[FreeTutorials.Eu] Udemy - Machine Learning A-Z Become Kaggle Master

Creation Time	Nov. 1, 2023, 2:22 p.m.
Last Access Time	Jan. 19, 2026, 5:55 p.m.
File Size	14.0 GB
Keywords	FreeTutorials - Kaggle Udemy Machine A-Z Master Learning Eu Become
Total Requests	124
Total Files	542
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

17. Logistic Regression/4. Case Study.mp4 198.2 MB
7. Data Visualisation/2. Seaborn.mp4 184.7 MB
15. Model Selection Part1/4. Gridsearch Case study Part2.mp4 178.9 MB
7. Data Visualisation/1. Matplotlib.mp4 172.8 MB
2. Numpy/3. Numpy Operations Part2.mp4 170.0 MB
18. Support Vector Machine (SVM)/14. Case Study 4.mp4 164.4 MB
4. Some Fun With Maths/1. Linear Algebra Vectors.mp4 162.4 MB
23. Dimension Reduction/1. Introduction.mp4 156.7 MB
20. Ensembling/16. Case Study Part1.mp4 141.5 MB
9. Simple Linear Regression/7. LR Case Study Part1.mp4 137.5 MB
26. Project Kaggle/2. Playing With The Data.mp4 137.1 MB
20. Ensembling/17. Case Study Part2.mp4 136.7 MB
26. Project Kaggle/17. Building Machine Learning model part2.mp4 135.2 MB
10. Multiple Linear Regression/9. Case Study Part4.mp4 132.2 MB
2. Numpy/2. Numpy Operations Part1.mp4 128.7 MB
19. Decision Tree/9. DT Case Study Part1.mp4 125.5 MB
15. Model Selection Part1/3. Gridsearch Case study Part1.mp4 124.2 MB
26. Project Kaggle/16. Building Machine Learning model part1.mp4 124.0 MB
23. Dimension Reduction/5. Case Study Part2.mp4 123.1 MB
26. Project Kaggle/5. Train, Test And Cross Validation Split.mp4 116.2 MB
14. Model Performance Metrics/1. Performance Metrics Part1.mp4 113.8 MB
7. Data Visualisation/3. Case Study.mp4 113.2 MB
26. Project Kaggle/3. Translating the Problem In Machine Learning World.mp4 113.0 MB
1. Python Fundamentals/5. Variables in Python.mp4 110.5 MB
24. Advanced Machine Learning Algorithms/8. Case Study.mp4 106.2 MB
1. Python Fundamentals/11. String Part1.mp4 106.0 MB
21. Model Selection Part2/1. Model Selection Part1.mp4 104.3 MB
25. Deep Learning/6. Neural Network Playground.mp4 103.7 MB
10. Multiple Linear Regression/3. Case Study part2.mp4 98.4 MB
23. Dimension Reduction/2. PCA.mp4 98.4 MB
26. Project Kaggle/4. Dealing with Text Data.mp4 98.1 MB
23. Dimension Reduction/3. Maths Behind PCA.mp4 96.8 MB
22. Unsupervised Learning/9. Case Study Part1.mp4 95.8 MB
19. Decision Tree/10. DT Case Study Part2.mp4 95.7 MB
16. Naive Bayes/9. Case Study 1.mp4 95.5 MB
4. Some Fun With Maths/2. Linear Algebra Matrix Part1.mp4 95.3 MB
1. Python Fundamentals/1. Introduction to the course.mp4 93.9 MB
26. Project Kaggle/1. Introduction to the Problem Statement.mp4 93.4 MB
14. Model Performance Metrics/2. Performance Metrics Part2.mp4 90.5 MB
1. Python Fundamentals/2. Introduction to Kaggle.mp4 90.1 MB
18. Support Vector Machine (SVM)/11. Case Study 2.mp4 90.0 MB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/4. Building Model Part2.mp4 87.8 MB
1. Python Fundamentals/14. List Part2.mp4 87.3 MB
1. Python Fundamentals/10. Functions.mp4 85.6 MB
26. Project Kaggle/6. Understanding Evaluation Matrix Log Loss.mp4 85.5 MB
13. KNN/11. Classification Case1.mp4 84.2 MB
10. Multiple Linear Regression/2. Case Study part1.mp4 83.0 MB
8. Exploratory Data Analysis/10. Univariate Analysis Part1.mp4 82.8 MB
1. Python Fundamentals/3. Installation of Python and Anaconda.mp4 82.3 MB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/2. Playing With Data.mp4 81.4 MB
16. Naive Bayes/3. Practical Example from NB with One Column.mp4 80.6 MB
4. Some Fun With Maths/3. Linear Algebra Matrix Part2.mp4 78.0 MB
1. Python Fundamentals/9. for while Loop.mp4 77.8 MB
8. Exploratory Data Analysis/8. Data Cleaning part1.mp4 76.2 MB
20. Ensembling/18. Case Study Part3.mp4 75.4 MB
16. Naive Bayes/10. Case Study 2 Part1.mp4 74.6 MB
18. Support Vector Machine (SVM)/7. SVM Case Study Part1.mp4 74.1 MB
1. Python Fundamentals/15. List Part3.mp4 73.6 MB
16. Naive Bayes/1. Introduction to Naive Bayes.mp4 73.4 MB
20. Ensembling/5. Case study.mp4 73.1 MB
10. Multiple Linear Regression/7. Case Study Part2.mp4 72.9 MB
