FreeCourseSite com Udemy - 2021 Python for Machine Learning & Data Science Masterclass

1. 01 Introduction to Course/002 COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!.mp424.55MB
2. 01 Introduction to Course/003 Anaconda Python and Jupyter Install and Setup.mp484.52MB
3. 01 Introduction to Course/005 Environment Setup.mp423.23MB
4. 02 OPTIONAL_ Python Crash Course/002 Python Crash Course - Part One.mp429.76MB
5. 02 OPTIONAL_ Python Crash Course/003 Python Crash Course - Part Two.mp425.85MB
6. 02 OPTIONAL_ Python Crash Course/004 Python Crash Course - Part Three.mp432.02MB
7. 02 OPTIONAL_ Python Crash Course/005 Python Crash Course - Exercise Questions.mp47.82MB
8. 02 OPTIONAL_ Python Crash Course/006 Python Crash Course - Exercise Solutions.mp433.49MB
9. 03 Machine Learning Pathway Overview/001 Machine Learning Pathway.mp440.54MB
10. 04 NumPy/001 Introduction to NumPy.mp47.88MB
11. 04 NumPy/002 NumPy Arrays.mp499.45MB
12. 04 NumPy/003 NumPy Indexing and Selection.mp439.64MB
13. 04 NumPy/004 NumPy Operations.mp436.04MB
14. 04 NumPy/005 NumPy Exercises.mp49.66MB
15. 04 NumPy/006 Numpy Exercises - Solutions.mp434.9MB
16. 05 Pandas/001 Introduction to Pandas.mp48.72MB
17. 05 Pandas/002 Series - Part One.mp428.63MB
18. 05 Pandas/003 Series - Part Two.mp426.13MB
19. 05 Pandas/004 DataFrames - Part One - Creating a DataFrame.mp497.4MB
20. 05 Pandas/005 DataFrames - Part Two - Basic Properties.mp440.26MB
21. 05 Pandas/006 DataFrames - Part Three - Working with Columns.mp484.07MB
22. 05 Pandas/007 DataFrames - Part Four - Working with Rows.mp472.57MB
23. 05 Pandas/008 Pandas - Conditional Filtering.mp469.23MB
24. 05 Pandas/009 Pandas - Useful Methods - Apply on Single Column.mp453.73MB
25. 05 Pandas/010 Pandas - Useful Methods - Apply on Multiple Columns.mp485.33MB
26. 05 Pandas/011 Pandas - Useful Methods - Statistical Information and Sorting.mp474.4MB
27. 05 Pandas/012 Missing Data - Overview.mp427.26MB
28. 05 Pandas/013 Missing Data - Pandas Operations.mp473.6MB
29. 05 Pandas/014 GroupBy Operations - Part One.mp486.92MB
30. 05 Pandas/015 GroupBy Operations - Part Two - MultiIndex.mp493.05MB
31. 05 Pandas/016 Combining DataFrames - Concatenation.mp436.84MB
32. 05 Pandas/017 Combining DataFrames - Inner Merge.mp440.28MB
33. 05 Pandas/018 Combining DataFrames - Left and Right Merge.mp416.43MB
34. 05 Pandas/019 Combining DataFrames - Outer Merge.mp422.19MB
35. 05 Pandas/020 Pandas - Text Methods for String Data.mp445.14MB
36. 05 Pandas/021 Pandas - Time Methods for Date and Time Data.mp480.22MB
37. 05 Pandas/022 Pandas Input and Output - CSV Files.mp437.13MB
38. 05 Pandas/023 Pandas Input and Output - HTML Tables.mp4102.38MB
39. 05 Pandas/024 Pandas Input and Output - Excel Files.mp425.91MB
40. 05 Pandas/025 Pandas Input and Output - SQL Databases.mp496.18MB
41. 05 Pandas/026 Pandas Pivot Tables.mp4128.74MB
42. 05 Pandas/027 Pandas Project Exercise Overview.mp439.38MB
43. 05 Pandas/028 Pandas Project Exercise Solutions.mp4172.62MB
44. 06 Matplotlib/001 Introduction to Matplotlib.mp411.39MB
45. 06 Matplotlib/002 Matplotlib Basics.mp431.07MB
46. 06 Matplotlib/003 Matplotlib - Understanding the Figure Object.mp411.7MB
47. 06 Matplotlib/004 Matplotlib - Implementing Figures and Axes.mp434.86MB
48. 06 Matplotlib/005 Matplotlib - Figure Parameters.mp411.4MB
49. 06 Matplotlib/006 Matplotlib - Subplots Functionality.mp496.18MB
50. 06 Matplotlib/007 Matplotlib Styling - Legends.mp416.21MB
51. 06 Matplotlib/008 Matplotlib Styling - Colors and Styles.mp444.29MB
52. 06 Matplotlib/009 Advanced Matplotlib Commands (Optional).mp425.24MB
53. 06 Matplotlib/010 Matplotlib Exercise Questions Overview.mp448.94MB
54. 06 Matplotlib/011 Matplotlib Exercise Questions - Solutions.mp4105.83MB
55. 07 Seaborn Data Visualizations/001 Introduction to Seaborn.mp410.53MB
