Bc update (oneapi-src#1155)

* updated sample.json and Teacherkit.ipynb

* remove ANSWERS

* update sample.json w pip install -r  requirements.txt

* fixes to bracnh bc-update: fix broken links, consolodate data to common data folder, answers in pw protected zip

* fix broken links lower level READMEs

* Update sample.json

replace sample.json with Piotr's updates last month

* Update sample.json

Co-authored-by: Jimmy Wei <[email protected]>

Author: zmadscientist

AI-and-Analytics/Jupyter/Introduction_to_Machine_Learning/01_Introduction_to_Machine_Learning_and_Tools/Introduction_to_Machine_Learning_and_Toolkit-ANSWERS.ipynb

@@ -95,7 +95,7 @@
    "source": [
     "from __future__ import print_function\n",
     "import os\n",
-    "data_path = [ 'data']"
+    "data_path = [ '../data']"
    ]
   },
   {
@@ -366,7 +366,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3.8 (Intel® oneAPI)",
+   "display_name": "Python 3 (Intel® oneAPI 2022.3)",
    "language": "python",
    "name": "c009-intel_distribution_of_python_3_oneapi-beta05-python"
   },
@@ -380,7 +380,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.10"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,

AI-and-Analytics/Jupyter/Introduction_to_Machine_Learning/01_Introduction_to_Machine_Learning_and_Tools/Introduction_to_Machine_Learning_and_Toolkit.ipynb

@@ -19,8 +19,13 @@ At the end of this course you will be able to:
 - Demonstrate supervised learning algorithms
 - Explain key concepts like under- and over-fitting, regularization, and cross-validation
 - Classify the type of problem to be solved, choose the right algorithm, tune parameters, and validate a model
-- Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware
-- Download and Install Intel® Extension for Scikit-learn* powered by oneAPI
+- Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware using two lines of code
+``` python
+      from sklearnex import patch_sklearn
+      patch_sklearn()
+```
+- Reference describing: Download and Install Intel® Extension for Scikit-learn* powered by oneAPI
+
 
 
 ## License  
@@ -38,46 +43,3 @@ Third party program Licenses can be found here: [third-party-programs.txt](https
 - Statistics
 