26. Project Kaggle/12. Significance of first categorical column.mp4 71.7 MB
12. Gradient Descent/8. Gradient Descent case study.mp4 71.7 MB
20. Ensembling/2. Bagging.mp4 71.2 MB
18. Support Vector Machine (SVM)/10. Kernel Part2.mp4 71.1 MB
26. Project Kaggle/9. First Categorical column analysis.mp4 71.1 MB
13. KNN/10. Case Study.mp4 70.7 MB
1. Python Fundamentals/20. Comprehentions.mp4 70.5 MB
10. Multiple Linear Regression/4. Case Study part3.mp4 68.7 MB
10. Multiple Linear Regression/6. Case Study Part1.mp4 68.5 MB
26. Project Kaggle/7. Building A Worst Model.mp4 68.5 MB
1. Python Fundamentals/17. Tuples.mp4 67.3 MB
26. Project Kaggle/14. Third Categorical column.mp4 66.7 MB
10. Multiple Linear Regression/8. Case Study Part3.mp4 66.6 MB
3. Pandas/3. DataFrame.mp4 66.2 MB
18. Support Vector Machine (SVM)/8. SVM Case Study Part2.mp4 66.2 MB
18. Support Vector Machine (SVM)/3. Hyperplane Part2.mp4 65.3 MB
24. Advanced Machine Learning Algorithms/4. Optimal Solution.mp4 65.2 MB
10. Multiple Linear Regression/11. Case Study Part6 (RFE).mp4 64.1 MB
1. Python Fundamentals/8. If else Loop.mp4 64.0 MB
1. Python Fundamentals/16. List Part4.mp4 63.9 MB
25. Deep Learning/5. Multi Layered Perceptron.mp4 63.8 MB
16. Naive Bayes/2. Bayes Theorem.mp4 63.1 MB
25. Deep Learning/3. History.mp4 61.9 MB
1. Python Fundamentals/19. Dictionaries.mp4 61.6 MB
3. Pandas/2. Series.mp4 61.5 MB
22. Unsupervised Learning/10. Case Study Part2.mp4 61.3 MB
18. Support Vector Machine (SVM)/13. Case Study 3 Part2.mp4 61.3 MB
12. Gradient Descent/1. Pre-Req For Gradient Descent Part1.mp4 61.2 MB
8. Exploratory Data Analysis/11. Univariate Analysis Part2.mp4 60.9 MB
8. Exploratory Data Analysis/13. Bivariate Analysis.mp4 60.6 MB
26. Project Kaggle/1.1 training.zip.zip 60.0 MB
16. Naive Bayes/4. Practical Example from NB with Multiple Columns.mp4 59.8 MB
3. Pandas/7. loc and iloc.mp4 59.4 MB
22. Unsupervised Learning/1. Introduction to Clustering.mp4 59.1 MB
26. Project Kaggle/8. Evaluating Worst ML Model.mp4 58.9 MB
18. Support Vector Machine (SVM)/1. Introduction.mp4 58.7 MB
9. Simple Linear Regression/4. How LR Works.mp4 58.7 MB
6. Hypothesis Testing/6. z Table.mp4 58.6 MB
1. Python Fundamentals/18. Sets.mp4 58.2 MB
22. Unsupervised Learning/3. Kmeans.mp4 57.7 MB
13. KNN/4. Accuracy of KNN.mp4 57.2 MB
18. Support Vector Machine (SVM)/12. Case Study 3 Part1.mp4 56.0 MB
16. Naive Bayes/7. Laplace Smoothing.mp4 55.3 MB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/3. Building Model Part1.mp4 55.1 MB
5. Inferential Statistics/2. Probability Theory.mp4 54.8 MB
16. Naive Bayes/5. Naive Bayes On Text Data Part1.mp4 54.7 MB
26. Project Kaggle/10. Response encoding and one hot encoder.mp4 54.7 MB
13. KNN/1. Introduction to Classification.mp4 54.1 MB
7. Data Visualisation/4. Seaborn On Time Series Data.mp4 54.1 MB
22. Unsupervised Learning/4. Maths Behind Kmeans.mp4 53.8 MB
8. Exploratory Data Analysis/7. Data Sourcing and Cleaning part6.mp4 53.7 MB
20. Ensembling/11. Adaboost Case Study.mp4 53.7 MB
9. Simple Linear Regression/8. LR Case Study Part2.mp4 53.4 MB
13. KNN/13. Classification Case3.mp4 53.0 MB
9. Simple Linear Regression/5. Some Fun With Maths Behind LR.mp4 52.8 MB
9. Simple Linear Regression/6. R Square.mp4 52.5 MB
13. KNN/12. Classification Case2.mp4 52.2 MB
15. Model Selection Part1/1. Model Creation Case1.mp4 52.1 MB
22. Unsupervised Learning/6. Kmeans plus.mp4 51.8 MB
26. Project Kaggle/21. Building Machine Learning model part6.mp4 50.8 MB
26. Project Kaggle/15. Data pre-processing before building machine learning model.mp4 50.6 MB
3. Pandas/6. Indexes.mp4 50.1 MB
18. Support Vector Machine (SVM)/9. Kernel Part1.mp4 49.2 MB
25. Deep Learning/2. Introduction.mp4 48.8 MB
24. Advanced Machine Learning Algorithms/6. Regularization.mp4 48.6 MB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/5. Building Model Part3.mp4 48.5 MB
26. Project Kaggle/11. Laplace Smoothing and Calibrated classifier.mp4 48.2 MB
13. KNN/5. Effectiveness of KNN.mp4 48.2 MB
