UdemyCourseDownloader Machine Learning Become Kaggle Master | FD2DB670761E9E7DADE0A30C9C5ECA4C94D13E31

UdemyCourseDownloader Machine Learning Become Kaggle Master

Name	[UdemyCourseDownloader] Machine Learning A-Z Become Kaggle Master	DOWNLOAD Copy Link
Total Size	15.0 GB
Total Files	540
Hash	FD2DB670761E9E7DADE0A30C9C5ECA4C94D13E31

/17. Logistic Regression/
4. Case Study.mp4	207.8 MB
1. Introduction.mp4	27.9 MB
1. Introduction.vtt	8.4 KB
1.1 LogisticReg.zip.zip	1.0 MB
2. Sigmoid Function.mp4	46.5 MB
2. Sigmoid Function.vtt	11.9 KB
3. Log Odds.mp4	43.9 MB
3. Log Odds.vtt	11.2 KB
4. Case Study.vtt	20.9 KB
/
udemycoursedownloader.com.url	0.1 KB
Udemy Course downloader.txt	0.1 KB
/1. Python Fundamentals/
1. Introduction to the course.mp4	98.4 MB
1. Introduction to the course.vtt	16.7 KB
2. Introduction to Kaggle.mp4	94.4 MB
2. Introduction to Kaggle.vtt	11.4 KB
3. Installation of Python and Anaconda.mp4	86.3 MB
3. Installation of Python and Anaconda.vtt	11.4 KB
3.1 Python-code-udemy.zip.zip	16.8 KB
3.2 Installing-Python.Teclov.pdf.pdf	1.4 MB
4. Python Introduction.mp4	10.7 MB
4. Python Introduction.vtt	3.6 KB
4.1 Python-code-udemy.zip.zip	16.8 KB
5. Variables in Python.mp4	115.8 MB
5. Variables in Python.vtt	21.2 KB
6. Numeric Operations in Python.mp4	38.7 MB
6. Numeric Operations in Python.vtt	7.3 KB
7. Logical Operations.mp4	18.2 MB
7. Logical Operations.vtt	3.3 KB
8. If else Loop.mp4	67.1 MB
8. If else Loop.vtt	10.3 KB
9. for while Loop.mp4	81.6 MB
9. for while Loop.vtt	13.3 KB
10. Functions.mp4	89.8 MB
10. Functions.vtt	14.8 KB
11. String Part1.mp4	111.2 MB
11. String Part1.vtt	15.9 KB
12. String Part2.mp4	28.7 MB
12. String Part2.vtt	3.5 KB
13. List Part1.mp4	10.5 MB
13. List Part1.vtt	3.0 KB
14. List Part2.mp4	91.6 MB
14. List Part2.vtt	13.5 KB
15. List Part3.mp4	77.1 MB
15. List Part3.vtt	10.7 KB
16. List Part4.mp4	67.0 MB
16. List Part4.vtt	10.7 KB
17. Tuples.mp4	70.6 MB
17. Tuples.vtt	10.4 KB
18. Sets.mp4	61.0 MB
18. Sets.vtt	8.0 KB
19. Dictionaries.mp4	64.6 MB
19. Dictionaries.vtt	8.5 KB
20. Comprehentions.mp4	74.0 MB
20. Comprehentions.vtt	8.3 KB
/2. Numpy/
1. Introduction.mp4	25.9 MB
1. Introduction.vtt	6.4 KB
1.1 Teclov-numpy.ipynb.zip.zip	5.3 KB
2. Numpy Operations Part1.mp4	135.0 MB
2. Numpy Operations Part1.vtt	24.4 KB
3. Numpy Operations Part2.mp4	178.2 MB
3. Numpy Operations Part2.vtt	30.5 KB
/3. Pandas/
1. Introduction.mp4	41.0 MB
1. Introduction.vtt	8.1 KB
1.1 Pandas.zip.zip	15.8 KB
2. Series.mp4	64.5 MB
2. Series.vtt	9.8 KB
3. DataFrame.mp4	69.4 MB
