种子简介

种子名称： [GigaCourse.Com] Udemy - Machine Learning & Deep Learning in Python & R
文件类型： 视频
文件数目： 278个文件
文件大小： 13.15 GB
收录时间： 2021-12-26 04:08
已经下载： 3次
资源热度： 124
最近下载： 2024-6-14 03:49

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:4d33b004bdddefc1de86cb8519c18e9d8815374e&dn=[GigaCourse.Com] Udemy - Machine Learning & Deep Learning in Python & R 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[GigaCourse.Com] Udemy - Machine Learning & Deep Learning in Python & R.torrent

1. Introduction/1. Introduction.mp429.4MB

10. Logistic Regression/1. Logistic Regression.mp432.93MB

10. Logistic Regression/10. Evaluating performance of model.mp435.17MB

10. Logistic Regression/11. Evaluating model performance in Python.mp49.02MB

10. Logistic Regression/12. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp455.7MB

10. Logistic Regression/2. Training a Simple Logistic Model in Python.mp447.87MB

10. Logistic Regression/3. Training a Simple Logistic model in R.mp425.57MB

10. Logistic Regression/4. Result of Simple Logistic Regression.mp426.94MB

10. Logistic Regression/5. Logistic with multiple predictors.mp48.53MB

10. Logistic Regression/6. Training multiple predictor Logistic model in Python.mp426.25MB

10. Logistic Regression/7. Training multiple predictor Logistic model in R.mp415.78MB

10. Logistic Regression/8. Confusion Matrix.mp421.1MB

10. Logistic Regression/9. Creating Confusion Matrix in Python.mp451.25MB

11. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp440.96MB

11. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp411.4MB

11. Linear Discriminant Analysis (LDA)/3. Linear Discriminant Analysis in R.mp474.36MB

12. K-Nearest Neighbors classifier/1. Test-Train Split.mp439.3MB

12. K-Nearest Neighbors classifier/2. Test-Train Split in Python.mp433.1MB

12. K-Nearest Neighbors classifier/3. Test-Train Split in R.mp474.23MB

12. K-Nearest Neighbors classifier/4. K-Nearest Neighbors classifier.mp475.42MB

12. K-Nearest Neighbors classifier/5. K-Nearest Neighbors in Python Part 1.mp437.23MB

12. K-Nearest Neighbors classifier/6. K-Nearest Neighbors in Python Part 2.mp442.36MB

12. K-Nearest Neighbors classifier/7. K-Nearest Neighbors in R.mp464.85MB

13. Comparing results from 3 models/1. Understanding the results of classification models.mp441.64MB

13. Comparing results from 3 models/2. Summary of the three models.mp422.22MB

14. Simple Decision Trees/1. Basics of Decision Trees.mp442.64MB

14. Simple Decision Trees/10. Test-Train split in Python.mp424.87MB

14. Simple Decision Trees/11. Splitting Data into Test and Train Set in R.mp443.98MB

14. Simple Decision Trees/12. Creating Decision tree in Python.mp417.87MB

14. Simple Decision Trees/13. Building a Regression Tree in R.mp4103.34MB

14. Simple Decision Trees/14. Evaluating model performance in Python.mp416.44MB

14. Simple Decision Trees/15. Plotting decision tree in Python.mp421.48MB

14. Simple Decision Trees/16. Pruning a tree.mp418.46MB

14. Simple Decision Trees/17. Pruning a tree in Python.mp473.5MB

14. Simple Decision Trees/18. Pruning a Tree in R.mp482.1MB

14. Simple Decision Trees/2. Understanding a Regression Tree.mp443.72MB

14. Simple Decision Trees/3. The stopping criteria for controlling tree growth.mp413.98MB

14. Simple Decision Trees/4. The Data set for this part.mp437.26MB

14. Simple Decision Trees/5. Importing the Data set into Python.mp425.85MB

14. Simple Decision Trees/6. Importing the Data set into R.mp443.7MB

14. Simple Decision Trees/7. Missing value treatment in Python.mp417.93MB

14. Simple Decision Trees/8. Dummy Variable creation in Python.mp424.94MB