56. 07 Seaborn Data Visualizations/002 Scatterplots with Seaborn.mp4111.13MB
57. 07 Seaborn Data Visualizations/003 Distribution Plots - Part One - Understanding Plot Types.mp415.04MB
58. 07 Seaborn Data Visualizations/004 Distribution Plots - Part Two - Coding with Seaborn.mp444.41MB
59. 07 Seaborn Data Visualizations/005 Categorical Plots - Statistics within Categories - Understanding Plot Types.mp416MB
60. 07 Seaborn Data Visualizations/006 Categorical Plots - Statistics within Categories - Coding with Seaborn.mp451.61MB
61. 07 Seaborn Data Visualizations/007 Categorical Plots - Distributions within Categories - Understanding Plot Types.mp444.98MB
62. 07 Seaborn Data Visualizations/008 Categorical Plots - Distributions within Categories - Coding with Seaborn.mp484.59MB
63. 07 Seaborn Data Visualizations/009 Seaborn - Comparison Plots - Understanding the Plot Types.mp410.57MB
64. 07 Seaborn Data Visualizations/010 Seaborn - Comparison Plots - Coding with Seaborn.mp451.13MB
65. 07 Seaborn Data Visualizations/011 Seaborn Grid Plots.mp486.98MB
66. 07 Seaborn Data Visualizations/012 Seaborn - Matrix Plots.mp434.45MB
67. 07 Seaborn Data Visualizations/013 Seaborn Plot Exercises Overview.mp415.8MB
68. 07 Seaborn Data Visualizations/014 Seaborn Plot Exercises Solutions.mp4105.67MB
69. 08 Data Analysis and Visualization Capstone Project Exercise/001 Capstone Project Overview.mp493.2MB
70. 08 Data Analysis and Visualization Capstone Project Exercise/002 Capstone Project Solutions - Part One.mp4101.92MB
71. 08 Data Analysis and Visualization Capstone Project Exercise/003 Capstone Project Solutions - Part Two.mp4106.21MB
72. 08 Data Analysis and Visualization Capstone Project Exercise/004 Capstone Project Solutions - Part Three.mp4137.26MB
73. 09 Machine Learning Concepts Overview/001 Introduction to Machine Learning Overview Section.mp413.19MB
74. 09 Machine Learning Concepts Overview/002 Why Machine Learning_.mp421.01MB
75. 09 Machine Learning Concepts Overview/003 Types of Machine Learning Algorithms.mp418.11MB
76. 09 Machine Learning Concepts Overview/004 Supervised Machine Learning Process.mp433.53MB
77. 09 Machine Learning Concepts Overview/005 Companion Book - Introduction to Statistical Learning.mp49.68MB
78. 10 Linear Regression/001 Introduction to Linear Regression Section.mp43.38MB
79. 10 Linear Regression/002 Linear Regression - Algorithm History.mp454.71MB
80. 10 Linear Regression/003 Linear Regression - Understanding Ordinary Least Squares.mp473.28MB
81. 10 Linear Regression/004 Linear Regression - Cost Functions.mp416.64MB
82. 10 Linear Regression/005 Linear Regression - Gradient Descent.mp429.21MB
83. 10 Linear Regression/006 Python coding Simple Linear Regression.mp483.88MB
84. 10 Linear Regression/007 Overview of Scikit-Learn and Python.mp423.14MB
85. 10 Linear Regression/008 Linear Regression - Scikit-Learn Train Test Split.mp461.44MB
86. 10 Linear Regression/009 Linear Regression - Scikit-Learn Performance Evaluation - Regression.mp461.79MB
87. 10 Linear Regression/010 Linear Regression - Residual Plots.mp429.66MB
88. 10 Linear Regression/011 Linear Regression - Model Deployment and Coefficient Interpretation.mp481.24MB
89. 10 Linear Regression/012 Polynomial Regression - Theory and Motivation.mp422.26MB
90. 10 Linear Regression/013 Polynomial Regression - Creating Polynomial Features.mp440.08MB
91. 10 Linear Regression/014 Polynomial Regression - Training and Evaluation.mp436.31MB
92. 10 Linear Regression/015 Bias Variance Trade-Off.mp436.19MB
93. 10 Linear Regression/016 Polynomial Regression - Choosing Degree of Polynomial.mp455.73MB
94. 10 Linear Regression/017 Polynomial Regression - Model Deployment.mp423.24MB
95. 10 Linear Regression/018 Regularization Overview.mp413.07MB
96. 10 Linear Regression/019 Feature Scaling.mp424.36MB