3. DataFrame.vtt	9.5 KB
4. Operations Part1.mp4	12.6 MB
4. Operations Part1.vtt	1.5 KB
5. Operations Part2.mp4	46.2 MB
5. Operations Part2.vtt	6.2 KB
6. Indexes.mp4	52.5 MB
6. Indexes.vtt	7.6 KB
7. loc and iloc.mp4	62.3 MB
7. loc and iloc.vtt	9.6 KB
8. Reading CSV.mp4	44.5 MB
8. Reading CSV.vtt	7.1 KB
9. Merging Part1.mp4	31.5 MB
9. Merging Part1.vtt	4.4 KB
10. groupby.mp4	49.2 MB
10. groupby.vtt	7.2 KB
11. Merging Part2.mp4	35.6 MB
11. Merging Part2.vtt	5.9 KB
12. Pivot Table.mp4	29.1 MB
12. Pivot Table.vtt	4.6 KB
/4. Some Fun With Maths/
1. Linear Algebra Vectors.mp4	170.3 MB
1. Linear Algebra Vectors.vtt	51.1 KB
2. Linear Algebra Matrix Part1.mp4	99.9 MB
2. Linear Algebra Matrix Part1.vtt	17.3 KB
3. Linear Algebra Matrix Part2.mp4	81.8 MB
3. Linear Algebra Matrix Part2.vtt	19.9 KB
4. Linear Algebra Going From 2D to nD Part1.mp4	29.1 MB
4. Linear Algebra Going From 2D to nD Part1.vtt	10.2 KB
5. Linear Algebra 2D to nD Part2.mp4	27.0 MB
5. Linear Algebra 2D to nD Part2.vtt	8.4 KB
/5. Inferential Statistics/
1. Inferential Statistics.mp4	10.8 MB
1. Inferential Statistics.vtt	3.1 KB
2. Probability Theory.mp4	57.4 MB
2. Probability Theory.vtt	14.2 KB
3. Probability Distribution.mp4	25.4 MB
3. Probability Distribution.vtt	5.6 KB
4. Expected Values Part1.mp4	25.4 MB
4. Expected Values Part1.vtt	5.6 KB
5. Expected Values Part2.mp4	15.2 MB
5. Expected Values Part2.vtt	4.0 KB
6. Without Experiment.mp4	30.1 MB
6. Without Experiment.vtt	7.4 KB
7. Binomial Distribution.mp4	18.4 MB
7. Binomial Distribution.vtt	4.3 KB
8. Commulative Distribution.mp4	8.8 MB
8. Commulative Distribution.vtt	2.8 KB
9. PDF.mp4	22.0 MB
9. PDF.vtt	5.6 KB
10. Normal Distribution.mp4	19.9 MB
10. Normal Distribution.vtt	5.4 KB
11. z Score.mp4	25.0 MB
11. z Score.vtt	5.5 KB
12. Sampling.mp4	40.6 MB
12. Sampling.vtt	10.1 KB
13. Sampling Distribution.mp4	26.8 MB
13. Sampling Distribution.vtt	7.0 KB
14. Central Limit Theorem.mp4	13.7 MB
14. Central Limit Theorem.vtt	3.0 KB
15. Confidence Interval Part1.mp4	36.2 MB
15. Confidence Interval Part1.vtt	7.4 KB
16. Confidence Interval Part2.mp4	14.0 MB
16. Confidence Interval Part2.vtt	3.3 KB
/6. Hypothesis Testing/
1. Introduction.mp4	32.6 MB
1. Introduction.vtt	9.4 KB
1.1 z-table.pdf.pdf	60.4 KB
1.2 t-table.pdf.pdf	150.8 KB
2. NULL And Alternate Hypothesis.mp4	30.2 MB
2. NULL And Alternate Hypothesis.vtt	7.7 KB
3. Examples.mp4	29.1 MB
3. Examples.vtt	6.8 KB
4. OneTwo Tailed Tests.mp4	39.8 MB
4. OneTwo Tailed Tests.vtt	10.4 KB
5. Critical Value Method.mp4	25.9 MB
