diff --git a/.env.sample b/.env.sample
index e69de29..0f3ca21 100644
--- a/.env.sample
+++ b/.env.sample
@@ -0,0 +1,11 @@
+# OpenAI Provider
+OPENAI_API_KEY='<add your OpenAI API key here>'
+
+## Azure OpenAI
+AZURE_OPENAI_KEY='<add your AOAI key here>'
+AZURE_OPENAI_ENDPOINT='<add your AOIA service endpoint here>'
+AZURE_OPENAI_DEPLOYMENT='<add your chat completion model name here>' 
+AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT='<add your embeddings model name here>'
+
+## Hugging Face
+HUGGING_FACE_API_KEY='<add your HuggingFace API or token here>'
\ No newline at end of file
diff --git a/docs/public/notebooks/100/lab-01.html b/docs/public/notebooks/100-01.html
similarity index 94%
rename from docs/public/notebooks/100/lab-01.html
rename to docs/public/notebooks/100-01.html
index c643c19..12fbc40 100644
--- a/docs/public/notebooks/100/lab-01.html
+++ b/docs/public/notebooks/100-01.html
@@ -3,7 +3,7 @@
 <html lang="en">
 <head><meta charset="utf-8"/>
 <meta content="width=device-width, initial-scale=1.0" name="viewport"/>
-<title>lab-01</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<title>100-01</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
 <style type="text/css">
     pre { line-height: 125%; }
 td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -7516,12 +7516,7 @@
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="Python-Basics:-Lab-01">Python Basics: Lab 01<a class="anchor-link" href="#Python-Basics:-Lab-01">¶</a></h1><p>Exercises will cover basic Python syntax, data structures, and functions. Open the Outline view in Visual Studio Code to see the syllabus at a glance. Each exercise should take under 5 minutes to complete. Useful resources for reference include:</p>
-<ol>
-<li><a href="https://www.kaggle.com/learn/python">Kaggle Python Course</a> - short, task-driven</li>
-<li><a href="https://runestone.academy/ns/books/published/fopp/index.html">Foundations of Python Programming</a> - long, detail-driven</li>
-<li><a href="https://learnpythonthehardway.org/python3/">Learn Python The Hard Way</a> - online, exercises-driven</li>
-</ol>
+<h1 id="100-01:-Getting-Started">100-01: Getting Started<a class="anchor-link" href="#100-01:-Getting-Started">¶</a></h1><p>Use this notebook to validate that you have your development environment setup correctly, and that you are familiar with basics of relevant tools for learning Python and Python-adjacent topics on the roadmap. Notebooks are meant to be code-centric (Python cells) with short explainers (Markdown cells) to provide learning context or instructions. Resources referenced for learning will be listed in the Table Of Contents for the level (100). Where required, additional resources used only in a specific notebook will be listed within it, at this title level.</p>
 </div>
 </div>
 </div>
@@ -7532,6 +7527,254 @@ <h1 id="Python-Basics:-Lab-01">Python Basics: Lab 01<a class="anchor-link" href=
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<hr/>
+<h3 id="1.-Verify-Setup-with-%22Hello,-World!%22">1. Verify Setup with "Hello, World!"<a class="anchor-link" href="#1.-Verify-Setup-with-%22Hello,-World!%22">¶</a></h3><ul>
+<li>We'll use comments in Python code cells to explain context or provide instructions</li>
+<li>Use "#" for single-line comments, typically for explainers</li>
+<li>Use triple quotes for multi-line comments, typically for instructions</li>
+<li>See example below - this code also helps verify that your setup is correct</li>
+</ul>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># This is a single line comment used for short explanations or context.</span>
+<span class="c1"># I can add multiple lines by marking each line separately.</span>
+
+<span class="sd">''' </span>
+<span class="sd">This is a multi-line comment useful for writing instructions or long explainers</span>
+<span class="sd">Example:</span>
+<span class="sd">Let's use this code to verify that our environment is setup correctly.</span>
+<span class="sd">1. Launch a dev container with this repo (local Docker Desktop or cloud Github Codespaces)</span>
+<span class="sd">1. Open this file in Visual Studio Code in that dev container environment</span>
+<span class="sd">1. "Select kernel" (top right) and pick the default Python environment (e.g., Python 3.10.13)</span>
+<span class="sd">1. Select "Run" (just this cell, left) to execute code below</span>
+<span class="sd">1. Verify you see "Hello, World!" output below this cell (comments are ignored by Python interpreter)</span>
+
+<span class="sd">Congratulations. You just ran your first Python program in Jupyter-Codespaces.</span>
+<span class="sd">'''</span>
+
+<span class="c1"># This is your first Python program</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"Hello, World!"</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Hello, World!
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<hr/>
+<h3 id="2.-Use-Python-Interpreter-from-Command-Line">2. Use Python Interpreter from Command-Line<a class="anchor-link" href="#2.-Use-Python-Interpreter-from-Command-Line">¶</a></h3><ul>
+<li>The Python interpreter can be used to run Python code directly from the command line.</li>
