GigaCourse Com Udemy - Python for Machine Learning The Complete Beginner's Course

1. 1. Introduction to Machine Learning/1. What is Machine Learning.mp47.48MB
2. 1. Introduction to Machine Learning/2. Applications of Machine Learning.mp46.51MB
3. 1. Introduction to Machine Learning/3. Machine learning Methods.mp43.7MB
4. 1. Introduction to Machine Learning/4. What is Supervised learning.mp46.23MB
5. 1. Introduction to Machine Learning/5. What is Unsupervised learning.mp45.95MB
6. 1. Introduction to Machine Learning/6. Supervised learning vs Unsupervised learning.mp414.33MB
7. 1. Introduction to Machine Learning/7.14 u.data1.98MB
8. 2. Simple Linear Regression/1. Introduction to regression.mp48.97MB
9. 2. Simple Linear Regression/2. How Does Linear Regression Work.mp47.68MB
10. 2. Simple Linear Regression/3. Line representation.mp45.45MB
11. 2. Simple Linear Regression/4. Implementation in python Importing libraries & datasets.mp47.55MB
12. 2. Simple Linear Regression/5. Implementation in python Distribution of the data.mp49.46MB
13. 2. Simple Linear Regression/6. Implementation in python Creating a linear regression object.mp413.22MB
14. 3. Multiple Linear Regression/1. Understanding Multiple linear regression.mp46.32MB
15. 3. Multiple Linear Regression/2. Implementation in python Exploring the dataset.mp413.31MB
16. 3. Multiple Linear Regression/3. Implementation in python Encoding Categorical Data.mp428.92MB
17. 3. Multiple Linear Regression/4. Implementation in python Splitting data into Train and Test Sets.mp48.83MB
18. 3. Multiple Linear Regression/5. Implementation in python Training the model on the Training set.mp48.62MB
19. 3. Multiple Linear Regression/6. Implementation in python Predicting the Test Set results.mp417.83MB
20. 3. Multiple Linear Regression/7. Evaluating the performance of the regression model.mp46.01MB
21. 3. Multiple Linear Regression/8. Root Mean Squared Error in Python.mp411.83MB
22. 4. Classification Algorithms K-Nearest Neighbors/1. Introduction to classification.mp44.67MB
23. 4. Classification Algorithms K-Nearest Neighbors/10. Implementation in python Results prediction & Confusion matrix.mp49.67MB
24. 4. Classification Algorithms K-Nearest Neighbors/2. K-Nearest Neighbors algorithm.mp46.05MB
25. 4. Classification Algorithms K-Nearest Neighbors/3. Example of KNN.mp43.48MB
26. 4. Classification Algorithms K-Nearest Neighbors/4. K-Nearest Neighbours (KNN) using python.mp46.14MB
27. 4. Classification Algorithms K-Nearest Neighbors/5. Implementation in python Importing required libraries.mp45.11MB
28. 4. Classification Algorithms K-Nearest Neighbors/6. Implementation in python Importing the dataset.mp49.29MB
29. 4. Classification Algorithms K-Nearest Neighbors/7. Implementation in python Splitting data into Train and Test Sets.mp419.69MB
30. 4. Classification Algorithms K-Nearest Neighbors/8. Implementation in python Feature Scaling.mp45.73MB
31. 4. Classification Algorithms K-Nearest Neighbors/9. Implementation in python Importing the KNN classifier.mp412.51MB
32. 5. Classification Algorithms Decision Tree/1. Introduction to decision trees.mp46.49MB
33. 5. Classification Algorithms Decision Tree/2. What is Entropy.mp45.23MB
34. 5. Classification Algorithms Decision Tree/3. Exploring the dataset.mp45.96MB
35. 5. Classification Algorithms Decision Tree/4. Decision tree structure.mp46.39MB
36. 5. Classification Algorithms Decision Tree/5. Implementation in python Importing libraries & datasets.mp44.65MB
37. 5. Classification Algorithms Decision Tree/6. Implementation in python Encoding Categorical Data.mp416.98MB
38. 5. Classification Algorithms Decision Tree/7. Implementation in python Splitting data into Train and Test Sets.mp44.92MB
39. 5. Classification Algorithms Decision Tree/8. Implementation in python Results prediction & Accuracy.mp410.44MB
40. 6. Classification Algorithms Logistic regression/1. Introduction.mp46.59MB
41. 6. Classification Algorithms Logistic regression/2. Implementation steps.mp45.49MB
42. 6. Classification Algorithms Logistic regression/3. Implementation in python Importing libraries & datasets.mp46.82MB
43. 6. Classification Algorithms Logistic regression/4. Implementation in python Splitting data into Train and Test Sets.mp47.18MB
44. 6. Classification Algorithms Logistic regression/5. Implementation in python Pre-processing.mp413.17MB
45. 6. Classification Algorithms Logistic regression/6. Implementation in python Training the model.mp47.83MB
46. 6. Classification Algorithms Logistic regression/7. Implementation in python Results prediction & Confusion matrix.mp413.46MB
47. 6. Classification Algorithms Logistic regression/8. Logistic Regression vs Linear Regression.mp410.76MB
48. 7. Clustering/1. Introduction to clustering.mp44.26MB
49. 7. Clustering/10. Importing the dataset.mp412.78MB
50. 7. Clustering/11. Visualizing the dataset.mp412.43MB
51. 7. Clustering/12. Defining the classifier.mp47.66MB
52. 7. Clustering/13. 3D Visualization of the clusters.mp47.82MB
53. 7. Clustering/14. 3D Visualization of the predicted values.mp412.84MB
54. 7. Clustering/15. Number of predicted clusters.mp49.49MB
55. 7. Clustering/2. Use cases.mp44.05MB
56. 7. Clustering/3. K-Means Clustering Algorithm.mp46.62MB
57. 7. Clustering/4. Elbow method.mp47.02MB
58. 7. Clustering/5. Steps of the Elbow method.mp45.84MB
59. 7. Clustering/6. Implementation in python.mp419MB
60. 7. Clustering/7. Hierarchical clustering.mp47.42MB
61. 7. Clustering/8. Density-based clustering.mp47.79MB
62. 7. Clustering/9. Implementation of k-means clustering in python.mp43.93MB
63. 8. Recommender System/1. Introduction.mp47.54MB
64. 8. Recommender System/10. Data pre-processing.mp410.76MB
65. 8. Recommender System/11. Sorting the most-rated movies.mp48.88MB
66. 8. Recommender System/12. Grabbing the ratings for two movies.mp45.47MB
67. 8. Recommender System/13. Correlation between the most-rated movies.mp413.29MB
68. 8. Recommender System/14. Sorting the data by correlation.mp46.14MB
69. 8. Recommender System/15. Filtering out movies.mp44.79MB
70. 8. Recommender System/16. Sorting values.mp46.84MB
71. 8. Recommender System/17. Repeating the process for another movie.mp412.66MB
72. 8. Recommender System/2. Collaborative Filtering in Recommender Systems.mp44.16MB
73. 8. Recommender System/3. Content-based Recommender System.mp44.88MB
74. 8. Recommender System/4. Implementation in python Importing libraries & datasets.mp410.26MB
75. 8. Recommender System/5. Merging datasets into one dataframe.mp44.19MB
76. 8. Recommender System/6. Sorting by title and rating.mp419.33MB
77. 8. Recommender System/7. Histogram showing number of ratings.mp45.67MB
78. 8. Recommender System/8. Frequency distribution.mp46.05MB
79. 8. Recommender System/9. Jointplot of the ratings and number of ratings.mp47.28MB
80. 9. Conclusion/1. Conclusion.mp42.8MB