5. Critical Value Method.vtt	4.7 KB
6. z Table.mp4	61.5 MB
6. z Table.vtt	9.0 KB
7. Examples.mp4	27.7 MB
7. Examples.vtt	3.6 KB
8. More Examples.mp4	17.3 MB
8. More Examples.vtt	3.5 KB
9. p Value.mp4	35.1 MB
9. p Value.vtt	6.6 KB
10. Types of Error.mp4	16.0 MB
10. Types of Error.vtt	3.5 KB
11. t- distribution Part1.mp4	22.4 MB
11. t- distribution Part1.vtt	4.2 KB
12. t- distribution Part2.mp4	30.7 MB
12. t- distribution Part2.vtt	3.1 KB
/7. Data Visualisation/
1. Matplotlib.mp4	181.2 MB
1. Matplotlib.vtt	27.0 KB
1.1 Datavisual.zip.zip	1.3 MB
2. Seaborn.mp4	193.7 MB
2. Seaborn.vtt	26.6 KB
3. Case Study.mp4	118.7 MB
3. Case Study.vtt	13.2 KB
4. Seaborn On Time Series Data.mp4	56.7 MB
4. Seaborn On Time Series Data.vtt	5.7 KB
/8. Exploratory Data Analysis/
1. Introduction.mp4	4.0 MB
1. Introduction.vtt	0.9 KB
2. Data Sourcing and Cleaning part1.mp4	16.3 MB
2. Data Sourcing and Cleaning part1.vtt	4.1 KB
3. Data Sourcing and Cleaning part2.mp4	16.4 MB
3. Data Sourcing and Cleaning part2.vtt	2.6 KB
4. Data Sourcing and Cleaning part3.mp4	10.5 MB
4. Data Sourcing and Cleaning part3.vtt	3.4 KB
5. Data Sourcing and Cleaning part4.mp4	10.9 MB
5. Data Sourcing and Cleaning part4.vtt	3.9 KB
6. Data Sourcing and Cleaning part5.mp4	13.0 MB
6. Data Sourcing and Cleaning part5.vtt	3.8 KB
7. Data Sourcing and Cleaning part6.mp4	56.3 MB
7. Data Sourcing and Cleaning part6.vtt	4.5 KB
8. Data Cleaning part1.mp4	79.9 MB
8. Data Cleaning part1.vtt	17.1 KB
9. Data Cleaning part2.mp4	31.1 MB
9. Data Cleaning part2.vtt	11.3 KB
10. Univariate Analysis Part1.mp4	86.8 MB
10. Univariate Analysis Part1.vtt	26.4 KB
11. Univariate Analysis Part2.mp4	63.8 MB
11. Univariate Analysis Part2.vtt	20.0 KB
12. Segmented Analysis.mp4	25.7 MB
12. Segmented Analysis.vtt	8.0 KB
13. Bivariate Analysis.mp4	63.5 MB
13. Bivariate Analysis.vtt	16.8 KB
14. Derived Columns.mp4	43.9 MB
14. Derived Columns.vtt	14.8 KB
/9. Simple Linear Regression/
1. Introduction to Machine Learning.mp4	11.7 MB
1. Introduction to Machine Learning.vtt	2.2 KB
1.1 code-LR-Teclov.zip.zip	78.7 KB
2. Types of Machine Learning.mp4	37.1 MB
2. Types of Machine Learning.vtt	9.2 KB
3. Introduction to Linear Regression (LR).mp4	18.8 MB
3. Introduction to Linear Regression (LR).vtt	3.0 KB
4. How LR Works.mp4	61.5 MB
4. How LR Works.vtt	10.2 KB
5. Some Fun With Maths Behind LR.mp4	55.3 MB
5. Some Fun With Maths Behind LR.vtt	11.2 KB
6. R Square.mp4	55.0 MB
6. R Square.vtt	12.6 KB
7. LR Case Study Part1.mp4	144.2 MB
7. LR Case Study Part1.vtt	17.9 KB
8. LR Case Study Part2.mp4	56.0 MB