13. KNN/6. Distance Metrics.mp4 47.9 MB
13. KNN/3. Introduction to KNN.mp4 47.1 MB
3. Pandas/10. groupby.mp4 46.9 MB
9. Simple Linear Regression/9. LR Case Study Part3.mp4 46.4 MB
16. Naive Bayes/6. Naive Bayes On Text Data Part2.mp4 46.1 MB
10. Multiple Linear Regression/10. Case Study Part5.mp4 45.7 MB
26. Project Kaggle/13. Second Categorical column.mp4 45.7 MB
23. Dimension Reduction/4. Case Study Part1.mp4 45.5 MB
24. Advanced Machine Learning Algorithms/3. Example Part2.mp4 45.1 MB
17. Logistic Regression/2. Sigmoid Function.mp4 44.3 MB
19. Decision Tree/4. Gini Index.mp4 44.2 MB
3. Pandas/5. Operations Part2.mp4 44.1 MB
3. Pandas/8. Reading CSV.mp4 42.5 MB
26. Project Kaggle/20. Building Machine Learning model part5.mp4 41.9 MB
8. Exploratory Data Analysis/14. Derived Columns.mp4 41.9 MB
17. Logistic Regression/3. Log Odds.mp4 41.8 MB
20. Ensembling/9. Adaboost Part1.mp4 41.5 MB
21. Model Selection Part2/2. Model Selection Part2.mp4 41.3 MB
13. KNN/14. Classification Case4.mp4 41.1 MB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/1. Introduction to the Problem Statement.mp4 40.8 MB
19. Decision Tree/2. Example of DT.mp4 40.6 MB
19. Decision Tree/8. Preventing Overfitting Issues in DT.mp4 40.3 MB
13. KNN/2. Defining Classification Mathematically.mp4 40.0 MB
24. Advanced Machine Learning Algorithms/5. Case study.mp4 40.0 MB
24. Advanced Machine Learning Algorithms/7. Ridge and Lasso.mp4 39.9 MB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/6. Verification of Model.mp4 39.5 MB
20. Ensembling/1. Introduction to Ensembles.mp4 39.3 MB
3. Pandas/1. Introduction.mp4 39.1 MB
20. Ensembling/15. XGboost Algorithm.mp4 38.8 MB
5. Inferential Statistics/12. Sampling.mp4 38.7 MB
20. Ensembling/10. Adaboost Part2.mp4 38.5 MB
26. Project Kaggle/18. Building Machine Learning model part3.mp4 38.4 MB
22. Unsupervised Learning/12. Hierarchial Clustering.mp4 38.0 MB
6. Hypothesis Testing/4. OneTwo Tailed Tests.mp4 38.0 MB
12. Gradient Descent/5. Gradient Descent.mp4 37.7 MB
1. Python Fundamentals/6. Numeric Operations in Python.mp4 36.9 MB
12. Gradient Descent/4. Defining Cost Functions More Formally.mp4 36.5 MB
22. Unsupervised Learning/7. Value of K.mp4 35.8 MB
21. Model Selection Part2/3. Model Selection Part3.mp4 35.7 MB
20. Ensembling/14. Boosting Part2.mp4 35.5 MB
9. Simple Linear Regression/2. Types of Machine Learning.mp4 35.4 MB
15. Model Selection Part1/2. Model Creation Case2.mp4 34.7 MB
5. Inferential Statistics/15. Confidence Interval Part1.mp4 34.5 MB
22. Unsupervised Learning/13. Case Study.mp4 34.4 MB
3. Pandas/11. Merging Part2.mp4 33.9 MB
6. Hypothesis Testing/9. p Value.mp4 33.5 MB
13. KNN/8. Finding k.mp4 33.3 MB
18. Support Vector Machine (SVM)/6. Slack Variable.mp4 33.3 MB
26. Project Kaggle/19. Building Machine Learning model part4.mp4 33.1 MB
20. Ensembling/6. Introduction to Boosting.mp4 33.0 MB
12. Gradient Descent/2. Pre-Req For Gradient Descent Part2.mp4 32.9 MB
24. Advanced Machine Learning Algorithms/9. Model Selection.mp4 31.3 MB
6. Hypothesis Testing/1. Introduction.mp4 31.1 MB
24. Advanced Machine Learning Algorithms/1. Introduction.mp4 30.9 MB
3. Pandas/9. Merging Part1.mp4 30.0 MB
25. Deep Learning/4. Perceptron.mp4 29.8 MB
19. Decision Tree/1. Introduction.mp4 29.8 MB
8. Exploratory Data Analysis/9. Data Cleaning part2.mp4 29.7 MB
6. Hypothesis Testing/12. t- distribution Part2.mp4 29.3 MB
19. Decision Tree/5. Information Gain Part1.mp4 29.3 MB
13. KNN/7. Distance Metrics Part2.mp4 28.8 MB
6. Hypothesis Testing/2. NULL And Alternate Hypothesis.mp4 28.8 MB
5. Inferential Statistics/6. Without Experiment.mp4 28.7 MB
22. Unsupervised Learning/2. Segmentation.mp4 28.7 MB
6. Hypothesis Testing/3. Examples.mp4 27.8 MB
4. Some Fun With Maths/4. Linear Algebra Going From 2D to nD Part1.mp4 27.7 MB
3. Pandas/12. Pivot Table.mp4 27.7 MB
24. Advanced Machine Learning Algorithms/2. Example Part1.mp4 27.5 MB
1. Python Fundamentals/12. String Part2.mp4 27.4 MB
19. Decision Tree/6. Information Gain Part2.mp4 27.4 MB
