include links to jupyter notebooks in 3rd, 4th, and 5th episodes

Author: code4yonglei

content/03-scientific-data-for-ML.md

@@ -165,7 +165,7 @@ Why to use tensors in ML/DL (advantages of Tensor)?
 
 :::{exercise} Tensor Creation and Operations
 
-[HERE](./jupyter-notebooks/3-Tensor.ipynb) we provide a tutorial about Tensor including
+In [Jupyter Notebook](./jupyter-notebooks/3-Tensor.ipynb) we provide a tutorial about Tensor including
 - Tensor creation
 - Tensor's properties (`shape`, `dtype`, `ndim`)
 - Tensor operations

content/04-data-preparation-for-ML.md

@@ -76,7 +76,7 @@ These data were collected from 2007 - 2009 by Dr. Kristen Gorman with the [Palme
 ## Importing Dataset
 
 
-Seaborn provides the Penguins dataset through its built-in data-loading functions. We can access it using ``sns.load_dataset('penguin')`` and then have a quick look at the data:
+Seaborn provides the Penguins dataset through its built-in data-loading functions. We can access it using ``sns.load_dataset('penguin')`` and then have a quick look at the data (code examples are availalbe in the [Jupyter Notebook](./jupyter-notebooks/4-Data-Preprocessing.ipynb)):
 ```python
 import numpy as np
 import matplotlib.pyplot as plt
@@ -200,10 +200,11 @@ penguins_test['BMG_median'] = penguins_test.body_mass_g.fillna(body_mass_g_media
 
 :::{exercise}
 
-How to mutate the missing values with ``mean`` or ``median`` values in place for all numerical values.
+How to mutate the missing values with ``mean`` or ``median`` values in place for all numerical values (code examples are availalbe in the [Jupyter Notebook](./jupyter-notebooks/4-Data-Preprocessing.ipynb)).
 - for one numerical feature like ``bill_length_mm``?
 - for all numerical features in the Penguins dataset?
 :::
+
 :::{solution}
 - 1. using the following script
 	```python
@@ -316,7 +317,7 @@ There are four main techniques for handling outliers in a dataset:
 
 Instead of using the IQR method, the upper and lower thresholds for detecting outliers can also be calculated with the mean-std deviation approach.
 
-In this exercise, you will
+In this exercise (code examples are availalbe in the [Jupyter Notebook](./jupyter-notebooks/4-Data-Preprocessing.ipynb)), you will
 - Compute the ``mean`` and ``std`` of the ``body_mass_g`` feature.
 - Calculate the upper and lower limits for outlier detection using the formulas.
 	:::{math}

content/05-supervised-ML-classification.md

@@ -38,7 +38,7 @@ By comparing the results, we aim to identify which model provides the most accur
 In the previous episode, [Episode 4: Data Preparation for Machine Learning](./04-data-preparation-for-ML.md), we discussed data preparation steps, including handling missing values, detecting outliers, and encoding categorical variables.
 
 In this episode, we will revisit these steps, with particular emphasis on encoding categorical variables. For the classification task, we will treat the categorical variable ``species`` as the label (target variable) and use the remaining columns as features to predict the penguins species.
-To achieve this, we transform the categorical features ``island`` and ``sex``, as well as the ``species`` label, into numerical format.
+To achieve this, we transform the categorical features ``island`` and ``sex``, as well as the ``species`` label, into numerical format (code examples are availalbe in the [Jupyter Notebook](./jupyter-notebooks/5-ML-Classifion.ipynb)).
 
 ```python
 from sklearn.preprocessing import LabelEncoder