8. LR Case Study Part2.vtt	5.6 KB
9. LR Case Study Part3.mp4	48.7 MB
9. LR Case Study Part3.vtt	5.7 KB
10. Residual Square Error (RSE).mp4	4.8 MB
10. Residual Square Error (RSE).vtt	1.0 KB
/10. Multiple Linear Regression/
1. Introduction.mp4	17.3 MB
1. Introduction.vtt	3.7 KB
1.1 Multplr_LR_Code_for Udemy.zip.zip	533.5 KB
2. Case Study part1.mp4	87.1 MB
2. Case Study part1.vtt	8.7 KB
3. Case Study part2.mp4	103.2 MB
3. Case Study part2.vtt	12.5 KB
4. Case Study part3.mp4	72.0 MB
4. Case Study part3.vtt	7.9 KB
5. Adjusted R Square.mp4	8.5 MB
5. Adjusted R Square.vtt	0.9 KB
6. Case Study Part1.mp4	71.9 MB
6. Case Study Part1.vtt	9.0 KB
7. Case Study Part2.mp4	76.4 MB
7. Case Study Part2.vtt	12.6 KB
8. Case Study Part3.mp4	69.8 MB
8. Case Study Part3.vtt	7.8 KB
9. Case Study Part4.mp4	138.6 MB
9. Case Study Part4.vtt	18.2 KB
10. Case Study Part5.mp4	47.9 MB
10. Case Study Part5.vtt	6.2 KB
11. Case Study Part6 (RFE).mp4	67.3 MB
11. Case Study Part6 (RFE).vtt	8.4 KB
/11. HotstarNetflix Real world Case Study for Multiple Linear Regression/
1. Introduction to the Problem Statement.mp4	42.8 MB
1. Introduction to the Problem Statement.vtt	6.4 KB
1.1 Hotstarcode-for-udemy.zip.zip	260.7 KB
2. Playing With Data.mp4	85.3 MB
2. Playing With Data.vtt	12.2 KB
3. Building Model Part1.mp4	57.7 MB
3. Building Model Part1.vtt	6.0 KB
4. Building Model Part2.mp4	92.1 MB
4. Building Model Part2.vtt	9.5 KB
5. Building Model Part3.mp4	50.9 MB
5. Building Model Part3.vtt	4.8 KB
6. Verification of Model.mp4	41.4 MB
6. Verification of Model.vtt	4.9 KB
/12. Gradient Descent/
1. Pre-Req For Gradient Descent Part1.mp4	64.2 MB
1. Pre-Req For Gradient Descent Part1.vtt	18.0 KB
1.1 Gradient+Descent+Updated.zip.zip	165.0 KB
2. Pre-Req For Gradient Descent Part2.mp4	34.5 MB
2. Pre-Req For Gradient Descent Part2.vtt	9.2 KB
3. Cost Functions.mp4	13.8 MB
3. Cost Functions.vtt	2.9 KB
4. Defining Cost Functions More Formally.mp4	38.3 MB
4. Defining Cost Functions More Formally.vtt	8.8 KB
5. Gradient Descent.mp4	39.5 MB
5. Gradient Descent.vtt	12.8 KB
6. Optimisation.mp4	22.7 MB
6. Optimisation.vtt	5.2 KB
7. Closed Form Vs Gradient Descent.mp4	27.9 MB
7. Closed Form Vs Gradient Descent.vtt	5.9 KB
8. Gradient Descent case study.mp4	75.1 MB
8. Gradient Descent case study.vtt	7.0 KB
/13. KNN/
1. Introduction to Classification.mp4	56.7 MB
1. Introduction to Classification.vtt	15.9 KB
1.1 KNN.zip.zip	1.4 MB
2. Defining Classification Mathematically.mp4	41.9 MB
2. Defining Classification Mathematically.vtt	9.2 KB
