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7 - Matplotlib Library.mp4
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1 - Introduction to Machine Learning
10 - Support Vector Machines
11 - K Nearest Neighbors for Classification and Regression
12 - Decision Tree Classifier Algorithm
13 - Random Forest Classifier Algorithm
14 - Naive Bayes Algorithm
15 - Resources
Notebooks
16 - KMeans Clustering
2 - Types of Machine Learning
3 - The Machine Learning Pipeline
4 - Numpy Library
5 - Pandas Library
6 - Analysis of Datasets using Pandas and Matplotlib Library
7 - The Scikitlearn Library and Preprocessing Techniques
30 - 29 Working with Iris Dataset from sklearn.mp4
MP4
194.1 MB
31 - 30 Binarization.mp4
MP4
43.2 MB
32 - 31 Feature Scaling.mp4
MP4
46.9 MB
8 - Supervised Learning Linear Regression
33 - 32 Analysis of Linear Regression.mp4
MP4
73.8 MB
34 - Use of Gradient Descent Optimizer.mp4
MP4
33 MB
35 - The Gradient Descent Optimizer Algorithm.mp4
MP4
81.1 MB
36 - 33 Demand vs Price Problem to understand Linear Regression.mp4
MP4
71.3 MB
37 - 34 Implementation of Linear Regression I.mp4
MP4
75.8 MB
38 - 35 Implementation of Linear Regression II.mp4
MP4
48.2 MB
39 - 36 Visualizing the LBF using matplotlib.mp4
MP4
23.3 MB
9 - Logistic Regression for Classification Problems
40 - 37 Why does Linear Regression fail for a classification problem.mp4
MP4
39.1 MB
41 - 38 The Sigmoid function in Logistic Regression.mp4
MP4
24 MB
42 - 39 The Confusion Matrix.mp4
MP4
60.1 MB
43 - 40 Implementation of Logistic Regression I.mp4
MP4
150.3 MB
44 - 41 Creating an heatmap of the confusion matrix.mp4
MP4
22.1 MB
15 matching files, 146 total files.
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7 - Matplotlib Library.mp4
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16.7 MB
1 matching file, 68 total files.
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7. Matplotlib Library Tutorial 5.mp4
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7. Matplotlib Library Tutorial 5.mp4
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36.2 MB
1 matching file, 224 total files.
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