 
-## Syllabus
-
-- 11 Modules (18 hours)
-- 11 Lab Exercises
-
------------------------
-| Modules | Description | Duration |
-| :--- | :------ | :------ |
-|[01_Introduction_to_Machine_Learning_and_Tools](01_Introduction_to_Machine_Learning_and_Tools/Introduction_to_Machine_Learning_and_Toolkit.ipynb)| + Demonstrate supervised learning algorithms.<br> + Explain key concepts like under- and over-fitting, regularization, and cross-validation<br>: + Classify the type of problem to be solved <br> + Choose an algorithm, tune parameters, and validate a model <br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 60 min |
-|[02-Introduction_to_Supervised_Learning_and_K_Nearest_Neighbors](02-Introduction_to_Supervised_Learning_and_K_Nearest_Neighbors/Supervised_Learning_and_K_Nearest_Neighbors_Exercises.ipynb)| + Explain supervised learning and how it can be applied to regression and classification problems.<br> + Apply K-Nearest Neighbor (KNN) algorithm for classification <br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[03-Train_Test_Splits_Validation_Linear_Regression](03-Train_Test_Splits_Validation_Linear_Regression/Train_Test_Splits_Validation_Linear_Regression.ipynb)| + Explain the difference between over-fitting and under-fitting a model<br> + Describe Bias-variance tradeoffs<br> + Find the optimal training and test data set splits, cross-validation, and model complexity versus error<br> + Apply a linear regression model for supervised learning.<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[04-Regularization_and_Gradient_Descent](04-Regularization_and_Gradient_Descent/Regularization_and_Gradient_Descent_Exercises.ipynb)| + Describe Logistic regression and how it differs from linear regression<br> + Identify metrics for classification errors and scenarios in which they can be used<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[05-Logistic_Regression_and_Classification_Error_Metrics](05-Logistic_Regression_and_Classification_Error_Metrics/Logistic_Regression_and_Classification_Error_Metrics_Exercises.ipynb)| + Explain cost functions, regularization, feature selection, and hyper-parameters<br> + | 120 min |
-|[06-SVM_and_Kernels](06-SVM_and_Kernels/SVM_Kernels_Exercises.ipynb)| + Apply support vector machines (SVMs)—a popular algorithm used for classification problems<br> + Recognize SVM similarity to logistic regression<br> + Compute the cost function of SVMs<br> + Apply regularization in SVMs and some tips to obtain non-linear classifications with SVMs<nbr> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware | 120 min |
-|[07-Decision_Trees](07-Decision_Trees/Decision_Trees_Exercises.ipynb)| + Recognize Decision trees and how to use them for classification problems<br> + Recognize how to identify the best split and the factors for splitting<br> + Explain strengths and weaknesses of decision trees<br> + Explain how regression trees help with classifying continuous values<br> + Describe motivation for choosing Random Forest Classifier over Decision Trees<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware for Random Forest Classifier| 120 min |
-|[08-Bagging](08-Bagging/Bagging_Exercises.ipynbb)| + Associate concepts of bootstrapping and aggregating (commonly known as “bagging”) to reduce variance<br> + Apply Random Forest algorithm that further reduces the correlation seen in bagging models<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[09-Boosting_and_Stacking](09-Boosting_and_Stacking/Boosting_and_Stacking_Exercises.ipynb)| + Explain how the boosting algorithm helps reduce variance and bias.<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[10-Introduction_to_Unsupervised_Learning_and_Clustering_Methods](10-Introduction_to_Unsupervised_Learning_and_Clustering_Methods/Clustering_Methods_Exercises.ipynb)| + Discuss unsupervised learning algorithms and how they can be applied<br> + Apply clustering<br> + Apply dimensionality reduction<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[11-Dimensionality_Reduction_and_Advanced_Topics](11-Dimensionality_Reduction_and_Advanced_Topics/Dimensionality_Reduction_Exercises.ipynb)| + Explain and Apply Principal Component Analysis (PCA)<br> + Explain Multidimensional Scaling (MDS)<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-    
-#### Content Structure
-
-Each module folder has a Jupyter Notebook file (`*.ipynb`), this can be opened in Jupyter Lab to view the training contant, edit code and compile/run. 
-
-## Install Directions
-
-The training content can be accessed locally on the computer after installing necessary tools, or you can directly access using Intel DevCloud without any installation.
-
-#### Local Installation of JupyterLab and oneAPI Tools
-
-The Jupyter Notebooks can be downloaded locally to computer and accessed:
-- Install Jupyter Lab on local computer: [Installation Guide](https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html)
-- Install Intel oneAPI Base Toolkit on local computer: [Installation Guide](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) 
-- git clone the repo and access the Notebooks using Jupyter Lab
-
-
-#### Access using Intel DevCloud
-
-The Jupyter notebooks are tested and can be run on Intel DevCloud without any installation necessary, below are the steps to access these Jupyter notebooks on Intel DevCloud:
-1. Register on [Intel DevCloud](https://devcloud.intel.com/oneapi)
-2. Login, Get Started and Launch Jupyter Lab
-3. Open Terminal in Jupyter Lab and git clone the repo and access the Notebooks
-

AI-and-Analytics/Jupyter/Introduction_to_Machine_Learning/01_Introduction_to_Machine_Learning_and_Tools/README.md

@@ -18,7 +18,11 @@ At the end of this course you will be able to:
 
 - Explain supervised learning and how it can be applied to regression and classification problems
 - Apply K-Nearest Neighbor (KNN) algorithm for classification
-- Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware
+- Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware using two lines of code
+``` python
+      from sklearnex import patch_sklearn
+      patch_sklearn()
+```
 
 ## License  
 Code samples 
@@ -35,46 +39,4 @@ Third party program Licenses can be found here: [third-party-programs.txt](https
 - Statistics
 