3. Introduction to KNN.mp4	49.4 MB
3. Introduction to KNN.vtt	14.0 KB
4. Accuracy of KNN.mp4	59.9 MB
4. Accuracy of KNN.vtt	15.2 KB
5. Effectiveness of KNN.mp4	50.6 MB
5. Effectiveness of KNN.vtt	16.2 KB
6. Distance Metrics.mp4	50.2 MB
6. Distance Metrics.vtt	15.0 KB
7. Distance Metrics Part2.mp4	30.2 MB
7. Distance Metrics Part2.vtt	9.5 KB
8. Finding k.mp4	34.9 MB
8. Finding k.vtt	11.8 KB
9. KNN on Regression.mp4	9.7 MB
9. KNN on Regression.vtt	3.0 KB
10. Case Study.mp4	74.1 MB
10. Case Study.vtt	11.1 KB
11. Classification Case1.mp4	88.3 MB
11. Classification Case1.vtt	25.6 KB
12. Classification Case2.mp4	54.8 MB
12. Classification Case2.vtt	17.3 KB
13. Classification Case3.mp4	55.5 MB
13. Classification Case3.vtt	15.6 KB
14. Classification Case4.mp4	43.1 MB
14. Classification Case4.vtt	14.1 KB
/14. Model Performance Metrics/
1. Performance Metrics Part1.mp4	119.4 MB
1. Performance Metrics Part1.vtt	27.8 KB
2. Performance Metrics Part2.mp4	94.9 MB
2. Performance Metrics Part2.vtt	19.6 KB
3. Performance Metrics Part3.mp4	25.2 MB
3. Performance Metrics Part3.vtt	6.4 KB
/15. Model Selection Part1/
1. Model Creation Case1.mp4	54.6 MB
1. Model Creation Case1.vtt	12.7 KB
1.1 CrossValidation_Linear Regression.zip.zip	350.4 KB
2. Model Creation Case2.mp4	36.4 MB
2. Model Creation Case2.vtt	9.0 KB
3. Gridsearch Case study Part1.mp4	130.3 MB
3. Gridsearch Case study Part1.vtt	13.8 KB
4. Gridsearch Case study Part2.mp4	187.6 MB
4. Gridsearch Case study Part2.vtt	18.9 KB
/16. Naive Bayes/
1. Introduction to Naive Bayes.mp4	76.9 MB
1. Introduction to Naive Bayes.vtt	18.2 KB
1.1 NaiveBayes.zip.zip	272.4 KB
2. Bayes Theorem.mp4	66.1 MB
2. Bayes Theorem.vtt	13.0 KB
3. Practical Example from NB with One Column.mp4	84.5 MB
3. Practical Example from NB with One Column.vtt	10.9 KB
4. Practical Example from NB with Multiple Columns.mp4	62.7 MB
4. Practical Example from NB with Multiple Columns.vtt	13.5 KB
5. Naive Bayes On Text Data Part1.mp4	57.4 MB
5. Naive Bayes On Text Data Part1.vtt	10.2 KB
6. Naive Bayes On Text Data Part2.mp4	48.3 MB
6. Naive Bayes On Text Data Part2.vtt	6.7 KB
7. Laplace Smoothing.mp4	57.9 MB
7. Laplace Smoothing.vtt	5.1 KB
8. Bernoulli Naive Bayes.mp4	28.4 MB
8. Bernoulli Naive Bayes.vtt	2.1 KB
9. Case Study 1.mp4	100.1 MB
9. Case Study 1.vtt	11.1 KB
10. Case Study 2 Part1.mp4	78.2 MB
10. Case Study 2 Part1.vtt	9.1 KB
11. Case Study 2 Part2.mp4	26.6 MB
11. Case Study 2 Part2.vtt	3.0 KB
/18. Support Vector Machine (SVM)/
1. Introduction.mp4	61.6 MB
1. Introduction.vtt	14.0 KB
