part 1.srt (9.7 KB)
  • 30.1 PR.py (1.7 KB)
  • 31. Create Polynomial Regression Model in Python-Part 2.mp4 (124.4 MB)
  • 31. Create Polynomial Regression Model in Python-Part 2.srt (13.4 KB)
  • 32. Course Rating.html (0.5 KB)
  • 33. Introduction to Classification.mp4 (19.9 MB)
  • 33. Introduction to Classification.srt (4.7 KB)
  • 34. Introduction to Logistic Regression.mp4 (29.1 MB)
  • 34. Introduction to Logistic Regression.srt (8.9 KB)
  • 35. Confusion Matrix.mp4 (15.6 MB)
  • 35. Confusion Matrix.srt (4.5 KB)
  • 36. Standard Scaler.mp4 (12.2 MB)
  • 36. Standard Scaler.srt (3.3 KB)
  • 37. Overview on the business problem data.mp4 (10.3 MB)
  • 37. Overview on the business problem data.srt (1.8 KB)
  • 37.1 Bank_Data.csv (17.2 KB)
  • 38. Create Logistic Regression Model in Python-Part 1.mp4 (112.0 MB)
  • 38. Create Logistic Regression Model in Python-Part 1.srt (12.9 KB)
  • 38.1 LR.py (1.1 KB)
  • 39. Create Logistic Regression Model in Python-Part 2.mp4 (59.6 MB)
  • 39. Create Logistic Regression Model in Python-Part 2.srt (6.8 KB)
  • 40. KNN Classification Algorithm.mp4 (15.3 MB)
  • 40. KNN Classification Algorithm.srt (4.4 KB)
  • 41. Create KNN Model in Python.mp4 (65.2 MB)
  • 41. Create KNN Model in Python.srt (8.2 KB)
  • 41.1 K-NN.py (1.2 KB)
  • 41.2 Bank_Data.csv (17.2 KB)
  • 42. Support Vector Machine (SVM) Classification Algorithm.mp4 (17.8 MB)
  • 42. Support Vector Machine (SVM) Classification Algorithm.srt (4.0 KB)
  • 43. Create Support Vector Machine in Python.mp4 (53.1 MB)
  • 43. Create Support Vector Machine in Python.srt (7.7 KB)
  • 43.1 SVM.py (1.2 KB)
  • 44. Naive Bayes Algorithm Part 1.mp4 (19.8 MB)
  • 44. Naive Bayes Algorithm Part 1.srt (5.2 KB)
  • 45. Naive Bayes Algorithm Part 2.mp4 (27.9 MB)
  • 45. Naive Bayes Algorithm Part 2.srt (7.0 KB)
  • 46. Create Naive Bayes Model in Python.mp4 (28.9 MB)
  • 46. Create Naive Bayes Model in Python.srt (3.8 KB)
  • 46.1 Naive_Bayes.py (1.1 KB)
  • 47. Decision Tree Algorithm.mp4 (34.5 MB)
  • 47. Decision Tree Algorithm.srt (7.9 KB)
  • 48. Create Decision Tree Model in Python.mp4 (33.6 MB)
  • 48. Create Decision Tree Model in Python.srt (3.5 KB)
  • 48.1 Bank_Data.csv (17.2 KB)
  • 48.2 Decision Tree.py (1.1 KB)
  • 49. Random Forest Algorithm.mp4 (6.1 MB)
  • 49. Random Forest Algorithm.srt (1.4 KB)
  • 50. Create Random Forest Model in Python.mp4 (69.4 MB)
  • 50. Create Random Forest Model in Python.srt (6.4 KB)
  • 50.1 Random_Forest.py (1.2 KB)
  • 51. Course Rating.html (0.5 KB)
1. Introduction
  • 1. Introduction to Course.mp4 (12.0 MB)
  • 1. Introduction to Course.srt (2.4 KB)
  • 2. Course Contents.mp4 (10.1 MB)
  • 2. Course Contents.srt (1.7 KB)
  • 3. Introduction to Data Mining.mp4 (42.8 MB)
  • 3. Introduction to Data Mining.srt (10.8 KB)
  • 4. Data Mining Definition.html (0.2 KB)
  • 5. Introduction to Machine Learning.mp4 (14.3 MB)
  • 5. Introduction to Machine Learning.srt (3.1 KB)
  • 6. Machine Leaning Sub-fields..html (0.2 KB)
  • 7. How Does Machine Learning Work.mp4 (19.5 MB)
  • 7. How Does Machine Learning Work.srt (4.5 KB)
  • 8. Train and Test Sets..html (0.2 KB)
  • 9. Machine Learning Algorithms Types.mp4 (40.8 MB)
  • 9. Machine Learning Algorithms Types.srt (8.2 KB)
  • 10. Machine Leaning Types.html (0.2 KB)
  • 11. Course Rating.html (0.5 KB)
2. Setup Programming Environment
  • 1. Install Anaconda package.mp4 (48.1 MB)
  • 1. Install Anaconda package.srt (7.3 KB)
4. Unsupervised Learning Algorithms
  • 1. Review Unsupervised Learning Algorithms.mp4 (8.5 MB)
  • 1. Review Unsupervised Learning Algorithms.srt (1.8 KB)
  • 2. Hierarchical Clustering Algorithm.mp4 (9.9 MB)
  • 2. Hierarchical Clustering Algorithm.srt (4.1 KB)