14. Simple Decision Trees/9. Dependent- Independent Data split in Python.mp415.18MB

15. Simple Classification Tree/1. Classification tree.mp428.2MB

15. Simple Classification Tree/2. The Data set for Classification problem.mp418.57MB

15. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp445.38MB

15. Simple Classification Tree/4. Classification tree in Python Training.mp482.72MB

15. Simple Classification Tree/5. Building a classification Tree in R.mp485.1MB

15. Simple Classification Tree/6. Advantages and Disadvantages of Decision Trees.mp46.86MB

16. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp428.14MB

16. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp477.3MB

16. Ensemble technique 1 - Bagging/3. Bagging in R.mp458.96MB

17. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp418.2MB

17. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp446.7MB

17. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp480.67MB

17. Ensemble technique 2 - Random Forests/4. Random Forest in R.mp430.72MB

18. Ensemble technique 3 - Boosting/1. Boosting.mp430.58MB

18. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp439.88MB

18. Ensemble technique 3 - Boosting/3. Gradient Boosting in R.mp469.09MB

18. Ensemble technique 3 - Boosting/4. Ensemble technique 3b - AdaBoost in Python.mp430.54MB

18. Ensemble technique 3 - Boosting/5. AdaBoosting in R.mp488.67MB

18. Ensemble technique 3 - Boosting/6. Ensemble technique 3c - XGBoost in Python.mp475.01MB

18. Ensemble technique 3 - Boosting/7. XGBoosting in R.mp4161.3MB

19. Maximum Margin Classifier/1. Content flow.mp48.64MB

19. Maximum Margin Classifier/2. The Concept of a Hyperplane.mp429.42MB

19. Maximum Margin Classifier/3. Maximum Margin Classifier.mp422.48MB

19. Maximum Margin Classifier/4. Limitations of Maximum Margin Classifier.mp410.61MB

2. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp416.27MB

2. Setting up Python and Jupyter Notebook/10. Working with Seaborn Library of Python.mp440.37MB

2. Setting up Python and Jupyter Notebook/2. This is a milestone!.mp420.66MB

2. Setting up Python and Jupyter Notebook/3. Opening Jupyter Notebook.mp465.19MB

2. Setting up Python and Jupyter Notebook/4. Introduction to Jupyter.mp440.92MB

2. Setting up Python and Jupyter Notebook/5. Arithmetic operators in Python Python Basics.mp412.74MB

2. Setting up Python and Jupyter Notebook/6. Strings in Python Python Basics.mp464.44MB

2. Setting up Python and Jupyter Notebook/7. Lists, Tuples and Directories Python Basics.mp460.33MB

2. Setting up Python and Jupyter Notebook/8. Working with Numpy Library of Python.mp443.88MB

2. Setting up Python and Jupyter Notebook/9. Working with Pandas Library of Python.mp446.88MB

20. Support Vector Classifier/1. Support Vector classifiers.mp456.17MB

20. Support Vector Classifier/2. Limitations of Support Vector Classifiers.mp410.8MB

21. Support Vector Machines/1. Kernel Based Support Vector Machines.mp440.12MB

22. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp44.04MB

22. Creating Support Vector Machine Model in Python/10. Classification model - Standardizing the data.mp49.72MB

22. Creating Support Vector Machine Model in Python/11. SVM Based classification model.mp464.13MB

22. Creating Support Vector Machine Model in Python/12. Hyper Parameter Tuning.mp457.74MB

22. Creating Support Vector Machine Model in Python/13. Polynomial Kernel with Hyperparameter Tuning.mp422.92MB

22. Creating Support Vector Machine Model in Python/14. Radial Kernel with Hyperparameter Tuning.mp437.21MB

22. Creating Support Vector Machine Model in Python/2. The Data set for the Regression problem.mp437.2MB