1.1 SVM.zip.zip	16.2 MB
2. Hyperplane Part1.mp4	28.4 MB
2. Hyperplane Part1.vtt	6.3 KB
3. Hyperplane Part2.mp4	68.5 MB
3. Hyperplane Part2.vtt	17.2 KB
4. Maths Behind SVM.mp4	25.2 MB
4. Maths Behind SVM.vtt	8.3 KB
5. Support Vectors.mp4	11.6 MB
5. Support Vectors.vtt	4.0 KB
6. Slack Variable.mp4	34.9 MB
6. Slack Variable.vtt	10.5 KB
7. SVM Case Study Part1.mp4	77.7 MB
7. SVM Case Study Part1.vtt	6.6 KB
8. SVM Case Study Part2.mp4	69.4 MB
8. SVM Case Study Part2.vtt	8.6 KB
9. Kernel Part1.mp4	51.6 MB
9. Kernel Part1.vtt	9.6 KB
10. Kernel Part2.mp4	74.6 MB
10. Kernel Part2.vtt	13.0 KB
11. Case Study 2.mp4	94.4 MB
11. Case Study 2.vtt	8.6 KB
12. Case Study 3 Part1.mp4	58.7 MB
12. Case Study 3 Part1.vtt	10.2 KB
13. Case Study 3 Part2.mp4	64.3 MB
13. Case Study 3 Part2.vtt	6.4 KB
14. Case Study 4.mp4	172.4 MB
14. Case Study 4.vtt	20.9 KB
/19. Decision Tree/
1. Introduction.mp4	31.2 MB
1. Introduction.vtt	9.2 KB
1.1 DT_forudemy.zip.zip	4.2 MB
2. Example of DT.mp4	42.6 MB
2. Example of DT.vtt	9.4 KB
3. Homogenity.mp4	21.6 MB
3. Homogenity.vtt	6.0 KB
4. Gini Index.mp4	46.3 MB
4. Gini Index.vtt	9.1 KB
5. Information Gain Part1.mp4	30.7 MB
5. Information Gain Part1.vtt	6.8 KB
6. Information Gain Part2.mp4	28.7 MB
6. Information Gain Part2.vtt	5.8 KB
7. Advantages and Disadvantages of DT.mp4	16.2 MB
7. Advantages and Disadvantages of DT.vtt	4.5 KB
8. Preventing Overfitting Issues in DT.mp4	42.2 MB
8. Preventing Overfitting Issues in DT.vtt	12.1 KB
9. DT Case Study Part1.mp4	131.5 MB
9. DT Case Study Part1.vtt	13.2 KB
10. DT Case Study Part2.mp4	100.4 MB
10. DT Case Study Part2.vtt	11.2 KB
/20. Ensembling/
1. Introduction to Ensembles.mp4	41.2 MB
1. Introduction to Ensembles.vtt	11.3 KB
1.1 Boosting.zip.zip	1.3 MB
1.2 RF_forudemy.zip.zip	1.1 MB
2. Bagging.mp4	74.7 MB
2. Bagging.vtt	15.8 KB
3. Advantages.mp4	15.6 MB
3. Advantages.vtt	5.2 KB
4. Runtime.mp4	17.2 MB
4. Runtime.vtt	4.8 KB
5. Case study.mp4	76.6 MB
5. Case study.vtt	7.1 KB
6. Introduction to Boosting.mp4	34.7 MB
6. Introduction to Boosting.vtt	6.6 KB
7. Weak Learners.mp4	18.8 MB
7. Weak Learners.vtt	3.2 KB
8. Shallow Decision Tree.mp4	15.7 MB
8. Shallow Decision Tree.vtt	2.8 KB
9. Adaboost Part1.mp4	43.6 MB
9. Adaboost Part1.vtt	8.5 KB
10. Adaboost Part2.mp4	40.3 MB
10. Adaboost Part2.vtt	8.1 KB
11. Adaboost Case Study.mp4	56.3 MB
11. Adaboost Case Study.vtt	6.2 KB
12. XGBoost.mp4	24.2 MB
12. XGBoost.vtt	4.9 KB
13. Boosting Part1.mp4	14.4 MB
13. Boosting Part1.vtt	3.8 KB
14. Boosting Part2.mp4	37.2 MB
14. Boosting Part2.vtt	8.0 KB
