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Udemy - Machine Learning Using Python Programming

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Udemy - Machine Learning Using Python Programming

Torrent Contents Size: 2.8 GB

Udemy - Machine Learning Using Python Programming
Bonus Resources.txt
TXT
102.4 B
Get Bonus Downloads Here.url
URL
204.8 B
~Get Your Files Here !
1 - Introduction to Machine Learning
1 - Introduction to Machine Learning English.vtt
VTT
6.7 KB
1 - Introduction to Machine Learning.mp4
MP4
30.6 MB
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
64 - Download all the notebooks and datasets here.html
HTML
102.4 B
Notebooks
16 - KMeans Clustering
2 - Types of Machine Learning
3 - The Machine Learning Pipeline
10 - 10 Introduction to iPython Environment English.vtt
VTT
10.4 KB
10 - 10 Introduction to iPython Environment.mp4
MP4
51.6 MB
11 - Important Libraries in Python.html
HTML
307.2 B
4 - Numpy Library
12 - 11Creating a numpy array English.vtt
VTT
17 KB
12 - 11Creating a numpy array.mp4
MP4
49.6 MB
13 - 12 Processing the numpy arrays English.vtt
VTT
16.5 KB
13 - 12 Processing the numpy arrays.mp4
MP4
58.2 MB
14 - 13 Accessing Columns from Numpy Matrices English.vtt
VTT
5 KB
14 - 13 Accessing Columns from Numpy Matrices.mp4
MP4
18.6 MB
15 - 14 Statistical methods in Numpy English.vtt
VTT
14.7 KB
15 - 14 Statistical methods in Numpy.mp4
MP4
56.2 MB
16 - 15 Matrix Operations in Numpy English.vtt
VTT
13.6 KB
16 - 15 Matrix Operations in Numpy.mp4
MP4
53.5 MB
17 - 16 Iterating through the numpy array English.vtt
VTT
5.9 KB
17 - 16 Iterating through the numpy array.mp4
MP4
24.5 MB
5 - Pandas Library
18 - 17 An Intuition on Pandas Dataframe and Series English.vtt
VTT
6.5 KB
18 - 17 An Intuition on Pandas Dataframe and Series.mp4
MP4
28.1 MB
19 - 18 Using numpy arrays to create Pandas Series English.vtt
VTT
8.1 KB
19 - 18 Using numpy arrays to create Pandas Series.mp4
MP4
28.8 MB
20 - 19 Using dictionary to create Pandas Series English.vtt
VTT
7.2 KB
20 - 19 Using dictionary to create Pandas Series.mp4
MP4
26.4 MB
21 - 20 Using a scalar to create Pandas Series English.vtt
VTT
2.1 KB
21 - 20 Using a scalar to create Pandas Series.mp4
MP4
10.5 MB
22 - 21 Series Processing English.vtt
VTT
1.5 KB
22 - 21 Series Processing.mp4
MP4
8.5 MB
23 - 22 Creating Pandas Dataframe from series English.vtt
VTT
7.1 KB
23 - 22 Creating Pandas Dataframe from series.mp4
MP4
24.5 MB
24 - 23 Using lists of data to create a Pandas Dataframe English.vtt
VTT
5 KB
24 - 23 Using lists of data to create a Pandas Dataframe.mp4
MP4
21 MB
25 - 24 Another approach to create Dataframes English.vtt
VTT
4.2 KB
25 - 24 Another approach to create Dataframes.mp4
MP4
21.6 MB
26 - 25 Directly creating a pandas dataframe from numpy arrays English.vtt
VTT
1.8 KB
26 - 25 Directly creating a pandas dataframe from numpy arrays.mp4
MP4
8.2 MB
6 - Analysis of Datasets using Pandas and Matplotlib Library
27 - 26 Loading the dataset Important English.vtt
VTT
6.7 KB
27 - 26 Loading the dataset Important.mp4
MP4
32.3 MB
28 - 27 Analysis of Datasets I English.vtt
VTT
7.3 KB
28 - 27 Analysis of Datasets I.mp4
MP4
42 MB
29 - 28 Analysis of Datasets by Plotting II English.vtt
VTT
17.1 KB