97. 10 Linear Regression/020 Introduction to Cross Validation.mp429.28MB
98. 10 Linear Regression/021 Regularization Data Setup.mp415.43MB
99. 10 Linear Regression/022 L2 Regularization - Ridge Regression Theory.mp461.08MB
100. 10 Linear Regression/023 L2 Regularization - Ridge Regression - Python Implementation.mp489.37MB
101. 10 Linear Regression/024 L1 Regularization - Lasso Regression - Background and Implementation.mp494.55MB
102. 10 Linear Regression/025 L1 and L2 Regularization - Elastic Net.mp466.42MB
103. 10 Linear Regression/026 Linear Regression Project - Data Overview.mp416.95MB
104. 11 Feature Engineering and Data Preparation/002 Introduction to Feature Engineering and Data Preparation.mp440.68MB
105. 11 Feature Engineering and Data Preparation/003 Dealing with Outliers.mp4120.68MB
106. 11 Feature Engineering and Data Preparation/004 Dealing with Missing Data _ Part One - Evaluation of Missing Data.mp431.43MB
107. 11 Feature Engineering and Data Preparation/005 Dealing with Missing Data _ Part Two - Filling or Dropping data based on Rows.mp4117.6MB
108. 11 Feature Engineering and Data Preparation/006 Dealing with Missing Data _ Part 3 - Fixing data based on Columns.mp4105.28MB
109. 11 Feature Engineering and Data Preparation/007 Dealing with Categorical Data - Encoding Options.mp458.93MB
110. 12 Cross Validation , Grid Search, and the Linear Regression Project/001 Section Overview and Introduction.mp49.95MB
111. 12 Cross Validation , Grid Search, and the Linear Regression Project/002 Cross Validation - Test _ Train Split.mp446.89MB
112. 12 Cross Validation , Grid Search, and the Linear Regression Project/003 Cross Validation - Test _ Validation _ Train Split.mp459.45MB
113. 12 Cross Validation , Grid Search, and the Linear Regression Project/004 Cross Validation - cross_val_score.mp444.51MB
114. 12 Cross Validation , Grid Search, and the Linear Regression Project/005 Cross Validation - cross_validate.mp445.08MB
115. 12 Cross Validation , Grid Search, and the Linear Regression Project/006 Grid Search.mp473.22MB
116. 12 Cross Validation , Grid Search, and the Linear Regression Project/007 Linear Regression Project Overview.mp423.55MB
117. 12 Cross Validation , Grid Search, and the Linear Regression Project/008 Linear Regression Project - Solutions.mp491.17MB
118. 13 Logistic Regression/002 Introduction to Logistic Regression Section.mp413.94MB
119. 13 Logistic Regression/003 Logistic Regression - Theory and Intuition - Part One_ The Logistic Function.mp417.35MB
120. 13 Logistic Regression/004 Logistic Regression - Theory and Intuition - Part Two_ Linear to Logistic.mp411.07MB
121. 13 Logistic Regression/005 Logistic Regression - Theory and Intuition - Linear to Logistic Math.mp436.05MB
122. 13 Logistic Regression/006 Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood.mp454.89MB
123. 13 Logistic Regression/007 Logistic Regression with Scikit-Learn - Part One - EDA.mp462.6MB
124. 13 Logistic Regression/008 Logistic Regression with Scikit-Learn - Part Two - Model Training.mp432.57MB
125. 13 Logistic Regression/009 Classification Metrics - Confusion Matrix and Accuracy.mp421.72MB
126. 13 Logistic Regression/010 Classification Metrics - Precison, Recall, F1-Score.mp423.42MB
127. 13 Logistic Regression/011 Classification Metrics - ROC Curves.mp416.09MB
128. 13 Logistic Regression/012 Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation.mp463.65MB
129. 13 Logistic Regression/013 Multi-Class Classification with Logistic Regression - Part One - Data and EDA.mp437.38MB
130. 13 Logistic Regression/014 Multi-Class Classification with Logistic Regression - Part Two - Model.mp4105.09MB
131. 13 Logistic Regression/015 Logistic Regression Exercise Project Overview.mp424.32MB
132. 13 Logistic Regression/016 Logistic Regression Project Exercise - Solutions.mp4145.55MB