+<li>You can run the Python interpreter by typing <code>python3</code> in the Visual Studio Code terminal.</li>
+<li>Open a terminal in Visual Studio Code from menu or by typing <kbd>Ctrl+`</kbd></li>
+<li>Then start the interpreter and explore Python commands interactively.</li>
+</ul>
+<p>The <strong>Markdown cell</strong> below shows a sample session (empty lines added for clarity only)</p>
+<ul>
+<li>The <code>python3</code> command is used to start the Python interpreter.</li>
+<li>The <code>&gt;&gt;&gt;</code> prompt indicates the Python interpreter is ready to accept commands.</li>
+<li>The <code>exit()</code> command is used to quit the Python interpreter.</li>
+</ul>
+<blockquote>
+<p>Note that the cell below is not executable code!
+You can use it for reference and type those commands into the terminal yourself.</p>
+</blockquote>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div class="highlight"><pre><span></span><span class="n">codespace</span> <span class="err">➜</span> <span class="o">.../</span><span class="n">src</span><span class="o">/</span><span class="n">content</span><span class="o">/</span><span class="n">docs</span><span class="o">/</span><span class="mi">100</span> <span class="p">(</span><span class="n">main</span><span class="p">)</span> <span class="err">$</span> <span class="n">python3</span>
+<span class="n">Python</span> <span class="mf">3.10.13</span> <span class="p">(</span><span class="n">main</span><span class="p">,</span> <span class="n">Mar</span>  <span class="mi">5</span> <span class="mi">2024</span><span class="p">,</span> <span class="mi">18</span><span class="p">:</span><span class="mi">35</span><span class="p">:</span><span class="mi">01</span><span class="p">)</span> <span class="p">[</span><span class="n">GCC</span> <span class="mf">9.4.0</span><span class="p">]</span> <span class="n">on</span> <span class="n">linux</span>
+<span class="n">Type</span> <span class="s2">"help"</span><span class="p">,</span> <span class="s2">"copyright"</span><span class="p">,</span> <span class="s2">"credits"</span> <span class="ow">or</span> <span class="s2">"license"</span> <span class="k">for</span> <span class="n">more</span> <span class="n">information</span><span class="o">.</span>
+
+<span class="o">&gt;&gt;&gt;</span> <span class="n">hudson_valley</span> <span class="o">=</span> <span class="s2">"The most amazing place on earth!"</span>
+
+<span class="o">&gt;&gt;&gt;</span> <span class="nb">print</span><span class="p">(</span><span class="s2">"Hudson Valley is ..</span><span class="se">\n</span><span class="s2">"</span><span class="p">,</span> <span class="n">hudson_valley</span><span class="p">)</span>
+<span class="n">Hudson</span> <span class="n">Valley</span> <span class="ow">is</span> <span class="o">..</span>
+ <span class="n">The</span> <span class="n">most</span> <span class="n">amazing</span> <span class="n">place</span> <span class="n">on</span> <span class="n">earth</span><span class="err">!</span>
+
+<span class="o">&gt;&gt;&gt;</span> <span class="n">exit</span><span class="p">()</span>
+<span class="n">codespace</span> <span class="err">➜</span> <span class="o">.../</span><span class="n">src</span><span class="o">/</span><span class="n">content</span><span class="o">/</span><span class="n">docs</span><span class="o">/</span><span class="mi">100</span> <span class="p">(</span><span class="n">main</span><span class="p">)</span> <span class="err">$</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<hr/>
+<h3 id="3.-Execute-terminal-commands-from-Jupyter-Notebook-cells">3. Execute terminal commands from Jupyter Notebook cells<a class="anchor-link" href="#3.-Execute-terminal-commands-from-Jupyter-Notebook-cells">¶</a></h3><p>Sometimes you will need to execute terminal commands prior to running code in a Jupyter cell. For instance, you may want to install a package for a specific purpose (and perhaps uninstall it when done) without having to switch contexts between Jupyter notebook and Visual Studio Code terminal. You can do this by running shell commands directly with <code>!</code> or by using a Python module called <code>subprocess</code> to invoke shell commands from within Python code. <strong>By documenting and executing these from a Jupyter Notebook cell, you make your code reproducible.</strong></p>
+<p>Try the exercises below to see options in action:</p>
+<ul>
+<li>Use <code>!</code> to run shell commands from a Jupyter Notebook cell.</li>
+<li>Use <code>!pip </code> to run shell commands to list, install or manage Python packages.</li>
+<li>Use the <code>subprocess</code> module to run shell commands from within Python code.</li>
+<li>Check the docs for more details on <a href="https://docs.python.org/3/library/subprocess.html#using-the-subprocess-module"><code>subprocess.run()</code></a></li>
+</ul>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Print "Hudson Valley is beautiful" in command line</span>
+<span class="o">!</span><span class="w"> </span><span class="nb">echo</span><span class="w"> </span><span class="s2">"Hudson Valley is beautiful"</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Hudson Valley is beautiful