16. Naive Bayes/8. Bernoulli Naive Bayes.mp4 27.1 MB
18. Support Vector Machine (SVM)/2. Hyperplane Part1.mp4 27.1 MB
12. Gradient Descent/7. Closed Form Vs Gradient Descent.mp4 26.6 MB
17. Logistic Regression/1. Introduction.mp4 26.6 MB
6. Hypothesis Testing/7. Examples.mp4 26.4 MB
4. Some Fun With Maths/5. Linear Algebra 2D to nD Part2.mp4 25.8 MB
5. Inferential Statistics/13. Sampling Distribution.mp4 25.5 MB
16. Naive Bayes/11. Case Study 2 Part2.mp4 25.4 MB
2. Numpy/1. Introduction.mp4 24.7 MB
6. Hypothesis Testing/5. Critical Value Method.mp4 24.7 MB
8. Exploratory Data Analysis/12. Segmented Analysis.mp4 24.5 MB
5. Inferential Statistics/4. Expected Values Part1.mp4 24.3 MB
5. Inferential Statistics/3. Probability Distribution.mp4 24.2 MB
18. Support Vector Machine (SVM)/4. Maths Behind SVM.mp4 24.0 MB
14. Model Performance Metrics/3. Performance Metrics Part3.mp4 24.0 MB
5. Inferential Statistics/11. z Score.mp4 23.8 MB
20. Ensembling/12. XGBoost.mp4 23.1 MB
12. Gradient Descent/6. Optimisation.mp4 21.7 MB
6. Hypothesis Testing/11. t- distribution Part1.mp4 21.3 MB
5. Inferential Statistics/9. PDF.mp4 21.0 MB
19. Decision Tree/3. Homogenity.mp4 20.6 MB
24. Advanced Machine Learning Algorithms/10. Adjusted R Square.mp4 20.1 MB
5. Inferential Statistics/10. Normal Distribution.mp4 19.0 MB
22. Unsupervised Learning/11. More on Segmentation.mp4 18.1 MB
20. Ensembling/7. Weak Learners.mp4 17.9 MB
9. Simple Linear Regression/3. Introduction to Linear Regression (LR).mp4 17.9 MB
5. Inferential Statistics/7. Binomial Distribution.mp4 17.6 MB
1. Python Fundamentals/7. Logical Operations.mp4 17.3 MB
6. Hypothesis Testing/8. More Examples.mp4 16.5 MB
10. Multiple Linear Regression/1. Introduction.mp4 16.5 MB
20. Ensembling/4. Runtime.mp4 16.4 MB
8. Exploratory Data Analysis/3. Data Sourcing and Cleaning part2.mp4 15.6 MB
8. Exploratory Data Analysis/2. Data Sourcing and Cleaning part1.mp4 15.6 MB
19. Decision Tree/7. Advantages and Disadvantages of DT.mp4 15.5 MB
18. Support Vector Machine (SVM)/1.1 SVM.zip.zip 15.4 MB
6. Hypothesis Testing/10. Types of Error.mp4 15.3 MB
20. Ensembling/8. Shallow Decision Tree.mp4 15.0 MB
20. Ensembling/3. Advantages.mp4 14.9 MB
5. Inferential Statistics/5. Expected Values Part2.mp4 14.5 MB
20. Ensembling/13. Boosting Part1.mp4 13.7 MB
5. Inferential Statistics/16. Confidence Interval Part2.mp4 13.4 MB
12. Gradient Descent/3. Cost Functions.mp4 13.2 MB
5. Inferential Statistics/14. Central Limit Theorem.mp4 13.1 MB
8. Exploratory Data Analysis/6. Data Sourcing and Cleaning part5.mp4 12.4 MB
22. Unsupervised Learning/8. Hopkins test.mp4 12.3 MB
3. Pandas/4. Operations Part1.mp4 12.0 MB
9. Simple Linear Regression/1. Introduction to Machine Learning.mp4 11.2 MB
18. Support Vector Machine (SVM)/5. Support Vectors.mp4 11.0 MB
8. Exploratory Data Analysis/5. Data Sourcing and Cleaning part4.mp4 10.4 MB
5. Inferential Statistics/1. Inferential Statistics.mp4 10.3 MB
1. Python Fundamentals/4. Python Introduction.mp4 10.2 MB
1. Python Fundamentals/13. List Part1.mp4 10.0 MB
8. Exploratory Data Analysis/4. Data Sourcing and Cleaning part3.mp4 10.0 MB
22. Unsupervised Learning/5. More Maths.mp4 9.4 MB
25. Deep Learning/1. Expectations.mp4 9.4 MB
13. KNN/9. KNN on Regression.mp4 9.3 MB
23. Dimension Reduction/1.1 PCA code for udemy.zip.zip 9.1 MB
5. Inferential Statistics/8. Commulative Distribution.mp4 8.4 MB
10. Multiple Linear Regression/5. Adjusted R Square.mp4 8.1 MB
22. Unsupervised Learning/1.1 Unsupervised.zip.zip 7.4 MB
9. Simple Linear Regression/10. Residual Square Error (RSE).mp4 4.6 MB
19. Decision Tree/1.1 DT_forudemy.zip.zip 4.0 MB
8. Exploratory Data Analysis/1. Introduction.mp4 3.8 MB
1. Python Fundamentals/3.2 Installing-Python.Teclov.pdf.pdf 1.4 MB
13. KNN/1.1 KNN.zip.zip 1.3 MB
26. Project Kaggle/1.2 Teclov Project - Medical treatment.ipynb.zip.zip 1.3 MB
20. Ensembling/1.1 Boosting.zip.zip 1.3 MB
7. Data Visualisation/1.1 Datavisual.zip.zip 1.2 MB
24. Advanced Machine Learning Algorithms/1.1 AdvanceReg.zip.zip 1.1 MB