133. 14 KNN - K Nearest Neighbors/001 Introduction to KNN Section.mp44.97MB
134. 14 KNN - K Nearest Neighbors/002 KNN Classification - Theory and Intuition.mp423.56MB
135. 14 KNN - K Nearest Neighbors/003 KNN Coding with Python - Part One.mp461.61MB
136. 14 KNN - K Nearest Neighbors/004 KNN Coding with Python - Part Two - Choosing K.mp4102.92MB
137. 14 KNN - K Nearest Neighbors/005 KNN Classification Project Exercise Overview.mp421.1MB
138. 14 KNN - K Nearest Neighbors/006 KNN Classification Project Exercise Solutions.mp4105.05MB
139. 15 Support Vector Machines/001 Introduction to Support Vector Machines.mp44.34MB
140. 15 Support Vector Machines/002 History of Support Vector Machines.mp415.55MB
141. 15 Support Vector Machines/003 SVM - Theory and Intuition - Hyperplanes and Margins.mp435.31MB
142. 15 Support Vector Machines/004 SVM - Theory and Intuition - Kernel Intuition.mp413.36MB
143. 15 Support Vector Machines/005 SVM - Theory and Intuition - Kernel Trick and Mathematics.mp452.69MB
144. 15 Support Vector Machines/006 SVM with Scikit-Learn and Python - Classification Part One.mp446.29MB
145. 15 Support Vector Machines/007 SVM with Scikit-Learn and Python - Classification Part Two.mp483.18MB
146. 15 Support Vector Machines/008 SVM with Scikit-Learn and Python - Regression Tasks.mp476.32MB
147. 15 Support Vector Machines/009 Support Vector Machine Project Overview.mp434.83MB
148. 15 Support Vector Machines/010 Support Vector Machine Project Solutions.mp493.45MB
149. 16 Tree Based Methods_ Decision Tree Learning/001 Introduction to Tree Based Methods.mp42.61MB
150. 16 Tree Based Methods_ Decision Tree Learning/002 Decision Tree - History.mp435.59MB
151. 16 Tree Based Methods_ Decision Tree Learning/003 Decision Tree - Terminology.mp46.34MB
152. 16 Tree Based Methods_ Decision Tree Learning/004 Decision Tree - Understanding Gini Impurity.mp419.47MB
153. 16 Tree Based Methods_ Decision Tree Learning/005 Constructing Decision Trees with Gini Impurity - Part One.mp417.72MB
154. 16 Tree Based Methods_ Decision Tree Learning/006 Constructing Decision Trees with Gini Impurity - Part Two.mp428.21MB
155. 16 Tree Based Methods_ Decision Tree Learning/007 Coding Decision Trees - Part One - The Data.mp498.73MB
156. 16 Tree Based Methods_ Decision Tree Learning/008 Coding Decision Trees - Part Two -Creating the Model.mp4115.85MB
157. 17 Random Forests/001 Introduction to Random Forests Section.mp44.1MB
158. 17 Random Forests/002 Random Forests - History and Motivation.mp424.01MB
159. 17 Random Forests/003 Random Forests - Key Hyperparameters.mp49.6MB
160. 17 Random Forests/004 Random Forests - Number of Estimators and Features in Subsets.mp427.33MB
161. 17 Random Forests/005 Random Forests - Bootstrapping and Out-of-Bag Error.mp443.38MB
162. 17 Random Forests/006 Coding Classification with Random Forest Classifier - Part One.mp452.11MB
163. 17 Random Forests/007 Coding Classification with Random Forest Classifier - Part Two.mp4130.38MB
164. 17 Random Forests/008 Coding Regression with Random Forest Regressor - Part One - Data.mp413.71MB
165. 17 Random Forests/009 Coding Regression with Random Forest Regressor - Part Two - Basic Models.mp484.92MB
166. 17 Random Forests/010 Coding Regression with Random Forest Regressor - Part Three - Polynomials.mp445.61MB
167. 17 Random Forests/011 Coding Regression with Random Forest Regressor - Part Four - Advanced Models.mp450.66MB
168. 18 Boosting Methods/001 Introduction to Boosting Section.mp44.11MB
169. 18 Boosting Methods/002 Boosting Methods - Motivation and History.mp421.97MB
170. 18 Boosting Methods/003 AdaBoost Theory and Intuition.mp441.55MB
171. 18 Boosting Methods/004 AdaBoost Coding Part One - The Data.mp422.77MB
172. 18 Boosting Methods/005 AdaBoost Coding Part Two - The Model.mp463.12MB