22. Creating Support Vector Machine Model in Python/3. Importing data for regression model.mp425.84MB

22. Creating Support Vector Machine Model in Python/4. X-y Split.mp415.18MB

22. Creating Support Vector Machine Model in Python/5. Test-Train Split.mp424.87MB

22. Creating Support Vector Machine Model in Python/6. Standardizing the data.mp438.41MB

22. Creating Support Vector Machine Model in Python/7. SVM based Regression Model in Python.mp467.64MB

22. Creating Support Vector Machine Model in Python/8. The Data set for the Classification problem.mp418.56MB

22. Creating Support Vector Machine Model in Python/9. Classification model - Preprocessing.mp445.38MB

23. Creating Support Vector Machine Model in R/1. Importing Data into R.mp453.67MB

23. Creating Support Vector Machine Model in R/2. Test-Train Split.mp450.48MB

23. Creating Support Vector Machine Model in R/4. Classification SVM model using Linear Kernel.mp4139.16MB

23. Creating Support Vector Machine Model in R/5. Hyperparameter Tuning for Linear Kernel.mp460.5MB

23. Creating Support Vector Machine Model in R/6. Polynomial Kernel with Hyperparameter Tuning.mp483.14MB

23. Creating Support Vector Machine Model in R/7. Radial Kernel with Hyperparameter Tuning.mp456.68MB

23. Creating Support Vector Machine Model in R/8. SVM based Regression Model in R.mp4106.12MB

24. Introduction - Deep Learning/1. Introduction to Neural Networks and Course flow.mp429.07MB

24. Introduction - Deep Learning/2. Perceptron.mp444.75MB

24. Introduction - Deep Learning/3. Activation Functions.mp434.62MB

24. Introduction - Deep Learning/4. Python - Creating Perceptron model.mp486.56MB

25. Neural Networks - Stacking cells to create network/1. Basic Terminologies.mp440.42MB

25. Neural Networks - Stacking cells to create network/2. Gradient Descent.mp460.34MB

25. Neural Networks - Stacking cells to create network/3. Back Propagation.mp4122.2MB

25. Neural Networks - Stacking cells to create network/4. Some Important Concepts.mp462.18MB

25. Neural Networks - Stacking cells to create network/5. Hyperparameter.mp445.36MB

26. ANN in Python/1. Keras and Tensorflow.mp414.92MB

26. ANN in Python/10. Using Functional API for complex architectures.mp492.11MB

26. ANN in Python/11. Saving - Restoring Models and Using Callbacks.mp4151.59MB

26. ANN in Python/12. Hyperparameter Tuning.mp460.63MB

26. ANN in Python/2. Installing Tensorflow and Keras.mp420.06MB

26. ANN in Python/3. Dataset for classification.mp456.19MB

26. ANN in Python/4. Normalization and Test-Train split.mp444.2MB

26. ANN in Python/5. Different ways to create ANN using Keras.mp410.82MB

26. ANN in Python/6. Building the Neural Network using Keras.mp479.11MB

26. ANN in Python/7. Compiling and Training the Neural Network model.mp481.63MB

26. ANN in Python/8. Evaluating performance and Predicting using Keras.mp469.91MB

26. ANN in Python/9. Building Neural Network for Regression Problem.mp4155.9MB

27. ANN in R/1. Installing Keras and Tensorflow.mp422.79MB

27. ANN in R/2. Data Normalization and Test-Train Split.mp4111.78MB

27. ANN in R/3. Building,Compiling and Training.mp4130.74MB

27. ANN in R/4. Evaluating and Predicting.mp499.28MB

27. ANN in R/5. ANN with NeuralNets Package.mp484.42MB

27. ANN in R/6. Building Regression Model with Functional API.mp4131.13MB

27. ANN in R/7. Complex Architectures using Functional API.mp479.57MB

27. ANN in R/8. Saving - Restoring Models and Using Callbacks.mp4216.03MB

28. CNN - Basics/1. CNN Introduction.mp451.16MB

28. CNN - Basics/2. Stride.mp416.58MB

