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1. Matplotlib Library Introduction.mp4

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Udemy - Machine Learning Using Python Programming

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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

27 - 26 Loading the dataset Important.mp4

MP4

32.3 MB

28 - 27 Analysis of Datasets I.mp4

MP4

42 MB

29 - 28 Analysis of Datasets by Plotting II.mp4

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71.1 MB

7 - The Scikitlearn Library and Preprocessing Techniques

30 - 29 Working with Iris Dataset from sklearn.mp4

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194.1 MB

31 - 30 Binarization.mp4

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43.2 MB

32 - 31 Feature Scaling.mp4

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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

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33 MB

35 - The Gradient Descent Optimizer Algorithm.mp4

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81.1 MB

36 - 33 Demand vs Price Problem to understand Linear Regression.mp4

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71.3 MB

37 - 34 Implementation of Linear Regression I.mp4

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75.8 MB

38 - 35 Implementation of Linear Regression II.mp4

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48.2 MB

39 - 36 Visualizing the LBF using matplotlib.mp4

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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