+</pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Test if a specific package is installed (e.g. mkdocs-material)</span>
+<span class="o">!</span><span class="w"> </span>pip<span class="w"> </span>show<span class="w"> </span>mkdocs-material
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre><span class="ansi-yellow-fg">WARNING: Package(s) not found: mkdocs-material</span><span class="ansi-yellow-fg">
+</span></pre>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Install the package (e.g. mkdocs-material) </span>
+<span class="c1"># Use -q to suppress verbose output (use -qq or -qqq for less output)</span>
+<span class="c1"># Suppression levels are: warnings, errors, and critical</span>
+<span class="o">!</span><span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>mkdocs-material<span class="w"> </span>-q<span class="w"> </span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
+<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+<div class="cm-editor cm-s-jupyter">
+<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Uninstall the installed package (mkdocs-material)</span>
+
+<span class="c1"># Option 1: Use shell command with -y to automatically confirm prompts</span>
+<span class="c1">#  !pip uninstall mkdocs-material -y` </span>
+
+<span class="c1"># Option 2: Use "subprocess" module to run command from Python code</span>
+<span class="c1">#  This introduces the concept of Python modules and imports (covered in depth later)</span>
+<span class="kn">import</span> <span class="nn">subprocess</span>
+<span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="s1">'pip'</span><span class="p">,</span> <span class="s1">'uninstall'</span><span class="p">,</span> <span class="s1">'mkdocs-material'</span><span class="p">,</span> <span class="s1">'-y'</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell-outputWrapper">
+<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
+</div>
+<div class="jp-OutputArea jp-Cell-outputArea">
+<div class="jp-OutputArea-child">
+<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+<pre>Found existing installation: mkdocs-material 9.5.17
+Uninstalling mkdocs-material-9.5.17:
+  Successfully uninstalled mkdocs-material-9.5.17
+</pre>
+</div>
+</div>
+<div class="jp-OutputArea-child jp-OutputArea-executeResult">
+<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+<pre>CompletedProcess(args=['pip', 'uninstall', 'mkdocs-material', '-y'], returncode=0)</pre>
+</div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<hr/>
+<h3 id=""><a class="anchor-link" href="#">¶</a></h3>
 </div>
 </div>
 </div>
diff --git a/docs/src/assets/prompt-engineering-roadmap.png b/docs/src/assets/prompt-engineering-roadmap.png
new file mode 100644
index 0000000..fcd6488
Binary files /dev/null and b/docs/src/assets/prompt-engineering-roadmap.png differ
diff --git a/docs/src/assets/python-roadmap.png b/docs/src/assets/python-roadmap.png
new file mode 100644
index 0000000..5945bc4
Binary files /dev/null and b/docs/src/assets/python-roadmap.png differ
diff --git a/docs/src/content/docs/100/100-01.ipynb b/docs/src/content/docs/100/100-01.ipynb
new file mode 100644
index 0000000..3d1bf97
--- /dev/null
+++ b/docs/src/content/docs/100/100-01.ipynb
@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 100-01: Getting Started\n",
+    "\n",
+    "Use this notebook to validate that you have your development environment setup correctly, and that you are familiar with basics of relevant tools for learning Python and Python-adjacent topics on the roadmap. Notebooks are meant to be code-centric (Python cells) with short explainers (Markdown cells) to provide learning context or instructions. \n",
+    "\n",
+    "Resources referenced for learning will be listed in the Table Of Contents for the level (100). Where required, additional resources used only in a specific notebook will be listed within it, at this title level."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "### 1. Verify Setup with \"Hello, World!\"\n",
+    "\n",
+    "- We'll use comments in Python code cells to explain context or provide instructions\n",
+    "- Use \"#\" for single-line comments, typically for explainers\n",
+    "- Use triple quotes for multi-line comments, typically for instructions\n",
+    "- See example below - this code also helps verify that your setup is correct"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello, World!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This is a single line comment used for short explanations or context.\n",
+    "# I can add multiple lines by marking each line separately.\n",
+    "\n",
+    "''' \n",
+    "This is a multi-line comment useful for writing instructions or long explainers\n",
+    "Example:\n",
+    "Let's use this code to verify that our environment is setup correctly.\n",
+    "1. Launch a dev container with this repo (local Docker Desktop or cloud Github Codespaces)\n",
+    "1. Open this file in Visual Studio Code in that dev container environment\n",
+    "1. \"Select kernel\" (top right) and pick the default Python environment (e.g., Python 3.10.13)\n",