29 - 28 Analysis of Datasets by Plotting II.mp4
MP4
71.1 MB
7 - The Scikitlearn Library and Preprocessing Techniques
30 - 29 Working with Iris Dataset from sklearn English.vtt
VTT
32.1 KB
30 - 29 Working with Iris Dataset from sklearn.mp4
MP4
194.1 MB
31 - 30 Binarization English.vtt
VTT
9.6 KB
31 - 30 Binarization.mp4
MP4
43.2 MB
32 - 31 Feature Scaling English.vtt
VTT
9.4 KB
32 - 31 Feature Scaling.mp4
MP4
46.9 MB
8 - Supervised Learning Linear Regression
33 - 32 Analysis of Linear Regression English.vtt
VTT
21.3 KB
33 - 32 Analysis of Linear Regression.mp4
MP4
73.8 MB
34 - Use of Gradient Descent Optimizer English.vtt
VTT
10.7 KB
34 - Use of Gradient Descent Optimizer.mp4
MP4
33 MB
35 - The Gradient Descent Optimizer Algorithm English.vtt
VTT
27.6 KB
35 - The Gradient Descent Optimizer Algorithm.mp4
MP4
81.1 MB
36 - 33 Demand vs Price Problem to understand Linear Regression English.vtt
VTT
11.6 KB
36 - 33 Demand vs Price Problem to understand Linear Regression.mp4
MP4
71.3 MB
37 - 34 Implementation of Linear Regression I English.vtt
VTT
12.7 KB
37 - 34 Implementation of Linear Regression I.mp4
MP4
75.8 MB
38 - 35 Implementation of Linear Regression II English.vtt
VTT
6.9 KB
38 - 35 Implementation of Linear Regression II.mp4
MP4
48.2 MB
39 - 36 Visualizing the LBF using matplotlib English.vtt
VTT
4.8 KB
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 English.vtt
VTT
11.2 KB
40 - 37 Why does Linear Regression fail for a classification problem.mp4
MP4
39.1 MB
41 - 38 The Sigmoid function in Logistic Regression English.vtt
VTT
6.3 KB
41 - 38 The Sigmoid function in Logistic Regression.mp4
MP4
24 MB
42 - 39 The Confusion Matrix English.vtt
VTT
14.2 KB
42 - 39 The Confusion Matrix.mp4
MP4
60.1 MB
43 - 40 Implementation of Logistic Regression I English.vtt
VTT
20.2 KB
43 - 40 Implementation of Logistic Regression I.mp4
MP4
150.3 MB
44 - 41 Creating an heatmap of the confusion matrix English.vtt
VTT
4.1 KB
44 - 41 Creating an heatmap of the confusion matrix.mp4
MP4
22.1 MB
6 - 6 The Machine Learning Pipeline Data Collection English.vtt
VTT
9.4 KB
6 - 6 The Machine Learning Pipeline Data Collection.mp4
MP4
43.8 MB
7 - 7 Importance of Data Prepocessing English.vtt
VTT
3.4 KB
7 - 7 Importance of Data Prepocessing.mp4
MP4
17.9 MB
8 - 8 Importance of Feature Selection and Feature Engineering English.vtt
VTT
10.8 KB
8 - 8 Importance of Feature Selection and Feature Engineering.mp4
MP4
52.5 MB
9 - 9 The Machine Learning Terminologies English.vtt
VTT
7.5 KB
9 - 9 The Machine Learning Terminologies.mp4
MP4
36.5 MB
4 - 4 Difference between Supervised and Unsupervised Learning English.vtt
VTT
9.1 KB
4 - 4 Difference between Supervised and Unsupervised Learning.mp4
MP4
39.2 MB
5 - 5 Algorithms in Supervised and Unsupervised Learning English.vtt
VTT
3.8 KB
5 - 5 Algorithms in Supervised and Unsupervised Learning.mp4
MP4
12.9 MB
65 - The complete flow of KMeans Clustering English.vtt
VTT
19.3 KB
65 - The complete flow of KMeans Clustering.mp4
MP4
89 MB
66 - The concept of Overfitting and Underfitting English.vtt
VTT
18.1 KB
66 - The concept of Overfitting and Underfitting.mp4
MP4
78.9 MB
Decision Tree.ipynb
IPYNB
35.6 KB
KNN Regression.ipynb
IPYNB
35.8 KB
KNN.ipynb
IPYNB
4.5 KB
LinearRegression.ipynb
IPYNB