28. CNN - Basics/3. Padding.mp431.63MB

28. CNN - Basics/4. Filters and Feature maps.mp452.71MB

28. CNN - Basics/5. Channels.mp467.77MB

28. CNN - Basics/6. PoolingLayer.mp446.88MB

29. Creating CNN model in Python/1. CNN model in Python - Preprocessing.mp440.63MB

29. Creating CNN model in Python/2. CNN model in Python - structure and Compile.mp443.26MB

29. Creating CNN model in Python/3. CNN model in Python - Training and results.mp455.15MB

29. Creating CNN model in Python/4. Comparison - Pooling vs Without Pooling in Python.mp457.97MB

3. Setting up R Studio and R crash course/1. Installing R and R studio.mp435.71MB

3. Setting up R Studio and R crash course/2. Basics of R and R studio.mp438.85MB

3. Setting up R Studio and R crash course/3. Packages in R.mp482.95MB

3. Setting up R Studio and R crash course/4. Inputting data part 1 Inbuilt datasets of R.mp440.74MB

3. Setting up R Studio and R crash course/5. Inputting data part 2 Manual data entry.mp425.52MB

3. Setting up R Studio and R crash course/6. Inputting data part 3 Importing from CSV or Text files.mp460.11MB

3. Setting up R Studio and R crash course/7. Creating Barplots in R.mp496.74MB

3. Setting up R Studio and R crash course/8. Creating Histograms in R.mp442.02MB

30. Creating CNN model in R/1. CNN on MNIST Fashion Dataset - Model Architecture.mp47.35MB

30. Creating CNN model in R/2. Data Preprocessing.mp467.03MB

30. Creating CNN model in R/3. Creating Model Architecture.mp471.6MB

30. Creating CNN model in R/4. Compiling and training.mp432.2MB

30. Creating CNN model in R/5. Model Performance.mp468.08MB

30. Creating CNN model in R/6. Comparison - Pooling vs Without Pooling in R.mp444.6MB

31. Project Creating CNN model from scratch in Python/1. Project - Introduction.mp449.39MB

31. Project Creating CNN model from scratch in Python/3. Project - Data Preprocessing in Python.mp471.83MB

31. Project Creating CNN model from scratch in Python/4. Project - Training CNN model in Python.mp465.98MB

31. Project Creating CNN model from scratch in Python/5. Project in Python - model results.mp421.02MB

32. Project Creating CNN model from scratch/1. Project in R - Data Preprocessing.mp487.76MB

32. Project Creating CNN model from scratch/2. CNN Project in R - Structure and Compile.mp446.12MB

32. Project Creating CNN model from scratch/3. Project in R - Training.mp424.58MB

32. Project Creating CNN model from scratch/4. Project in R - Model Performance.mp423.18MB

32. Project Creating CNN model from scratch/5. Project in R - Data Augmentation.mp456.38MB

32. Project Creating CNN model from scratch/6. Project in R - Validation Performance.mp423.69MB

33. Project Data Augmentation for avoiding overfitting/1. Project - Data Augmentation Preprocessing.mp441.42MB

33. Project Data Augmentation for avoiding overfitting/2. Project - Data Augmentation Training and Results.mp453.04MB

34. Transfer Learning Basics/1. ILSVRC.mp420.93MB

34. Transfer Learning Basics/2. LeNET.mp47MB

34. Transfer Learning Basics/3. VGG16NET.mp410.35MB

34. Transfer Learning Basics/4. GoogLeNet.mp421.37MB

34. Transfer Learning Basics/5. Transfer Learning.mp429.99MB

34. Transfer Learning Basics/6. Project - Transfer Learning - VGG16.mp4129.1MB

35. Transfer Learning in R/1. Project - Transfer Learning - VGG16 (Implementation).mp4101.57MB

35. Transfer Learning in R/2. Project - Transfer Learning - VGG16 (Performance).mp464.11MB

36. Time Series Analysis and Forecasting/1. Introduction.mp412.27MB

36. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.mp425.92MB

36. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.mp410.11MB

36. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).mp434.5MB

36. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp462.48MB

37. Time Series - Preprocessing in Python/1. Data Loading in Python.mp4108.87MB

37. Time Series - Preprocessing in Python/10. Exponential Smoothing.mp48.39MB

37. Time Series - Preprocessing in Python/2. Time Series - Visualization Basics.mp463.72MB

37. Time Series - Preprocessing in Python/3. Time Series - Visualization in Python.mp4165.2MB

37. Time Series - Preprocessing in Python/4. Time Series - Feature Engineering Basics.mp459.48MB

37. Time Series - Preprocessing in Python/5. Time Series - Feature Engineering in Python.mp4112.69MB

37. Time Series - Preprocessing in Python/6. Time Series - Upsampling and Downsampling.mp416.96MB

37. Time Series - Preprocessing in Python/7. Time Series - Upsampling and Downsampling in Python.mp4100.67MB

37. Time Series - Preprocessing in Python/8. Time Series - Power Transformation.mp414.86MB

37. Time Series - Preprocessing in Python/9. Moving Average.mp438.71MB

38. Time Series - Important Concepts/1. White Noise.mp411.37MB

38. Time Series - Important Concepts/2. Random Walk.mp421.17MB

38. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp459.84MB

38. Time Series - Important Concepts/4. Differencing.mp432.35MB

38. Time Series - Important Concepts/5. Differencing in Python.mp4113.01MB

39. Time Series - Implementation in Python/1. Test Train Split in Python.mp457.42MB

39. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp443.38MB

39. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp416.89MB

39. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp453.49MB

39. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp449.6MB

39. Time Series - Implementation in Python/6. Moving Average model -Basics.mp424.1MB

39. Time Series - Implementation in Python/7. Moving Average model in Python.mp456.65MB

4. Basics of Statistics/1. Types of Data.mp421.76MB

4. Basics of Statistics/2. Types of Statistics.mp410.94MB

4. Basics of Statistics/3. Describing data Graphically.mp465.4MB

4. Basics of Statistics/4. Measures of Centers.mp438.58MB

4. Basics of Statistics/5. Measures of Dispersion.mp422.85MB

40. Time Series - ARIMA model/1. ACF and PACF.mp441.23MB

40. Time Series - ARIMA model/2. ARIMA model - Basics.mp421.37MB

40. Time Series - ARIMA model/3. ARIMA model in Python.mp474.44MB

40. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp432.15MB

41. Time Series - SARIMA model/1. SARIMA model.mp439.03MB

41. Time Series - SARIMA model/2. SARIMA model in Python.mp466.23MB

41. Time Series - SARIMA model/3. Stationary time Series.mp45.58MB

42. Bonus Section/1. The final milestone!.mp411.85MB

5. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4109.18MB

5. Introduction to Machine Learning/2. Building a Machine Learning Model.mp439.48MB

6. Data Preprocessing/1. Gathering Business Knowledge.mp422.29MB

6. Data Preprocessing/10. Outlier Treatment in Python.mp470.26MB

6. Data Preprocessing/11. Outlier Treatment in R.mp430.74MB

6. Data Preprocessing/12. Missing Value Imputation.mp425MB

6. Data Preprocessing/13. Missing Value Imputation in Python.mp423.42MB

6. Data Preprocessing/14. Missing Value imputation in R.mp426.01MB

6. Data Preprocessing/15. Seasonality in Data.mp417.02MB

6. Data Preprocessing/16. Bi-variate analysis and Variable transformation.mp4100.4MB

6. Data Preprocessing/17. Variable transformation and deletion in Python.mp444.12MB

6. Data Preprocessing/18. Variable transformation in R.mp455.43MB

6. Data Preprocessing/19. Non-usable variables.mp420.25MB