+    "1. Select \"Run\" (just this cell, left) to execute code below\n",
+    "1. Verify you see \"Hello, World!\" output below this cell (comments are ignored by Python interpreter)\n",
+    "\n",
+    "Congratulations. You just ran your first Python program in Jupyter-Codespaces.\n",
+    "'''\n",
+    "\n",
+    "# This is your first Python program\n",
+    "print(\"Hello, World!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "### 2. Use Python Interpreter from Command-Line\n",
+    "\n",
+    "- The Python interpreter can be used to run Python code directly from the command line.\n",
+    "- You can run the Python interpreter by typing `python3` in the Visual Studio Code terminal.\n",
+    "- Open a terminal in Visual Studio Code from menu or by typing <kbd>Ctrl+`</kbd>\n",
+    "- Then start the interpreter and explore Python commands interactively.\n",
+    "\n",
+    "The **Markdown cell** below shows a sample session (empty lines added for clarity only)\n",
+    "- The `python3` command is used to start the Python interpreter.\n",
+    "- The `>>>` prompt indicates the Python interpreter is ready to accept commands.\n",
+    "- The `exit()` command is used to quit the Python interpreter.\n",
+    "\n",
+    "> Note that the cell below is not executable code! \n",
+    "> You can use it for reference and type those commands into the terminal yourself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "```python\n",
+    "codespace ➜ .../src/content/docs/100 (main) $ python3\n",
+    "Python 3.10.13 (main, Mar  5 2024, 18:35:01) [GCC 9.4.0] on linux\n",
+    "Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n",
+    "\n",
+    ">>> hudson_valley = \"The most amazing place on earth!\"\n",
+    "\n",
+    ">>> print(\"Hudson Valley is ..\\n\", hudson_valley)\n",
+    "Hudson Valley is ..\n",
+    " The most amazing place on earth!\n",
+    "\n",
+    ">>> exit()\n",
+    "codespace ➜ .../src/content/docs/100 (main) $ \n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "### 3. Execute terminal commands from Jupyter Notebook cells\n",
+    "\n",
+    "Sometimes you will need to execute terminal commands prior to running code in a Jupyter cell. For instance, you may want to install a package for a specific purpose (and perhaps uninstall it when done) without having to switch contexts between Jupyter notebook and Visual Studio Code terminal. You can do this by running shell commands directly with `!` or by using a Python module called `subprocess` to invoke shell commands from within Python code. **By documenting and executing these from a Jupyter Notebook cell, you make your code reproducible.**\n",
+    "\n",
+    "Try the exercises below to see options in action:\n",
+    "- Use `!` to run shell commands from a Jupyter Notebook cell.\n",
+    "- Use `!pip ` to run shell commands to list, install or manage Python packages.\n",
+    "- Use the `subprocess` module to run shell commands from within Python code.\n",
+    "- Check the docs for more details on [`subprocess.run()`](https://docs.python.org/3/library/subprocess.html#using-the-subprocess-module)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hudson Valley is beautiful\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print \"Hudson Valley is beautiful\" in command line\n",
+    "! echo \"Hudson Valley is beautiful\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARNING: Package(s) not found: mkdocs-material\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "# Test if a specific package is installed (e.g. mkdocs-material)\n",
+    "! pip show mkdocs-material"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install the package (e.g. mkdocs-material) \n",
+    "# Use -q to suppress verbose output (use -qq or -qqq for less output)\n",
+    "# Suppression levels are: warnings, errors, and critical\n",
+    "! pip install mkdocs-material -q "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found existing installation: mkdocs-material 9.5.17\n",
+      "Uninstalling mkdocs-material-9.5.17:\n",
+      "  Successfully uninstalled mkdocs-material-9.5.17\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CompletedProcess(args=['pip', 'uninstall', 'mkdocs-material', '-y'], returncode=0)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Uninstall the installed package (mkdocs-material)\n",
+    "\n",
+    "# Option 1: Use shell command with -y to automatically confirm prompts\n",
+    "#  !pip uninstall mkdocs-material -y` \n",
+    "\n",
+    "# Option 2: Use \"subprocess\" module to run command from Python code\n",
+    "#  This introduces the concept of Python modules and imports (covered in depth later)\n",
+    "import subprocess\n",
+    "subprocess.run(['pip', 'uninstall', 'mkdocs-material', '-y'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "### "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/src/content/docs/100/toc.md b/docs/src/content/docs/100/toc.md
index d034732..011e32e 100644
--- a/docs/src/content/docs/100/toc.md
+++ b/docs/src/content/docs/100/toc.md
@@ -3,28 +3,53 @@ title: Table Of Contents
 description: Start by learning Python3 syntax and basics
 ---
 