20. Ensembling/1.2 RF_forudemy.zip.zip 1.1 MB
17. Logistic Regression/1.1 LogisticReg.zip.zip 983.7 KB
10. Multiple Linear Regression/1.1 Multplr_LR_Code_for Udemy.zip.zip 521.0 KB
15. Model Selection Part1/1.1 CrossValidation_Linear Regression.zip.zip 342.2 KB
16. Naive Bayes/1.1 NaiveBayes.zip.zip 266.0 KB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/1.1 Hotstarcode-for-udemy.zip.zip 254.6 KB
12. Gradient Descent/1.1 Gradient+Descent+Updated.zip.zip 161.1 KB
6. Hypothesis Testing/1.2 t-table.pdf.pdf 147.3 KB
9. Simple Linear Regression/1.1 code-LR-Teclov.zip.zip 76.8 KB
6. Hypothesis Testing/1.1 z-table.pdf.pdf 59.0 KB
4. Some Fun With Maths/1. Linear Algebra Vectors.vtt 49.9 KB
23. Dimension Reduction/1. Introduction.vtt 32.2 KB
2. Numpy/3. Numpy Operations Part2.vtt 29.8 KB
14. Model Performance Metrics/1. Performance Metrics Part1.vtt 27.1 KB
23. Dimension Reduction/2. PCA.vtt 26.4 KB
7. Data Visualisation/1. Matplotlib.vtt 26.3 KB
7. Data Visualisation/2. Seaborn.vtt 26.0 KB
8. Exploratory Data Analysis/10. Univariate Analysis Part1.vtt 25.8 KB
23. Dimension Reduction/3. Maths Behind PCA.vtt 25.5 KB
13. KNN/11. Classification Case1.vtt 25.0 KB
2. Numpy/2. Numpy Operations Part1.vtt 23.8 KB
21. Model Selection Part2/1. Model Selection Part1.vtt 23.2 KB
1. Python Fundamentals/5. Variables in Python.vtt 20.7 KB
17. Logistic Regression/4. Case Study.vtt 20.4 KB
18. Support Vector Machine (SVM)/14. Case Study 4.vtt 20.4 KB
26. Project Kaggle/6. Understanding Evaluation Matrix Log Loss.vtt 20.0 KB
8. Exploratory Data Analysis/11. Univariate Analysis Part2.vtt 19.6 KB
4. Some Fun With Maths/3. Linear Algebra Matrix Part2.vtt 19.4 KB
14. Model Performance Metrics/2. Performance Metrics Part2.vtt 19.1 KB
23. Dimension Reduction/5. Case Study Part2.vtt 19.0 KB
15. Model Selection Part1/4. Gridsearch Case study Part2.vtt 18.4 KB
25. Deep Learning/3. History.vtt 18.0 KB
26. Project Kaggle/2. Playing With The Data.vtt 17.9 KB
10. Multiple Linear Regression/9. Case Study Part4.vtt 17.8 KB
16. Naive Bayes/1. Introduction to Naive Bayes.vtt 17.8 KB
12. Gradient Descent/1. Pre-Req For Gradient Descent Part1.vtt 17.6 KB
9. Simple Linear Regression/7. LR Case Study Part1.vtt 17.5 KB
26. Project Kaggle/16. Building Machine Learning model part1.vtt 17.3 KB
13. KNN/12. Classification Case2.vtt 16.9 KB
4. Some Fun With Maths/2. Linear Algebra Matrix Part1.vtt 16.8 KB
18. Support Vector Machine (SVM)/3. Hyperplane Part2.vtt 16.8 KB
24. Advanced Machine Learning Algorithms/4. Optimal Solution.vtt 16.7 KB
8. Exploratory Data Analysis/8. Data Cleaning part1.vtt 16.7 KB
8. Exploratory Data Analysis/13. Bivariate Analysis.vtt 16.4 KB
1. Python Fundamentals/3.1 Python-code-udemy.zip.zip 16.4 KB
1. Python Fundamentals/4.1 Python-code-udemy.zip.zip 16.4 KB
1. Python Fundamentals/1. Introduction to the course.vtt 16.3 KB
13. KNN/5. Effectiveness of KNN.vtt 15.9 KB
1. Python Fundamentals/11. String Part1.vtt 15.5 KB
13. KNN/1. Introduction to Classification.vtt 15.5 KB
3. Pandas/1.1 Pandas.zip.zip 15.5 KB
20. Ensembling/2. Bagging.vtt 15.4 KB
26. Project Kaggle/17. Building Machine Learning model part2.vtt 15.3 KB
13. KNN/13. Classification Case3.vtt 15.2 KB
13. KNN/4. Accuracy of KNN.vtt 14.9 KB
26. Project Kaggle/9. First Categorical column analysis.vtt 14.7 KB
13. KNN/6. Distance Metrics.vtt 14.6 KB
21. Model Selection Part2/2. Model Selection Part2.vtt 14.5 KB
1. Python Fundamentals/10. Functions.vtt 14.5 KB
25. Deep Learning/5. Multi Layered Perceptron.vtt 14.5 KB
26. Project Kaggle/11. Laplace Smoothing and Calibrated classifier.vtt 14.4 KB
8. Exploratory Data Analysis/14. Derived Columns.vtt 14.4 KB
5. Inferential Statistics/2. Probability Theory.vtt 13.9 KB
13. KNN/14. Classification Case4.vtt 13.8 KB
18. Support Vector Machine (SVM)/1. Introduction.vtt 13.7 KB
13. KNN/3. Introduction to KNN.vtt 13.7 KB
25. Deep Learning/6. Neural Network Playground.vtt 13.6 KB
15. Model Selection Part1/3. Gridsearch Case study Part1.vtt 13.5 KB
