|
Please note that this page does not hosts or makes available any of the listed filenames. You
cannot download any of those files from here.
|
| [TGx]Downloaded from torrentgalaxy.to .txt |
585B |
| 0 |
217B |
| 1 |
104B |
| 1.1 car data.csv |
16.81KB |
| 1.1 Handling Missing Values (1).pptx |
620.89KB |
| 1.1 Introduction and key learning outcomes.pptx |
372.57KB |
| 1.1 Machine Learning Introduction.pptx |
506.69KB |
| 1.1 Matplotlib Intro and Getting started.ipynb |
15.15KB |
| 1.1 Numpy Introduction and Installation.ipynb |
1.65KB |
| 1.1 Pandas Intro and Installation.ipynb |
1.97KB |
| 1.2 Car Price Prediction.ipynb |
41.92KB |
| 1.2 Missing Values.ipynb |
14.91KB |
| 1.3 Car Price Prediction.pptx |
456.16KB |
| 1.3 Placement_Data_Full_Class.csv |
19.25KB |
| 1. Car Price Prediction.mp4 |
142.94MB |
| 1. Course Introduction and Key Learning Outcomes.mp4 |
9.27MB |
| 1. Data Types and Variables.mp4 |
38.23MB |
| 1. Handling Missing Values.mp4 |
64.54MB |
| 1. Introduction and Installation.mp4 |
35.96MB |
| 1. Introduction to Machine Learning.mp4 |
36.68MB |
| 1. Introduction to Matplotlib.mp4 |
29.50MB |
| 1. Pandas Introduction and Installation.mp4 |
26.11MB |
| 10 |
39.23KB |
| 10.1 Decision Tree Algorithm.pptx |
463.37KB |
| 10.2 Decision Tree Practical.ipynb |
6.37KB |
| 10.3 User_Data.csv |
10.67KB |
| 10. Decision Tree.mp4 |
73.76MB |
| 11 |
232.16KB |
| 11.1 Random Forest Algorithm.pptx |
400.85KB |
| 11.2 Random Forest Practical.ipynb |
6.66KB |
| 11.3 User_Data.csv |
10.67KB |
| 11. Random Forest.mp4 |
48.35MB |
| 12 |
108.37KB |
| 12.1 K Means Practical.ipynb |
60.72KB |
| 12.2 K-Means Clustering Algorithm.pptx |
588.47KB |
| 12.3 Mall_Customers.csv |
4.67KB |
| 12. K Means Clustering.mp4 |
66.02MB |
| 13 |
187.05KB |
| 13.1 GridSearch CV.ipynb |
5.58KB |
| 13.2 GridSearchCV.pptx |
487.17KB |
| 13. Grid Search CV.mp4 |
74.03MB |
| 14 |
68.49KB |
| 14.1 Machine learning Pipeline.pptx |
415.53KB |
| 14.2 ML Pipeline.ipynb |
10.54KB |
| 14. Machine Learning Pipeline.mp4 |
56.82MB |
| 15 |
7.55KB |
| 16 |
270.55KB |
| 17 |
24.36KB |
| 18 |
79.61KB |
| 19 |
150.60KB |
| 2 |
551B |
| 2.1 airport.csv |
47.24KB |
| 2.1 Creating Arrays Numpy.ipynb |
4.49KB |
| 2.1 Customer Segmentation using K-means Clustering.pptx |
521.41KB |
| 2.1 Different types of plots in Matplotlib.ipynb |
29.33KB |
| 2.1 Pandas Series.ipynb |
1.54KB |
| 2.1 Supervised Machine Learning.pptx |
364.86KB |
| 2.2 Customer Segmentation.ipynb |
60.30KB |
| 2.2 Feature Encoding.ipynb |
36.83KB |
| 2.3 Feature Encoding.pptx |
492.31KB |
| 2.3 Mall_Customers.csv |
3.89KB |
| 2.4 Iris.csv |
4.99KB |
| 2. ChatGPT Introduction.mp4 |
26.37MB |
| 2. Create a Numpy Array.mp4 |
52.99MB |
| 2. Customer Segmentation.mp4 |