-The table below will be populated with links to the relevant notebook with a brief description of the recipe covered. To open the notebook you must be running in a GitHub Codespace or have a relevant Jupyter Notebook environment setup.
+:::tip About Lessons
 
-|Lab Notebook | Recipe Notes |
-|---|---|
-| [Lab 01](../../../../../notebooks/100/lab-01.ipynb) - [HTML](/python/notebooks/100/lab-01.html) | Learn Basic Python Syntax, Get Familiar with Notebooks. | Learn Basic Python Syntax, Get Familiar with Notebooks -  |
-| | |
-| | |
-| | |
+Each Lesson has a Lab and an HTML link. 
+- The first is an executable Jupyter notebook for interactive learning. You must open this in Visual Studio Code with a suitable runtime (e.g., GitHub Codespaces). It cannot be viewed in a browser.
+- The second is a static HTML version of the notebook suitable for viewing in a browser. To view it within Visual Studio Code use `<kbd>Ctrl</kbd>+<kbd>K</kbd>, <kbd>V</kbd>`.
 
+:::
 
----
+<!-- 
+Use this command to convert a notebook in a folder to HTML and save it in the public folder.
+jupyter nbconvert --output-dir ./../../../../public/notebooks --to html <notebook-name.ipynb> 
+-->
+
+## Roadmap
+
+For this bucket, I'll start by following the broad outlines of the [Python Developer Roadmap](https://roadmap.sh/python) shown below (captured Apr 2024). Please refer to the source for the latest version. Each box has a list of relevant resources that I will use as a starting point.
+
+![](./../../../assets/python-roadmap.png)
 
-## Resources 
-I wanted to start with a structured set of courses or modules that would have an associated community (for support) and a credential (for achievement). So I decided to focus on the [Python 3 Programming Specialization](https://www.coursera.org/specializations/python-3-programming) offered by the University Of Michigan, on Coursera.
 
-It consists of these 5 courses _with an estimated completion time of 3 months at 10 hrs/week_. I expect to complete at least 2-of-5 courses in #30Days.
+Let's Follow the 
 
- 1. [Python Basics](https://www.coursera.org/learn/python-basics?specialization=python-3-programming) Course - 34 hours, 4 modules
- 1. [Python Functions, Files & Dictionaries](https://www.coursera.org/learn/python-functions-files-dictionaries?specialization=python-3-programming) Course - 31 hours, 5 modules
- 1. [Data Collection & Processing With Python](https://www.coursera.org/learn/data-collection-processing-python?specialization=python-3-programming) - 16 hours, 3 modules
- 1. [Python Classes & Inheritance](https://www.coursera.org/learn/python-classes-inheritance?specialization=python-3-programming) - 18 hours, 3 modules
- 1. [Python Project: pillow, tesseract, opencv](https://www.coursera.org/learn/python-project?specialization=python-3-programming) - 21 hours, 3 modules
+| Notebook | Web View | Description |
+|---|---|---|
+| [Lab 01](./100-01.ipynb) | [HTML](./../../../../public/notebooks/100-01.html) | **Getting Started.** Use this lab to validate your development environment is setup correctly. Learn to use Jupyter Notebooks, invoke commands from Python cells, and run commands from a VS Code terminal. |
+| Lab 02 | HTML | **Learn The Basics**. Basic Syntax. Variables & Data Types. Conditionals. Type Casting & Exceptions. Functions & Built-in Functions. Lists, Tuples, Sets & Dictionaries. |
+| Lab 03 | HTML | **Data Structures and Algorithms**. Arrays. Linked Lists. Heaps, Stacks & Queues. Hash Tables. Binary Search Trees. Recursion. Sorting Algorithms. |
+| Lab 04 | HTML | **Advanced Topics I**. Iterators. RegEx. Decorators. Lambdas. |
+| Lab 05 | HTML | **Object Oriented Programming.** Classes. Inheritance. Methods. Dunder.   |
+| Lab 06 | HTML | **Modules.** Built-in Modules. Custom Modules. |
+| Lab 07 | HTML | **Package Managers**. PyPi. Pip. Conda.  | 
+| Lab 08 | HTML | **Advanced Topics II.** List Comprehensions. Generator Expressions. Paradigms.|
+| Lab 09 | HTML | **Testing**. doctest. nose. pytest. unittest/pyUnit. Playwright. | 
+| Lab 07 | HTML | **Miscellaneous**. Developer Tools. Best Practices. End-to-End Workflows.  | 
+| | | |
+
+
+<br/>
+
+## Resources 
 
-Once this specialization is complete, I'll continue to add new sections focused on _applied learning_ exploring the usage of Python3 in specific application contexts like **data science**, **generative AI** and **web app development**.
+This will be an evolving set of resources as labs are completed.
 
+| Resource | Source | Description |
+|---|---|---|
+| [Python Developer Roadmap](https://roadmap.sh/python) | Roadmap.sh | Python Developer Roadmap. |
+| [Python 3 Documentation](https://docs.python.org/3/) | Python.org | Official Python 3 Documentation. |
+| [Learn on Kaggle](https://www.kaggle.com/learn/) | Kaggle | Python - Intro to ML - Pandas - Data Viz - Deep Learning etc. |
+| [Python 3 Specialization](https://www.coursera.org/specializations/python-3-programming) | Coursera | Python 3 Programming Specialization from U.Mich (5 courses). |
+| | | |
diff --git a/docs/src/content/docs/400/toc.md b/docs/src/content/docs/400/toc.md
index 5d612e9..0b44b2f 100644
--- a/docs/src/content/docs/400/toc.md
+++ b/docs/src/content/docs/400/toc.md
@@ -3,14 +3,64 @@ title: 1. Introduction
 description: Explore Python SDKs and tools for building generative AI applications
 ---
 