20. Ensembling/17. Case Study Part2.vtt 13.4 KB
22. Unsupervised Learning/4. Maths Behind Kmeans.vtt 13.2 KB
22. Unsupervised Learning/9. Case Study Part1.vtt 13.2 KB
1. Python Fundamentals/14. List Part2.vtt 13.2 KB
16. Naive Bayes/4. Practical Example from NB with Multiple Columns.vtt 13.2 KB
1. Python Fundamentals/9. for while Loop.vtt 13.0 KB
19. Decision Tree/9. DT Case Study Part1.vtt 12.9 KB
7. Data Visualisation/3. Case Study.vtt 12.9 KB
16. Naive Bayes/2. Bayes Theorem.vtt 12.7 KB
22. Unsupervised Learning/1. Introduction to Clustering.vtt 12.7 KB
18. Support Vector Machine (SVM)/10. Kernel Part2.vtt 12.7 KB
12. Gradient Descent/5. Gradient Descent.vtt 12.5 KB
15. Model Selection Part1/1. Model Creation Case1.vtt 12.4 KB
9. Simple Linear Regression/6. R Square.vtt 12.3 KB
10. Multiple Linear Regression/7. Case Study Part2.vtt 12.3 KB
26. Project Kaggle/5. Train, Test And Cross Validation Split.vtt 12.3 KB
10. Multiple Linear Regression/3. Case Study part2.vtt 12.2 KB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/2. Playing With Data.vtt 11.9 KB
26. Project Kaggle/3. Translating the Problem In Machine Learning World.vtt 11.9 KB
19. Decision Tree/8. Preventing Overfitting Issues in DT.vtt 11.8 KB
20. Ensembling/16. Case Study Part1.vtt 11.8 KB
17. Logistic Regression/2. Sigmoid Function.vtt 11.6 KB
13. KNN/8. Finding k.vtt 11.5 KB
22. Unsupervised Learning/6. Kmeans plus.vtt 11.4 KB
1. Python Fundamentals/2. Introduction to Kaggle.vtt 11.1 KB
1. Python Fundamentals/3. Installation of Python and Anaconda.vtt 11.1 KB
20. Ensembling/1. Introduction to Ensembles.vtt 11.1 KB
8. Exploratory Data Analysis/9. Data Cleaning part2.vtt 11.0 KB
9. Simple Linear Regression/5. Some Fun With Maths Behind LR.vtt 11.0 KB
17. Logistic Regression/3. Log Odds.vtt 10.9 KB
19. Decision Tree/10. DT Case Study Part2.vtt 10.9 KB
16. Naive Bayes/9. Case Study 1.vtt 10.8 KB
13. KNN/10. Case Study.vtt 10.8 KB
24. Advanced Machine Learning Algorithms/3. Example Part2.vtt 10.7 KB
24. Advanced Machine Learning Algorithms/8. Case Study.vtt 10.7 KB
16. Naive Bayes/3. Practical Example from NB with One Column.vtt 10.7 KB
26. Project Kaggle/7. Building A Worst Model.vtt 10.7 KB
25. Deep Learning/2. Introduction.vtt 10.6 KB
1. Python Fundamentals/15. List Part3.vtt 10.5 KB
1. Python Fundamentals/16. List Part4.vtt 10.4 KB
24. Advanced Machine Learning Algorithms/6. Regularization.vtt 10.3 KB
18. Support Vector Machine (SVM)/6. Slack Variable.vtt 10.2 KB
1. Python Fundamentals/17. Tuples.vtt 10.2 KB
6. Hypothesis Testing/4. OneTwo Tailed Tests.vtt 10.1 KB
22. Unsupervised Learning/3. Kmeans.vtt 10.1 KB
1. Python Fundamentals/8. If else Loop.vtt 10.0 KB
16. Naive Bayes/5. Naive Bayes On Text Data Part1.vtt 10.0 KB
4. Some Fun With Maths/4. Linear Algebra Going From 2D to nD Part1.vtt 9.9 KB
9. Simple Linear Regression/4. How LR Works.vtt 9.9 KB
18. Support Vector Machine (SVM)/12. Case Study 3 Part1.vtt 9.9 KB
26. Project Kaggle/4. Dealing with Text Data.vtt 9.9 KB
5. Inferential Statistics/12. Sampling.vtt 9.9 KB
26. Project Kaggle/1. Introduction to the Problem Statement.vtt 9.6 KB
3. Pandas/2. Series.vtt 9.6 KB
18. Support Vector Machine (SVM)/9. Kernel Part1.vtt 9.4 KB
3. Pandas/7. loc and iloc.vtt 9.3 KB
3. Pandas/3. DataFrame.vtt 9.3 KB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/4. Building Model Part2.vtt 9.3 KB
13. KNN/7. Distance Metrics Part2.vtt 9.3 KB
6. Hypothesis Testing/1. Introduction.vtt 9.2 KB
19. Decision Tree/2. Example of DT.vtt 9.2 KB
26. Project Kaggle/21. Building Machine Learning model part6.vtt 9.1 KB
22. Unsupervised Learning/10. Case Study Part2.vtt 9.0 KB
22. Unsupervised Learning/12. Hierarchial Clustering.vtt 9.0 KB
13. KNN/2. Defining Classification Mathematically.vtt 9.0 KB
9. Simple Linear Regression/2. Types of Machine Learning.vtt 9.0 KB
19. Decision Tree/1. Introduction.vtt 9.0 KB
12. Gradient Descent/2. Pre-Req For Gradient Descent Part2.vtt 9.0 KB
26. Project Kaggle/12. Significance of first categorical column.vtt 8.9 KB