  • 3. Dendrogram Diagram Method.mp4 (28.6 MB)
  • 3. Dendrogram Diagram Method.srt (6.9 KB)
  • 4. Overview on the business problem data.mp4 (4.2 MB)
  • 4. Overview on the business problem data.srt (0.8 KB)
  • 5. Create Hierarchical Clustering Algorithm in Python-1.mp4 (128.3 MB)
  • 5. Create Hierarchical Clustering Algorithm in Python-1.srt (13.7 KB)
  • 5.1 Hierarchical Clustering.py (1.2 KB)
  • 5.2 Movies.csv (1.7 KB)
  • 6. Create Hierarchical Clustering Algorithm in Python-2.mp4 (72.1 MB)
  • 6. Create Hierarchical Clustering Algorithm in Python-2.srt (7.3 KB)
  • 7. K-means Clustering Algorithm.mp4 (16.2 MB)
  • 7. K-means Clustering Algorithm.srt (4.0 KB)
  • 8. Using Elbow Method to Determine Optimal Number of Clusters.mp4 (48.8 MB)
  • 8. Using Elbow Method to Determine Optimal Number of Clusters.srt (11.3 KB)
  • 9. Create K-means Clustering Algorithm Model in Python - 1.mp4 (78.6 MB)
  • 9. Create K-means Clustering Algorithm Model in Python - 1.srt (8.6 KB)
  • 9.1 kmeans.py (1.2 KB)
  • 9.2 Movies.csv (1.7 KB)
  • 10. Create K-means Clustering Algorithm Model in Python - 2.mp4 (33.0 MB)
  • 10. Create K-means Clustering Algorithm Model in Python - 2.srt (3.7 KB)
  • 11. Association Rules (Market Basket Analysis).mp4 (52.2 MB)
  • 11. Association Rules (Market Basket Analysis).srt (11.5 KB)
  • 12. Overview on the business problem data.mp4 (13.5 MB)
  • 12. Overview on the business problem data.srt (2.0 KB)
  • 12.1 GroceryStoreDataSet.csv (0.5 KB)
  • 13. Create Association Rules (Market Basket Analysis) Model in Python - 1.mp4 (69.4 MB)
  • 13. Create Association Rules (Market Basket Analysis) Model in Python - 1.srt (10.3 KB)
  • 13.1 apyori.py (14.2 KB)
  • 13.2 AR.py (0.5 KB)
  • 14. Create Association Rules (Market Basket Analysis) Model in Python - 2.mp4 (33.3 MB)
  • 14. Create Association Rules (Market Basket Analysis) Model in Python - 2.srt (5.1 KB)
  • 15. Create Association Rules (Market Basket Analysis) Model in Python - 3.mp4 (28.6 MB)
  • 15. Create Association Rules (Market Basket Analysis) Model in Python - 3.srt (2.8 KB)
5. Deep Learning
  • 1. Introduction to Deep Learning.mp4 (36.9 MB)
  • 1. Introduction to Deep Learning.srt (6.9 KB)
  • 2. Use Deep Learning in Classification.mp4 (14.1 MB)
  • 2. Use Deep Learning in Classification.srt (2.7 KB)
  • 3. How Does Deep Learning Work.mp4 (27.6 MB)
  • 3. How Does Deep Learning Work.srt (5.7 KB)
  • 4. Activation Functions.mp4 (34.8 MB)
  • 4. Activation Functions.srt (7.3 KB)
  • 5. What is Tensorflow.mp4 (7.0 MB)
  • 5. What is Tensorflow.srt (2.6 KB)
  • 6. Introduction to the Deep Learning Problem and Dataset.mp4 (9.0 MB)
  • 6. Introduction to the Deep Learning Problem and Dataset.srt (1.1 KB)
  • 7. Create Artificial Neural Network Model in Python Part-1.mp4 (64.9 MB)
  • 7. Create Artificial Neural Network Model in Python Part-1.srt (7.0 KB)
  • 7.1 M_ANN.py (1.5 KB)
  • 7.2 Medical_data.csv (23.5 KB)
  • 8. Create Artificial Neural Network Model in Python Part-2.mp4 (69.0 MB)
  • 8. Create Artificial Neural Network Model in Python Part-2.srt (9.9 KB)
  • 9. Create Artificial Neural Network Model in Python Part-3.mp4 (65.6 MB)
  • 9. Create Artificial Neural Network Model in Python Part-3.srt (6.5 KB)
  • 9.1 Link to the Keras documentation website..html (0.1 KB)
  • 10. The Newer Version of Keras Python code to Create the Model and Add the Layers.html (1.3 KB)
  • 11. Create Artificial Neural Network Model in Python Part-4.mp4 (22.0 MB)
  • 11. Create Artificial Neural Network Model in Python Part-4.srt (2.3 KB)
  • 12. Course Rating.html (0.5 KB)
  • TutsNode.com.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)

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