-The table below will be populated with links to the relevant notebook with a brief description of the recipe covered. To open the notebook you must be running in a GitHub Codespace or have a relevant Jupyter Notebook environment setup.
-
-|Lab Notebook | Recipe Notes |
-|---|---|
-| | |
-| | |
-| | |
-| | |
+:::tip About Lessons
+
+Each Lesson has a Lab and an HTML link. 
+- The first is an executable Jupyter notebook for interactive learning. You must open this in Visual Studio Code with a suitable runtime (e.g., GitHub Codespaces). It cannot be viewed in a browser.
+- The second is a static HTML version of the notebook suitable for viewing in a browser. To view it within Visual Studio Code use `<kbd>Ctrl</kbd>+<kbd>K</kbd>, <kbd>V</kbd>`.
+
+For LLM providers, I will focus on three deployment options: OpenAI, Azure OpenAI, and Hugging Face. 
+Check the `.env.sample` for updated LLM providers and relevant environment variables needed in context.
+
+:::
+
+<!-- 
+Use this command to convert a notebook in a folder to HTML and save it in the public folder.
+jupyter nbconvert --output-dir ./../../../../public/notebooks --to html <notebook-name.ipynb> 
+-->
+
+## Roadmap
+
+For this bucket, I'll be using two key resources:
+
+1. The [Prompt Engineering Roadmap](https://roadmap.sh/prompt-engineering) shown below (captured Apr 2024). Please refer to the linekd source for updates. Each box has a list of relevant resources. 
+
+    ![](./../../../assets/prompt-engineering-roadmap.png)
+
+2. The [Deep Learning Short Courses](https://www.deeplearning.ai/short-courses/) series created in conjunction with key industry partners. The table lists courses in chronological order (older courses may have deprecated concepts or APIs). Levels are 100 (beginner) to 300 (advanced) - and most should have a Python SDK or sample. **All courses are 1 hour in length unless specified otherwise.** Most require basic Python knowledge. Some require familiarity with advanced frameworks like PyTorch or TensorFlow.
+
+    | Course | Collaborator | Level | Description | Notebook |
+    |---|---|---|---|---|
+    | 1. [ChatGPT Prompt Engineering For Developers](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/) | Isa Fulford, Open AI| 100-300 | Go beyond the chat box. Use API access to leverage LLMs into your own applications, and learn to build a custom chatbot.|  |
+    | 2. [Building Systems with the ChatGPT API](https://www.deeplearning.ai/short-courses/building-systems-with-chatgpt/) | Isa Fulford, Open AI| 100-300 |Level up your use of LLMs. Learn to break down complex tasks, automate workflows, chain LLM calls, and get better outputs.|
+    | 3. [LangChain for LLM Application Development](https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/) | Harrison Chase, LangChain| 100 | The framework to take LLMs out of the box. Learn to use LangChain to call LLMs into new environments, and use memories, chains, and agents to take on new and complex tasks.|
+    | 4. [How Diffusion Models Work](https://www.deeplearning.ai/short-courses/how-diffusion-models-work/) | Sharon Zhou, Lamini | 200 | Learn and build diffusion models from the ground up. Start with an image of pure noise, and arrive at a final image, learning and building intuition at each step along the way.|
+    | 5. [LangChain: Chat with Your Data](https://www.deeplearning.ai/short-courses/langchain-chat-with-your-data/) | Harrison Chase, LangChain | 100 | Create a chatbot to interface with your private data and documents using LangChain.| |
+    | 6. [Building Generative AI Applications with Gradio](https://www.deeplearning.ai/short-courses/building-generative-ai-applications-with-gradio/) |Apolinário Passos, Hugging Face | 100 | Create and demo machine learning applications quickly. Share your app with the world on Hugging Face Spaces.| |
+    | 7. [Learn MLOps tools for managing, versioning, debugging and experimenting in your ML workflow.](https://www.deeplearning.ai/short-courses/evaluating-debugging-generative-ai/) | Carey Phelps, Weights & Biases | 200 |Learn MLOps tools for managing, versioning, debugging and experimenting in your ML workflow. | |
+    | 8. [Large Language Models with Semantic Search](https://www.deeplearning.ai/short-courses/large-language-models-semantic-search/) | Jay Alammar, Luis Serrano, Cohere | 100  | Learn to use LLMs to enhance search and summarize results.| |
+    | 9. [Finetuning Large Language Models](https://www.deeplearning.ai/short-courses/finetuning-large-language-models/) | Sharon Zhou, Lamini | 200 |Learn to finetune an LLM in minutes and specialize it to use your own data | |
+    | 10. [How Business Thinkers Can Start Building AI Plugins With Semantic Kernel](https://www.deeplearning.ai/short-courses/microsoft-semantic-kernel/) | John Maeda, Microsoft | 100 |Learn Microsoft’s open source orchestrator, Semantic Kernel, and develop business applications using LLMs. | |
+    | 11. [Understanding and Applying Text Embeddings](https://www.deeplearning.ai/short-courses/google-cloud-vertex-ai/) | Nikita Namjoshi, Google Cloud | 100 | Learn how to accelerate the application development process with text embeddings| |