16. Naive Bayes/10. Case Study 2 Part1.vtt 8.9 KB
19. Decision Tree/4. Gini Index.vtt 8.9 KB
10. Multiple Linear Regression/6. Case Study Part1.vtt 8.8 KB
6. Hypothesis Testing/6. z Table.vtt 8.8 KB
20. Ensembling/15. XGboost Algorithm.vtt 8.8 KB
15. Model Selection Part1/2. Model Creation Case2.vtt 8.8 KB
22. Unsupervised Learning/2. Segmentation.vtt 8.7 KB
12. Gradient Descent/4. Defining Cost Functions More Formally.vtt 8.6 KB
26. Project Kaggle/14. Third Categorical column.vtt 8.6 KB
10. Multiple Linear Regression/2. Case Study part1.vtt 8.5 KB
18. Support Vector Machine (SVM)/8. SVM Case Study Part2.vtt 8.4 KB
18. Support Vector Machine (SVM)/11. Case Study 2.vtt 8.4 KB
1. Python Fundamentals/19. Dictionaries.vtt 8.3 KB
20. Ensembling/9. Adaboost Part1.vtt 8.3 KB
25. Deep Learning/4. Perceptron.vtt 8.2 KB
10. Multiple Linear Regression/11. Case Study Part6 (RFE).vtt 8.2 KB
17. Logistic Regression/1. Introduction.vtt 8.2 KB
4. Some Fun With Maths/5. Linear Algebra 2D to nD Part2.vtt 8.2 KB
18. Support Vector Machine (SVM)/4. Maths Behind SVM.vtt 8.1 KB
1. Python Fundamentals/20. Comprehentions.vtt 8.1 KB
20. Ensembling/10. Adaboost Part2.vtt 7.9 KB
3. Pandas/1. Introduction.vtt 7.9 KB
20. Ensembling/14. Boosting Part2.vtt 7.8 KB
1. Python Fundamentals/18. Sets.vtt 7.8 KB
8. Exploratory Data Analysis/12. Segmented Analysis.vtt 7.8 KB
10. Multiple Linear Regression/4. Case Study part3.vtt 7.7 KB
24. Advanced Machine Learning Algorithms/7. Ridge and Lasso.vtt 7.7 KB
10. Multiple Linear Regression/8. Case Study Part3.vtt 7.6 KB
22. Unsupervised Learning/7. Value of K.vtt 7.6 KB
6. Hypothesis Testing/2. NULL And Alternate Hypothesis.vtt 7.5 KB
3. Pandas/6. Indexes.vtt 7.4 KB
5. Inferential Statistics/15. Confidence Interval Part1.vtt 7.3 KB
5. Inferential Statistics/6. Without Experiment.vtt 7.2 KB
24. Advanced Machine Learning Algorithms/1. Introduction.vtt 7.2 KB
1. Python Fundamentals/6. Numeric Operations in Python.vtt 7.1 KB
26. Project Kaggle/8. Evaluating Worst ML Model.vtt 7.1 KB
3. Pandas/10. groupby.vtt 7.1 KB
3. Pandas/8. Reading CSV.vtt 6.9 KB
20. Ensembling/5. Case study.vtt 6.9 KB
20. Ensembling/18. Case Study Part3.vtt 6.9 KB
5. Inferential Statistics/13. Sampling Distribution.vtt 6.9 KB
12. Gradient Descent/8. Gradient Descent case study.vtt 6.9 KB
19. Decision Tree/5. Information Gain Part1.vtt 6.7 KB
6. Hypothesis Testing/3. Examples.vtt 6.7 KB
16. Naive Bayes/6. Naive Bayes On Text Data Part2.vtt 6.6 KB
26. Project Kaggle/10. Response encoding and one hot encoder.vtt 6.6 KB
22. Unsupervised Learning/13. Case Study.vtt 6.5 KB
24. Advanced Machine Learning Algorithms/9. Model Selection.vtt 6.5 KB
18. Support Vector Machine (SVM)/7. SVM Case Study Part1.vtt 6.5 KB
20. Ensembling/6. Introduction to Boosting.vtt 6.5 KB
6. Hypothesis Testing/9. p Value.vtt 6.5 KB
2. Numpy/1. Introduction.vtt 6.3 KB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/1. Introduction to the Problem Statement.vtt 6.2 KB
14. Model Performance Metrics/3. Performance Metrics Part3.vtt 6.2 KB
18. Support Vector Machine (SVM)/13. Case Study 3 Part2.vtt 6.2 KB
18. Support Vector Machine (SVM)/2. Hyperplane Part1.vtt 6.1 KB
10. Multiple Linear Regression/10. Case Study Part5.vtt 6.1 KB
3. Pandas/5. Operations Part2.vtt 6.1 KB
23. Dimension Reduction/4. Case Study Part1.vtt 6.1 KB
24. Advanced Machine Learning Algorithms/2. Example Part1.vtt 6.0 KB
20. Ensembling/11. Adaboost Case Study.vtt 6.0 KB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/3. Building Model Part1.vtt 5.8 KB
19. Decision Tree/3. Homogenity.vtt 5.8 KB
12. Gradient Descent/7. Closed Form Vs Gradient Descent.vtt 5.8 KB
3. Pandas/11. Merging Part2.vtt 5.7 KB
19. Decision Tree/6. Information Gain Part2.vtt 5.7 KB
26. Project Kaggle/15. Data pre-processing before building machine learning model.vtt 5.6 KB
7. Data Visualisation/4. Seaborn On Time Series Data.vtt 5.6 KB
9. Simple Linear Regression/9. LR Case Study Part3.vtt 5.5 KB
5. Inferential Statistics/4. Expected Values Part1.vtt 5.5 KB