 
-## Syllabus
-
-- 11 Modules (18 hours)
-- 11 Lab Exercises
-
------------------------
-| Modules | Description | Duration |
-| :--- | :------ | :------ |
-|[01_Introduction_to_Machine_Learning_and_Tools](01_Introduction_to_Machine_Learning_and_Tools/Introduction_to_Machine_Learning_and_Toolkit.ipynb)| + Demonstrate supervised learning algorithms.<br> + Explain key concepts like under- and over-fitting, regularization, and cross-validation<br>: + Classify the type of problem to be solved <br> + Choose an algorithm, tune parameters, and validate a model <br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 60 min |
-|[02-Introduction_to_Supervised_Learning_and_K_Nearest_Neighbors](02-Introduction_to_Supervised_Learning_and_K_Nearest_Neighbors/Supervised_Learning_and_K_Nearest_Neighbors_Exercises.ipynb)| + Explain supervised learning and how it can be applied to regression and classification problems.<br> + Apply K-Nearest Neighbor (KNN) algorithm for classification <br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[03-Train_Test_Splits_Validation_Linear_Regression](03-Train_Test_Splits_Validation_Linear_Regression/Train_Test_Splits_Validation_Linear_Regression.ipynb)| + Explain the difference between over-fitting and under-fitting a model<br> + Describe Bias-variance tradeoffs<br> + Find the optimal training and test data set splits, cross-validation, and model complexity versus error<br> + Apply a linear regression model for supervised learning.<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[04-Regularization_and_Gradient_Descent](04-Regularization_and_Gradient_Descent/Regularization_and_Gradient_Descent_Exercises.ipynb)| + Describe Logistic regression and how it differs from linear regression<br> + Identify metrics for classification errors and scenarios in which they can be used<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[05-Logistic_Regression_and_Classification_Error_Metrics](05-Logistic_Regression_and_Classification_Error_Metrics/Logistic_Regression_and_Classification_Error_Metrics_Exercises.ipynb)| + Explain cost functions, regularization, feature selection, and hyper-parameters<br> + | 120 min |
-|[06-SVM_and_Kernels](06-SVM_and_Kernels/SVM_Kernels_Exercises.ipynb)| + Apply support vector machines (SVMs)—a popular algorithm used for classification problems<br> + Recognize SVM similarity to logistic regression<br> + Compute the cost function of SVMs<br> + Apply regularization in SVMs and some tips to obtain non-linear classifications with SVMs<nbr> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware | 120 min |
-|[07-Decision_Trees](07-Decision_Trees/Decision_Trees_Exercises.ipynb)| + Recognize Decision trees and how to use them for classification problems<br> + Recognize how to identify the best split and the factors for splitting<br> + Explain strengths and weaknesses of decision trees<br> + Explain how regression trees help with classifying continuous values<br> + Describe motivation for choosing Random Forest Classifier over Decision Trees<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware for Random Forest Classifier| 120 min |
-|[08-Bagging](08-Bagging/Bagging_Exercises.ipynbb)| + Associate concepts of bootstrapping and aggregating (commonly known as “bagging”) to reduce variance<br> + Apply Random Forest algorithm that further reduces the correlation seen in bagging models<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[09-Boosting_and_Stacking](09-Boosting_and_Stacking/Boosting_and_Stacking_Exercises.ipynb)| + Explain how the boosting algorithm helps reduce variance and bias.<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[10-Introduction_to_Unsupervised_Learning_and_Clustering_Methods](10-Introduction_to_Unsupervised_Learning_and_Clustering_Methods/Clustering_Methods_Exercises.ipynb)| + Discuss unsupervised learning algorithms and how they can be applied<br> + Apply clustering<br> + Apply dimensionality reduction<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-|[11-Dimensionality_Reduction_and_Advanced_Topics](11-Dimensionality_Reduction_and_Advanced_Topics/Dimensionality_Reduction_Exercises.ipynb)| + Explain and Apply Principal Component Analysis (PCA)<br> + Explain Multidimensional Scaling (MDS)<br> + Apply Intel® Extension for Scikit-learn* to leverage underlying compute capabilities of hardware| 120 min |
-    
-#### Content Structure
-
-Each module folder has a Jupyter Notebook file (`*.ipynb`), this can be opened in Jupyter Lab to view the training contant, edit code and compile/run. 
-
-## Install Directions
-
-The training content can be accessed locally on the computer after installing necessary tools, or you can directly access using Intel DevCloud without any installation.
-
-#### Local Installation of JupyterLab and oneAPI Tools
-
-The Jupyter Notebooks can be downloaded locally to computer and accessed:
-- Install Jupyter Lab on local computer: [Installation Guide](https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html)
-- Install Intel oneAPI Base Toolkit on local computer: [Installation Guide](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) 
-- git clone the repo and access the Notebooks using Jupyter Lab
-
-
-#### Access using Intel DevCloud
-
-The Jupyter notebooks are tested and can be run on Intel DevCloud without any installation necessary, below are the steps to access these Jupyter notebooks on Intel DevCloud:
-1. Register on [Intel DevCloud](https://devcloud.intel.com/oneapi)
-2. Login, Get Started and Launch Jupyter Lab
-3. Open Terminal in Jupyter Lab and git clone the repo and access the Notebooks