15. XGboost Algorithm.mp4	40.6 MB
15. XGboost Algorithm.vtt	9.0 KB
16. Case Study Part1.mp4	148.4 MB
16. Case Study Part1.vtt	12.1 KB
17. Case Study Part2.mp4	143.3 MB
17. Case Study Part2.vtt	13.7 KB
18. Case Study Part3.mp4	79.1 MB
18. Case Study Part3.vtt	7.0 KB
/21. Model Selection Part2/
1. Model Selection Part1.mp4	109.4 MB
1. Model Selection Part1.vtt	23.8 KB
2. Model Selection Part2.mp4	43.3 MB
2. Model Selection Part2.vtt	14.9 KB
3. Model Selection Part3.mp4	37.4 MB
/22. Unsupervised Learning/
1. Introduction to Clustering.mp4	62.0 MB
1. Introduction to Clustering.vtt	13.0 KB
1.1 Unsupervised.zip.zip	7.7 MB
2. Segmentation.mp4	30.0 MB
2. Segmentation.vtt	8.9 KB
3. Kmeans.mp4	60.5 MB
3. Kmeans.vtt	10.4 KB
4. Maths Behind Kmeans.mp4	56.4 MB
4. Maths Behind Kmeans.vtt	13.5 KB
5. More Maths.mp4	9.9 MB
5. More Maths.vtt	3.0 KB
6. Kmeans plus.mp4	54.3 MB
6. Kmeans plus.vtt	11.7 KB
7. Value of K.mp4	37.6 MB
7. Value of K.vtt	7.8 KB
8. Hopkins test.mp4	12.9 MB
8. Hopkins test.vtt	3.1 KB
9. Case Study Part1.mp4	100.5 MB
9. Case Study Part1.vtt	13.5 KB
10. Case Study Part2.mp4	64.3 MB
10. Case Study Part2.vtt	9.3 KB
11. More on Segmentation.mp4	18.9 MB
11. More on Segmentation.vtt	5.6 KB
12. Hierarchial Clustering.mp4	39.9 MB
12. Hierarchial Clustering.vtt	9.3 KB
13. Case Study.mp4	36.1 MB
13. Case Study.vtt	6.7 KB
/23. Dimension Reduction/
1. Introduction.mp4	164.3 MB
1. Introduction.vtt	32.9 KB
1.1 PCA code for udemy.zip.zip	9.5 MB
2. PCA.mp4	103.2 MB
2. PCA.vtt	27.1 KB
3. Maths Behind PCA.mp4	101.5 MB
3. Maths Behind PCA.vtt	26.1 KB
4. Case Study Part1.mp4	47.7 MB
4. Case Study Part1.vtt	6.2 KB
5. Case Study Part2.mp4	129.0 MB
5. Case Study Part2.vtt	19.4 KB
/24. Advanced Machine Learning Algorithms/
1. Introduction.mp4	32.4 MB
1. Introduction.vtt	7.3 KB
1.1 AdvanceReg.zip.zip	1.2 MB
2. Example Part1.mp4	28.8 MB
2. Example Part1.vtt	6.2 KB
3. Example Part2.mp4	47.3 MB
3. Example Part2.vtt	11.0 KB
4. Optimal Solution.mp4	68.4 MB
4. Optimal Solution.vtt	17.1 KB
5. Case study.mp4	41.9 MB
5. Case study.vtt	4.3 KB
6. Regularization.mp4	51.0 MB
6. Regularization.vtt	10.6 KB
7. Ridge and Lasso.mp4	41.9 MB
7. Ridge and Lasso.vtt	7.9 KB
8. Case Study.mp4	111.4 MB
8. Case Study.vtt	10.9 KB
9. Model Selection.mp4	32.8 MB
9. Model Selection.vtt	6.7 KB
10. Adjusted R Square.mp4	21.1 MB
10. Adjusted R Square.vtt	4.2 KB
/25. Deep Learning/
1. Expectations.mp4	9.8 MB
1. Expectations.vtt	2.9 KB
2. Introduction.mp4	51.1 MB
2. Introduction.vtt	10.8 KB
3. History.mp4	64.9 MB