119.90MB |
| 2. Feature Encoding.mp4 |
67.70MB |
| 2. Series.mp4 |
17.97MB |
| 2. Supervised Machine Learning.mp4 |
27.40MB |
| 2. Type of Plots in Matplotlib.mp4 |
47.18MB |
| 2. User Input.mp4 |
12.49MB |
| 20 |
329.74KB |
| 21 |
348.55KB |
| 22 |
279.08KB |
| 23 |
331.61KB |
| 24 |
67.48KB |
| 25 |
37.71KB |
| 26 |
198.85KB |
| 27 |
356.00KB |
| 28 |
4.28KB |
| 29 |
236.72KB |
| 3 |
1.03KB |
| 3.1 Feature Scaling.ipynb |
117.33KB |
| 3.1 Pandas DataFrame Practical.ipynb |
3.19KB |
| 3.1 Seaborn.ipynb |
67.72KB |
| 3.1 Wine Quality Prediction.ipynb |
90.30KB |
| 3.2 Wine Quality Prediction.pptx |
480.46KB |
| 3.3 winequality-red.csv |
98.58KB |
| 3. ChatGPT Practical.mp4 |
17.96MB |
| 3. DataFrame.mp4 |
26.53MB |
| 3. Feature Scaling.mp4 |
56.89MB |
| 3. Lists.mp4 |
36.43MB |
| 3. Seaborn.mp4 |
63.92MB |
| 3. Shape and Reshape.mp4 |
50.07MB |
| 3. Unsupervised Machine Learning.mp4 |
22.54MB |
| 3. Wine Quality Prediction.mp4 |
95.46MB |
| 30 |
104.44KB |
| 31 |
75.30KB |
| 32 |
132.66KB |
| 33 |
22.75KB |
| 34 |
209.33KB |
| 35 |
70.57KB |
| 36 |
299.31KB |
| 37 |
293.13KB |
| 38 |
32.38KB |
| 39 |
36.87KB |
| 4 |
1.25KB |
| 4.1 airport.csv |
47.24KB |
| 4.1 Array Indexing.ipynb |
3.96KB |
| 4.2 Read CSV.ipynb |
17.25KB |
| 4. Conditional Statements.mp4 |
19.71MB |
| 4. Indexing.mp4 |
35.81MB |
| 4. Read_CSV.mp4 |
18.71MB |
| 4. Train Test Split.mp4 |
14.59MB |
| 40 |
54.33KB |
| 41 |
8.67KB |
| 42 |
232.26KB |
| 43 |
403.11KB |
| 44 |
75.41KB |
| 45 |
469.21KB |
| 46 |
482.98KB |
| 47 |
502.59KB |
| 48 |
504.20KB |
| 5 |
565B |
| 5.1 airport.csv |
47.24KB |
| 5.1 Array Iterating Practical.ipynb |
2.11KB |
| 5.1 Regression Analysis.pptx |
541.02KB |
| 5.2 Analyzing DataFrames.ipynb |
60.11KB |
| 5. Analyze Pandas DataFrames.mp4 |
48.42MB |
| 5. Iterating.mp4 |
25.80MB |
| 5. Loops.mp4 |
27.77MB |
| 5. Regression Analysis.mp4 |
55.43MB |
| 6 |
509B |
| 6.1 Array Slicing.ipynb |
4.26KB |
| 6.1 Linear Regression.ipynb |
22.04KB |
| 6.2 Linear Regression.pptx |
360.12KB |
| 6.3 Salary_Data.csv |
454B |
| 6. Linear Regression.mp4 |
52.24MB |
| 6. Slicing.mp4 |
43.16MB |
| 7 |
2.73KB |
| 7.1 Logistic Regression Practical.ipynb |
25.33KB |
| 7.1 Searching and Sorting numpy array prac.ipynb |
3.94KB |
| 7.2 Logistic Regression.pptx |
405.18KB |
| 7.3 User_Data.csv |
10.67KB |
| 7. Logistic Regression.mp4 |
74.71MB |
| 7. Searching and Sorting.mp4 |
32.65MB |
| 8 |
1.55KB |
| 8.1 K Nearest Neighbors(KNN).pptx |
519.80KB |
| 8.2 KNN Practical.ipynb |
60.80KB |
| 8.3 User_Data.csv |
10.67KB |
| 8. KNN.mp4 |
79.02MB |
| 9 |
240B |
| 9.1 Support Vector Machine (SVM).pptx |
554.79KB |
| 9.2 SVM Practical.ipynb |
8.68KB |
| 9.3 User_Data.csv |
10.67KB |
| 9. SVM.mp4 |
63.77MB |
| TutsNode.net.txt |
63B |