+    | 12. [Pair Programming with a Large Language Model]() | Laurence Moroney, Google | 100  | Learn how to effectively prompt an LLM to help you improve, debug, understand, and document your code | |
+    | 13. [Functions, Tools and Agents with LangChain](https://www.deeplearning.ai/short-courses/functions-tools-agents-langchain/) | Harrison Chase, LangChain  | 200 | Learn and apply the new capabilities of LLMs as a developer tool.| |
+    | 14. [Vector Databases: from Embeddings to Applications](https://www.deeplearning.ai/short-courses/vector-databases-embeddings-applications/) | Sebastian Witalec, Weaviate | 200 | Design and execute real-world applications of vector databases. | |
+    | 15. [Quality and Safety for LLM Applications](https://www.deeplearning.ai/short-courses/quality-safety-llm-applications/) |Bernease Herman, WhyLabs | 100 | Learn how to evaluate the safety and security of your LLM applications and protect against potential risks.| |
+    | 16. [Building and Evaluating Advanced RAG Applications](https://www.deeplearning.ai/short-courses/building-evaluating-advanced-rag/) | Jerry Liu, Anupam Datta, truera | 100  | Learn how to efficiently bring Retrieval Augmented Generation (RAG) into production by enhancing retrieval techniques and mastering evaluation metrics.| |
+    | 17. [Reinforcement Learning from Human Feedback](https://www.deeplearning.ai/short-courses/reinforcement-learning-from-human-feedback/) | Nikita Namjoshi, Google Cloud | 200 | A conceptual and hands-on introduction to tuning and evaluating large language models (LLMs) using Reinforcement Learning from Human Feedback.| |
+    | 18. [Advanced Retrieval for AI with Chroma](https://www.deeplearning.ai/short-courses/advanced-retrieval-for-ai/) |Anton Troynikov, Chroma| 200 |Learn advanced retrieval techniques to improve the relevancy of retrieved results. | |
+    | 19. [Build LLM Apps with LangChain.js](https://www.deeplearning.ai/short-courses/build-llm-apps-with-langchain-js/) |Jacob Lee, LangChain | 200 | Expand your toolkits with LangChain.js, a popular JavaScript framework for building with LLMs, and get useful concepts for creating powerful, context-aware applications.| |
+    | 20. [LLMOps](https://www.deeplearning.ai/short-courses/llmops/) |Erwin Huizenga, Google Cloud | 100  |  Learn LLMOps best practices as you design and automate the steps to tune an LLM for a specific task and deploy it as a callable API. In the course, you'll tune an LLM to act as a question-answering coding expert. You can apply the methods learned here to tune your own LLM for other use cases.| |
+    | 21. [Automated Testing for LLMOps](https://www.deeplearning.ai/short-courses/automated-testing-llmops/) | Rob Zuber, CircleCI | 200 |Learn how to create an automated continuous integration (CI) pipeline to evaluate your LLM applications on every change, for faster, safer, and more efficient application development. | |
+    | 22. [Building Applications with Vector Databases](https://www.deeplearning.ai/short-courses/building-applications-vector-databases/) | Tim Tully, Pinecone | 100 | Learn to build six applications powered by vector databases: semantic search, retrieval augmented generation (RAG), anomaly detection, hybrid search, image similarity search, and recommender systems, each using a different dataset.| |
+    | 23. [Serverless LLM apps with Amazon Bedrock](https://www.deeplearning.ai/short-courses/serverless-llm-apps-amazon-bedrock/) | Mike Chambers, AWS |200  | Learn how to deploy a large language model-based application into production using serverless technology.| |
+    | 24. [Prompt Engineering with Llama 2](https://www.deeplearning.ai/short-courses/prompt-engineering-with-llama-2/) |  Amit Sangani, Meta | 100 | Learn best practices for prompting and selecting among Meta Llama 2 models.| |
+    | 25. [Open Source Models with Hugging Face](https://www.deeplearning.ai/short-courses/open-source-models-hugging-face/) | Maria K, Marc S, Younes B, HuggingFace | 100 | Learn how to easily build AI applications using open source models and Hugging Face tools.| |
+    | 26. [Knowledge Graphs for RAG](https://www.deeplearning.ai/short-courses/knowledge-graphs-rag/) | Anreas Kollegger, neo4j | 200 |Learn how to build and use knowledge graph systems to improve your retrieval augmented generation applications. | |
+    | 27. [Efficiently Serving LLMs](https://www.deeplearning.ai/short-courses/efficiently-serving-llms/) | Travis Addair, Predibase | 200 | Gain a ground-up understanding of how to serve LLM applications in production.| |
+    | 28. [JavaScript RAG Web Apps with LlamaIndex](https://www.deeplearning.ai/short-courses/javascript-rag-web-apps-with-llamaindex/) |Laurie Voss, LlamaIndex |  100 | JavaScript RAG Web Apps with LlamaIndex| |
+    | 29. [Red Teaming LLM Applications](https://www.deeplearning.ai/short-courses/red-teaming-llm-applications/) |Matteo Dora, Luca Martial, Giskard | 100 |Learn how to make safer LLM apps through red teaming | |
+    | | | | |
 