3. History.vtt	18.4 KB
4. Perceptron.mp4	31.2 MB
4. Perceptron.vtt	8.4 KB
5. Multi Layered Perceptron.mp4	66.9 MB
5. Multi Layered Perceptron.vtt	14.8 KB
6. Neural Network Playground.mp4	108.7 MB
6. Neural Network Playground.vtt	13.9 KB
/26. Project Kaggle/
1. Introduction to the Problem Statement.mp4	97.9 MB
1. Introduction to the Problem Statement.vtt	9.9 KB
1.1 training.zip.zip	62.9 MB
1.2 Teclov Project - Medical treatment.ipynb.zip.zip	1.3 MB
2. Playing With The Data.mp4	143.7 MB
2. Playing With The Data.vtt	18.3 KB
3. Translating the Problem In Machine Learning World.mp4	118.5 MB
3. Translating the Problem In Machine Learning World.vtt	12.2 KB
4. Dealing with Text Data.mp4	102.8 MB
4. Dealing with Text Data.vtt	10.1 KB
5. Train, Test And Cross Validation Split.mp4	121.9 MB
5. Train, Test And Cross Validation Split.vtt	12.6 KB
6. Understanding Evaluation Matrix Log Loss.mp4	89.7 MB
6. Understanding Evaluation Matrix Log Loss.vtt	20.5 KB
7. Building A Worst Model.mp4	71.8 MB
7. Building A Worst Model.vtt	10.9 KB
8. Evaluating Worst ML Model.mp4	61.7 MB
8. Evaluating Worst ML Model.vtt	7.2 KB
9. First Categorical column analysis.mp4	74.6 MB
9. First Categorical column analysis.vtt	15.0 KB
10. Response encoding and one hot encoder.mp4	57.3 MB
10. Response encoding and one hot encoder.vtt	6.7 KB
11. Laplace Smoothing and Calibrated classifier.mp4	50.6 MB
11. Laplace Smoothing and Calibrated classifier.vtt	14.8 KB
12. Significance of first categorical column.mp4	75.2 MB
12. Significance of first categorical column.vtt	9.1 KB
13. Second Categorical column.mp4	47.9 MB
13. Second Categorical column.vtt	5.4 KB
14. Third Categorical column.mp4	70.0 MB
14. Third Categorical column.vtt	8.8 KB
15. Data pre-processing before building machine learning model.mp4	53.0 MB
15. Data pre-processing before building machine learning model.vtt	5.8 KB
16. Building Machine Learning model part1.mp4	130.0 MB
16. Building Machine Learning model part1.vtt	17.7 KB
17. Building Machine Learning model part2.mp4	141.7 MB
17. Building Machine Learning model part2.vtt	15.7 KB
18. Building Machine Learning model part3.mp4	40.3 MB
18. Building Machine Learning model part3.vtt	4.3 KB
19. Building Machine Learning model part4.mp4	34.7 MB
19. Building Machine Learning model part4.vtt	4.0 KB
20. Building Machine Learning model part5.mp4	44.0 MB
20. Building Machine Learning model part5.vtt	5.2 KB
21. Building Machine Learning model part6.mp4	53.3 MB
21. Building Machine Learning model part6.vtt	9.3 KB
Total files 540