38.1 KB
LogisticRegression.ipynb
IPYNB
14.9 KB
MyFirstNotebook.ipynb
IPYNB
16.9 KB
Naive Bayes' Algorithm.ipynb
IPYNB
9 KB
Naive Bayes' Classifier.ipynb
IPYNB
26.3 KB
Preprocessing Techniques.ipynb
IPYNB
2.5 KB
Product.csv
CSV
10.7 KB
Random Forest Classifier.ipynb
IPYNB
9 KB
SVM.ipynb
IPYNB
8.3 KB
Salary_Data.csv
CSV
409.6 B
car.csv
CSV
10.3 KB
62 - 59 Naive Bayes Classifier English.vtt
VTT
13.6 KB
62 - 59 Naive Bayes Classifier.mp4
MP4
61.4 MB
63 - 60 Implementing Naive Bayes Classifier for wine dataset English.vtt
VTT
16.9 KB
63 - 60 Implementing Naive Bayes Classifier for wine dataset.mp4
MP4
141.7 MB
60 - 56 Ensemble Techniques Random Forest Classifier English.vtt
VTT
8.9 KB
60 - 56 Ensemble Techniques Random Forest Classifier.mp4
MP4
41.4 MB
61 - 57 Implementing Random Classifier in Python English.vtt
VTT
7.2 KB
61 - 57 Implementing Random Classifier in Python.mp4
MP4
54.3 MB
54 - 51 Introduction to Decision Trees English.vtt
VTT
921.6 B
54 - 51 Introduction to Decision Trees.mp4
MP4
1.9 MB
55 - 52 Basic Tree Terminologies English.vtt
VTT
18.8 KB
55 - 52 Basic Tree Terminologies.mp4
MP4
90 MB
56 - 53 Example 1 for Decision Tree English.vtt
VTT
3.4 KB
56 - 53 Example 1 for Decision Tree.mp4
MP4
14.3 MB
57 - 531 Example 2 for Decision Tree English.vtt
VTT
2.6 KB
57 - 531 Example 2 for Decision Tree.mp4
MP4
8.8 MB
58 - 54 Implementation of Decision Tree Algorithm I English.vtt
VTT
3.5 KB
58 - 54 Implementation of Decision Tree Algorithm I.mp4
MP4
25.5 MB
59 - 55 Implementation of Decision Tree Algorithm II English.vtt
VTT
2.7 KB
59 - 55 Implementation of Decision Tree Algorithm II.mp4
MP4
22.5 MB
48 - 45 Drawing the classification diagrams English.vtt
VTT
7.3 KB
48 - 45 Drawing the classification diagrams.mp4
MP4
30.5 MB
49 - 46 Introduction to KNearest Neighbors English.vtt
VTT
3.8 KB
49 - 46 Introduction to KNearest Neighbors.mp4
MP4
16.2 MB
50 - 47 Steps in KNN Classification and KNN Regression English.vtt
VTT
8.1 KB
50 - 47 Steps in KNN Classification and KNN Regression.mp4
MP4
35.9 MB
51 - 48 Implementing KNN Classification using sklearn English.vtt
VTT
6.7 KB
51 - 48 Implementing KNN Classification using sklearn.mp4
MP4
53.6 MB
52 - 49 Implementing KNN Regression Algorithm in Python I English.vtt
VTT
5 KB
52 - 49 Implementing KNN Regression Algorithm in Python I.mp4
MP4
32.5 MB
53 - 50 Implementing KNN Regression Algorithm in Python II English.vtt
VTT
2 KB
53 - 50 Implementing KNN Regression Algorithm in Python II.mp4
MP4
18.3 MB
45 - 42 Understanding Support Vector Machines and Hyperplanes English.vtt
VTT
13.5 KB
45 - 42 Understanding Support Vector Machines and Hyperplanes.mp4
MP4
52.6 MB
46 - 43 Understanding the Kernels of SVM English.vtt
VTT
3 KB
46 - 43 Understanding the Kernels of SVM.mp4
MP4
13.7 MB
47 - 44 Implementing Support Vector Classifiers in Python English.vtt
VTT
8.2 KB
47 - 44 Implementing Support Vector Classifiers in Python.mp4
MP4
52.3 MB
2 - 2 Features of Machine Learning English.vtt
VTT
3 KB
2 - 2 Features of Machine Learning.mp4
MP4
17.3 MB
3 - 3 Traditional Programming vs Machine Learning English.vtt
VTT
7.2 KB
3 - 3 Traditional Programming vs Machine Learning.mp4
MP4
21.9 MB

Description

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