6. Data Preprocessing/2. Data Exploration.mp420.51MB

6. Data Preprocessing/20. Dummy variable creation Handling qualitative data.mp436.81MB

6. Data Preprocessing/21. Dummy variable creation in Python.mp426.53MB

6. Data Preprocessing/22. Dummy variable creation in R.mp443.99MB

6. Data Preprocessing/23. Correlation Analysis.mp471.6MB

6. Data Preprocessing/24. Correlation Analysis in Python.mp455.3MB

6. Data Preprocessing/25. Correlation Matrix in R.mp483.13MB

6. Data Preprocessing/3. The Dataset and the Data Dictionary.mp469.29MB

6. Data Preprocessing/4. Importing Data in Python.mp427.84MB

6. Data Preprocessing/5. Importing the dataset into R.mp413.12MB

6. Data Preprocessing/6. Univariate analysis and EDD.mp424.19MB

6. Data Preprocessing/7. EDD in Python.mp461.81MB

6. Data Preprocessing/8. EDD in R.mp496.98MB

6. Data Preprocessing/9. Outlier Treatment.mp424.5MB

7. Linear Regression/1. The Problem Statement.mp49.37MB

7. Linear Regression/10. Multiple Linear Regression in Python.mp469.74MB

7. Linear Regression/11. Multiple Linear Regression in R.mp462.38MB

7. Linear Regression/12. Test-train split.mp441.88MB

7. Linear Regression/13. Bias Variance trade-off.mp425.09MB

7. Linear Regression/14. Test train split in Python.mp444.88MB

7. Linear Regression/15. Test-Train Split in R.mp475.6MB

7. Linear Regression/16. Regression models other than OLS.mp416.55MB

7. Linear Regression/17. Subset selection techniques.mp479.07MB

7. Linear Regression/18. Subset selection in R.mp463.53MB

7. Linear Regression/19. Shrinkage methods Ridge and Lasso.mp433.34MB

7. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp443.37MB

7. Linear Regression/20. Ridge regression and Lasso in Python.mp4128.85MB

7. Linear Regression/21. Ridge regression and Lasso in R.mp4103.43MB

7. Linear Regression/22. Heteroscedasticity.mp414.49MB

7. Linear Regression/3. Assessing accuracy of predicted coefficients.mp492.11MB

7. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp443.6MB

7. Linear Regression/5. Simple Linear Regression in Python.mp463.43MB

7. Linear Regression/6. Simple Linear Regression in R.mp440.83MB

7. Linear Regression/7. Multiple Linear Regression.mp434.32MB

7. Linear Regression/8. The F - statistic.mp455.99MB

7. Linear Regression/9. Interpreting results of Categorical variables.mp422.5MB

8. Classification Models Data Preparation/1. The Data and the Data Dictionary.mp479.01MB

8. Classification Models Data Preparation/10. Variable transformation and Deletion in Python.mp429.26MB

8. Classification Models Data Preparation/11. Variable transformation in R.mp438.03MB

8. Classification Models Data Preparation/12. Dummy variable creation in Python.mp426.37MB

8. Classification Models Data Preparation/13. Dummy variable creation in R.mp444.36MB

8. Classification Models Data Preparation/2. Data Import in Python.mp422.06MB

8. Classification Models Data Preparation/3. Importing the dataset into R.mp413.47MB

8. Classification Models Data Preparation/4. EDD in Python.mp477.63MB

8. Classification Models Data Preparation/5. EDD in R.mp466.52MB

8. Classification Models Data Preparation/6. Outlier treatment in Python.mp447.32MB

8. Classification Models Data Preparation/7. Outlier Treatment in R.mp425.37MB

8. Classification Models Data Preparation/8. Missing Value Imputation in Python.mp422.56MB

8. Classification Models Data Preparation/9. Missing Value imputation in R.mp419.05MB

9. The Three classification models/1. Three Classifiers and the problem statement.mp420.34MB

9. The Three classification models/2. Why can't we use Linear Regression.mp416.94MB