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[CourseClub.ME].url |
122б |
[FCS Forum].url |
133б |
[FreeCourseSite.com].url |
127б |
[GigaCourse.Com].url |
49б |
001 Course Content.mp4 |
17.07Мб |
001 Introduction.mp4 |
38.11Мб |
001 Introduction.mp4 |
17.02Мб |
001 Learning Types.mp4 |
45.44Мб |
001 Python Source Codes.html |
1.23Кб |
001 Reading and Modifying a Dataset.mp4 |
154.55Мб |
001 Simple and Multiple Linear Regression Concepts.mp4 |
212.19Мб |
001 Supervised Learning Models - Introduction and Understanding the Data.mp4 |
233.75Мб |
002 K-means Concepts1.mp4 |
44.53Мб |
002 k-NN Concepts.mp4 |
48.03Мб |
002 Multiple Linear Regression - Model Development.mp4 |
75.59Мб |
002 NumPy1.mp4 |
37.49Мб |
002 Statistics1.mp4 |
34.04Мб |
002 Support Vector Regression - Model Tuning.mp4 |
125.61Мб |
002 What is Machine Learning_ Some Basic Terms.mp4 |
25.82Мб |
003 Evaluation Metrics - Concepts.mp4 |
49.47Мб |
003 K-means Concepts2.mp4 |
21.28Мб |
003 K-Means - Model Tuning.mp4 |
15.30Мб |
003 k-NN Model Development.mp4 |
140.65Мб |
003 NumPy2.mp4 |
56.91Мб |
003 Python Installation.html |
1.47Кб |
003 Statistics2.mp4 |
207.49Мб |
004 Evaluation Metrics - Implementation.mp4 |
159.90Мб |
004 K-means Model Development1.mp4 |
35.98Мб |
004 k-NN - Model Tuning.mp4 |
133.64Мб |
004 k-NN Training-Set and Test-Set Creation.mp4 |
228.42Мб |
004 NumPy3.mp4 |
84.52Мб |
004 Python IDE.mp4 |
7.51Мб |
004 Statistics3 - Covariance.mp4 |
107.48Мб |
005 Decision Tree Concepts.mp4 |
25.62Мб |
005 IDE Installation.mp4 |
22.28Мб |
005 K-means Model Development2.mp4 |
103.83Мб |
005 Missing Values1.mp4 |
129.64Мб |
005 NumPy4.mp4 |
56.56Мб |
005 Overfitting and Underfitting.mp4 |
72.09Мб |
005 Polynomial Linear Regression Concepts.mp4 |
26.39Мб |
006 Decision Tree Model Development.mp4 |
66.84Мб |
006 Installation of Required Libraries.mp4 |
70.78Мб |
006 K-means - Model Evaluation.mp4 |
102.40Мб |
006 Missing Values2.mp4 |
219.38Мб |
006 NumPy5.mp4 |
152.64Мб |
006 Polynomial Linear Regression Model Development.mp4 |
219.12Мб |
007 DBSCAN Concepts.mp4 |
26.85Мб |
007 Decision Tree - Cross Validation.mp4 |
54.64Мб |
007 NumPy6.mp4 |
134.50Мб |
007 Outlier Detection1.mp4 |
73.20Мб |
007 Random Forest Concepts.mp4 |
30.23Мб |
007 Spyder Interface.mp4 |
46.36Мб |
008 DBSCAN Model Development.mp4 |
86.87Мб |
008 Naive Bayes Concepts.mp4 |
59.23Мб |
008 Outlier Detection2.mp4 |
130.66Мб |
008 Pandas1.mp4 |
95.61Мб |
008 Random Forest Model Development.mp4 |
246.24Мб |
009 Hierarchical Clustering Concepts.mp4 |
24.29Мб |
009 Naive Bayes Model Development.mp4 |
58.95Мб |
009 Outlier Detection3.mp4 |
31.01Мб |
009 Pandas2.mp4 |
116.94Мб |
009 Support Vector Regression Concepts.mp4 |
26.97Мб |
010 Concatenation.mp4 |
65.88Мб |
010 Hierarchical Clustering Model Development.mp4 |
145.89Мб |
010 Logistic Regression Concepts.mp4 |
10.85Мб |
010 Pandas3.mp4 |
117.83Мб |
010 Support Vector Regression Model Development.mp4 |
121.01Мб |
011 Dummy Variable.mp4 |
57.59Мб |
011 Logistic Regression Model Development.mp4 |
112.12Мб |
011 Pandas4.mp4 |
203.04Мб |
012 Model Evaluation Concepts.mp4 |
83.47Мб |
012 Normalization.mp4 |
186.87Мб |
012 Visualization with Matplotlib1.mp4 |
99.45Мб |
013 Model Evaluation - Calculating with Python.mp4 |
174.04Мб |
013 Visualization with Matplotlib2.mp4 |
205.24Мб |
014 Visualization with Matplotlib3.mp4 |
188.84Мб |
015 Visualization with Matplotlib4.mp4 |
142.99Мб |
016 Visualization with Matplotlib5.mp4 |
129.25Мб |
018 Data_Set.txt |
580б |
024 Data_Set.txt |
580б |
033 Data_New.txt |
201б |
Chapter2.Matplotlib.py |
2.95Кб |
Chapter2.NumPy.py |
1.11Кб |
Chapter2.Pandas.py |
1.12Кб |
Chapter3.Preprocessing.py |
2.57Кб |
Chapter5.Classification.py |
3.31Кб |
Chapter6.Regression.py |
4.32Кб |
Chapter7.Clustering.py |
1.44Кб |
Chapter8.Parameter Tuning.py |
1.95Кб |
Readme.txt |
70б |
Readme.txt |
70б |