173. 18 Boosting Methods/006 Gradient Boosting Theory.mp422.96MB
174. 18 Boosting Methods/007 Gradient Boosting Coding Walkthrough.mp457.98MB
175. 19 Supervised Learning Capstone Project - Cohort Analysis and Tree Based Methods/001 Introduction to Supervised Learning Capstone Project.mp473.3MB
176. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/001 Introduction to NLP and Naive Bayes Section.mp46.75MB
177. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/002 Naive Bayes Algorithm - Part One - Bayes Theorem.mp422.04MB
178. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/003 Naive Bayes Algorithm - Part Two - Model Algorithm.mp448.64MB
179. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/009 Text Classification Project Exercise Overview.mp430.53MB
180. 20 Naive Bayes Classification and Natural Language Processing (Supervised Learning)/010 Text Classification Project Exercise Solutions.mp4108.07MB
181. 21 Unsupervised Learning/001 Unsupervised Learning Overview.mp431.73MB
182. 22 K-Means Clustering/001 Introduction to K-Means Clustering Section.mp44.57MB
183. 22 K-Means Clustering/002 Clustering General Overview.mp424.89MB
184. 22 K-Means Clustering/003 K-Means Clustering Theory.mp452.36MB
185. 22 K-Means Clustering/004 K-Means Clustering - Coding Part One.mp497.51MB
186. 22 K-Means Clustering/005 K-Means Clustering Coding Part Two.mp480.63MB
187. 22 K-Means Clustering/006 K-Means Clustering Coding Part Three.mp459.54MB
188. 22 K-Means Clustering/007 K-Means Color Quantization - Part One.mp480.36MB
189. 22 K-Means Clustering/008 K-Means Color Quantization - Part Two.mp464.75MB
190. 22 K-Means Clustering/009 K-Means Clustering Exercise Overview.mp459.33MB
191. 22 K-Means Clustering/010 K-Means Clustering Exercise Solution - Part One.mp479.72MB
192. 22 K-Means Clustering/011 K-Means Clustering Exercise Solution - Part Two.mp4107.89MB
193. 22 K-Means Clustering/012 K-Means Clustering Exercise Solution - Part Three.mp462.47MB
194. 23 Hierarchical Clustering/001 Introduction to Hierarchical Clustering.mp45.81MB
195. 23 Hierarchical Clustering/002 Hierarchical Clustering - Theory and Intuition.mp451.94MB
196. 23 Hierarchical Clustering/003 Hierarchical Clustering - Coding Part One - Data and Visualization.mp4114.83MB
197. 23 Hierarchical Clustering/004 Hierarchical Clustering - Coding Part Two - Scikit-Learn.mp4208.67MB
198. 24 DBSCAN - Density-based spatial clustering of applications with noise/001 Introduction to DBSCAN Section.mp45.92MB
199. 24 DBSCAN - Density-based spatial clustering of applications with noise/002 DBSCAN - Theory and Intuition.mp4109.1MB
200. 24 DBSCAN - Density-based spatial clustering of applications with noise/003 DBSCAN versus K-Means Clustering.mp466.74MB
201. 24 DBSCAN - Density-based spatial clustering of applications with noise/004 DBSCAN - Hyperparameter Theory.mp416.46MB
202. 24 DBSCAN - Density-based spatial clustering of applications with noise/005 DBSCAN - Hyperparameter Tuning Methods.mp4105.09MB
203. 24 DBSCAN - Density-based spatial clustering of applications with noise/006 DBSCAN - Outlier Project Exercise Overview.mp450.18MB
204. 24 DBSCAN - Density-based spatial clustering of applications with noise/007 DBSCAN - Outlier Project Exercise Solutions.mp4127.96MB
205. 25 PCA - Principal Component Analysis and Manifold Learning/001 Introduction to Principal Component Analysis.mp46.15MB
206. 25 PCA - Principal Component Analysis and Manifold Learning/002 PCA Theory and Intuition - Part One.mp429.73MB
207. 25 PCA - Principal Component Analysis and Manifold Learning/003 PCA Theory and Intuition - Part Two.mp419.06MB
208. 25 PCA - Principal Component Analysis and Manifold Learning/004 PCA - Manual Implementation in Python.mp495.14MB
209. 25 PCA - Principal Component Analysis and Manifold Learning/005 PCA - SciKit-Learn.mp474.1MB