22. Unsupervised Learning/11. More on Segmentation.vtt 5.5 KB
5. Inferential Statistics/3. Probability Distribution.vtt 5.5 KB
9. Simple Linear Regression/8. LR Case Study Part2.vtt 5.4 KB
5. Inferential Statistics/9. PDF.vtt 5.4 KB
5. Inferential Statistics/11. z Score.vtt 5.4 KB
5. Inferential Statistics/10. Normal Distribution.vtt 5.3 KB
26. Project Kaggle/13. Second Categorical column.vtt 5.3 KB
2. Numpy/1.1 Teclov-numpy.ipynb.zip.zip 5.2 KB
26. Project Kaggle/20. Building Machine Learning model part5.vtt 5.1 KB
20. Ensembling/3. Advantages.vtt 5.1 KB
12. Gradient Descent/6. Optimisation.vtt 5.1 KB
16. Naive Bayes/7. Laplace Smoothing.vtt 4.9 KB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/6. Verification of Model.vtt 4.7 KB
20. Ensembling/12. XGBoost.vtt 4.7 KB
20. Ensembling/4. Runtime.vtt 4.7 KB
11. HotstarNetflix Real world Case Study for Multiple Linear Regression/5. Building Model Part3.vtt 4.7 KB
6. Hypothesis Testing/5. Critical Value Method.vtt 4.6 KB
3. Pandas/12. Pivot Table.vtt 4.5 KB
19. Decision Tree/7. Advantages and Disadvantages of DT.vtt 4.4 KB
8. Exploratory Data Analysis/7. Data Sourcing and Cleaning part6.vtt 4.4 KB
3. Pandas/9. Merging Part1.vtt 4.3 KB
24. Advanced Machine Learning Algorithms/5. Case study.vtt 4.2 KB
5. Inferential Statistics/7. Binomial Distribution.vtt 4.2 KB
26. Project Kaggle/18. Building Machine Learning model part3.vtt 4.2 KB
6. Hypothesis Testing/11. t- distribution Part1.vtt 4.1 KB
24. Advanced Machine Learning Algorithms/10. Adjusted R Square.vtt 4.1 KB
8. Exploratory Data Analysis/2. Data Sourcing and Cleaning part1.vtt 4.0 KB
18. Support Vector Machine (SVM)/5. Support Vectors.vtt 4.0 KB
26. Project Kaggle/19. Building Machine Learning model part4.vtt 3.9 KB
5. Inferential Statistics/5. Expected Values Part2.vtt 3.9 KB
8. Exploratory Data Analysis/5. Data Sourcing and Cleaning part4.vtt 3.8 KB
8. Exploratory Data Analysis/6. Data Sourcing and Cleaning part5.vtt 3.7 KB
20. Ensembling/13. Boosting Part1.vtt 3.7 KB
10. Multiple Linear Regression/1. Introduction.vtt 3.6 KB
6. Hypothesis Testing/7. Examples.vtt 3.5 KB
1. Python Fundamentals/4. Python Introduction.vtt 3.5 KB
1. Python Fundamentals/12. String Part2.vtt 3.4 KB
6. Hypothesis Testing/10. Types of Error.vtt 3.4 KB
6. Hypothesis Testing/8. More Examples.vtt 3.4 KB
8. Exploratory Data Analysis/4. Data Sourcing and Cleaning part3.vtt 3.4 KB
1. Python Fundamentals/7. Logical Operations.vtt 3.2 KB
5. Inferential Statistics/16. Confidence Interval Part2.vtt 3.2 KB
20. Ensembling/7. Weak Learners.vtt 3.1 KB
6. Hypothesis Testing/12. t- distribution Part2.vtt 3.1 KB
5. Inferential Statistics/1. Inferential Statistics.vtt 3.1 KB
22. Unsupervised Learning/8. Hopkins test.vtt 3.0 KB
5. Inferential Statistics/14. Central Limit Theorem.vtt 3.0 KB
9. Simple Linear Regression/3. Introduction to Linear Regression (LR).vtt 3.0 KB
16. Naive Bayes/11. Case Study 2 Part2.vtt 3.0 KB
13. KNN/9. KNN on Regression.vtt 2.9 KB
1. Python Fundamentals/13. List Part1.vtt 2.9 KB
22. Unsupervised Learning/5. More Maths.vtt 2.9 KB
12. Gradient Descent/3. Cost Functions.vtt 2.8 KB
25. Deep Learning/1. Expectations.vtt 2.8 KB
20. Ensembling/8. Shallow Decision Tree.vtt 2.7 KB
5. Inferential Statistics/8. Commulative Distribution.vtt 2.7 KB
8. Exploratory Data Analysis/3. Data Sourcing and Cleaning part2.vtt 2.5 KB
9. Simple Linear Regression/1. Introduction to Machine Learning.vtt 2.2 KB
16. Naive Bayes/8. Bernoulli Naive Bayes.vtt 2.0 KB
3. Pandas/4. Operations Part1.vtt 1.4 KB
[FTU Forum].url 1.3 KB
9. Simple Linear Regression/10. Residual Square Error (RSE).vtt 1.0 KB
8. Exploratory Data Analysis/1. Introduction.vtt 897 bytes
10. Multiple Linear Regression/5. Adjusted R Square.vtt 855 bytes
How you can help Team-FTU.txt 241 bytes
[FreeCoursesOnline.Me].url 133 bytes
[FreeTutorials.Eu].url 129 bytes

[FreeTutorials.Eu] Udemy - Machine Learning A-Z Become Kaggle Master

File List

Download Torrent

Related Resources

Copyright Infringement