 
 ---
diff --git a/docs/src/content/docs/introduction.md b/docs/src/content/docs/introduction.md
index 99e9e9f..e69fc73 100644
--- a/docs/src/content/docs/introduction.md
+++ b/docs/src/content/docs/introduction.md
@@ -13,6 +13,10 @@ I believe in learning _by doing_, and creating content that is _reusable_ by def
  - Get **curated cookbooks** for data analysis, machine learning & generative AI
  - Just **have fun** experimenting with new tools, ideas and projects
 
+:::note THIS IS A PERSONAL PROJECT
+
+My goal is to learn in public and cite all resources I use in my learning journey. The content here is _not_ meant for production or commercial use of any kind. You are welcome to fork the repository and use it for your own personal learning journey, but please respect the licenses of any resources I've used and cite them appropriately.
+:::
 
 ## The Mindset
 
@@ -43,12 +47,19 @@ For now, the goal is to simply curate notebooks in 4 large buckets:
  - **Machine Learning**: Scikit-Learn, TensorFlow, PyTorch - MLOps Pipeline
  - **Generative AI**: Transformers, Prompt Engineering - LLMOps Pipeline
 
-Each folder will have a table of contents listing the notebooks (recipes) and the description (usage) as they are created. The goal is to allow anyone to fork the repo and walk through these notebooks as self-guided exploration of concepts - selecting the subset they need for their own **goal-oriented learning**. Ideally, a #30Days journey will consist of 4 weeks of learning - each focused on one of these buckets.
+Check the **Table Of Contents** page for a roadmap specific to that bucket, along with relevant resources used for creating learning exercises. The ideal #30Days journey will allow someone to spend 1 week on each bucket, to get a broad idea of the domains, then dive deeper into each bucket at their own pace to suit a **goal-oriented learning** approach.
+
+:::note Work In Progress
+
+While my end goal is to get a curated cookbook of reusable recipes for myself, I will not follow any specific order in creating them. Instead, I am following a **goal oriented approach** where I pick some topic I want to learn and then create a collection of recipes (notebooks) to capture my insights and study notes. This means the content will be updated and expanded over time.
+
+:::
 
 ## The Resources
 
-Here are some of the resources I reference in various labs. For more topic-specific references, look at the Table Of Contents in each section.
+I am broadly looking to explore the following roadmaps, and will likely expand this over time. Each of these roadmaps cites relevant resources that I will likely use as a starting point for that topic. The _table of contents_ for each level will contain a more comprehensive list of resources used.
 
-1. [Learn On Kaggle](https://www.kaggle.com/learn) - short and focused
-1. [Foundations of Python Programming](https://runestone.academy/ns/books/published/fopp/index.html) - long and detail-driven
-1. [Learn Python The Hard Way](https://learnpythonthehardway.org/python3/) - fast and exercises-driven
\ No newline at end of file
+1. 100 / [Python Developer Roadmap](https://roadmap.sh/python) - suggested roadmap for learning core Python
+1. 200 / [AI Data Scientist Roadmap](https://roadmap.sh/ai-data-scientist) - exploratory data analysis to deep learning and ML Ops
+1. 300 / [ML Ops Roadmap](https://roadmap.sh/mlops) - from Cloud Computing to Infrastructure-as-Code
+1. 400 / [Prompt Engineering Roadmap](https://roadmap.sh/prompt-engineering) - from LLM concepts to Prompt Hacking and Security
\ No newline at end of file
diff --git a/notebooks/100/lab-01.ipynb b/notebooks/100/lab-01.ipynb
deleted file mode 100644
index ac31bd1..0000000
--- a/notebooks/100/lab-01.ipynb
+++ /dev/null
@@ -1,29 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Python Basics: Lab 01\n",
-    "\n",
-    "Exercises will cover basic Python syntax, data structures, and functions. Open the Outline view in Visual Studio Code to see the syllabus at a glance. Each exercise should take under 5 minutes to complete. Useful resources for reference include:\n",
-    "\n",
-    "1. [Kaggle Python Course](https://www.kaggle.com/learn/python) - short, task-driven\n",
-    "1. [Foundations of Python Programming](https://runestone.academy/ns/books/published/fopp/index.html) - long, detail-driven\n",
-    "1. [Learn Python The Hard Way](https://learnpythonthehardway.org/python3/) - online, exercises-driven"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
