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   "source": [
    "# What is Machine Learning, and how does it work? ([video #1](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1))\n",
    "\n",
    "Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Machine Learning](images/01_robot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agenda\n",
    "\n",
    "- What is Machine Learning?\n",
    "- What are the two main categories of Machine Learning?\n",
    "- What are some examples of Machine Learning?\n",
    "- How does Machine Learning \"work\"?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Machine Learning?\n",
    "\n",
    "One definition: \"Machine Learning is the semi-automated extraction of knowledge from data\"\n",
    "\n",
    "- **Knowledge from data**: Starts with a question that might be answerable using data\n",
    "- **Automated extraction**: A computer provides the insight\n",
    "- **Semi-automated**: Requires many smart decisions by a human"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the two main categories of Machine Learning?\n",
    "\n",
    "**Supervised learning**: Making predictions using data\n",
    "    \n",
    "- Example: Is a given email \"spam\" or \"ham\"?\n",
    "- There is an outcome we are trying to predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Spam filter](images/01_spam_filter.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Unsupervised learning**: Extracting structure from data\n",
    "\n",
    "- Example: Segment grocery store shoppers into clusters that exhibit similar behaviors\n",
    "- There is no \"right answer\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Clustering](images/01_clustering.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How does Machine Learning \"work\"?\n",
    "\n",
    "High-level steps of supervised learning:\n",
    "\n",
    "1. First, train a **Machine Learning model** using **labeled data**\n",
    "\n",
    "    - \"Labeled data\" has been labeled with the outcome\n",
    "    - \"Machine Learning model\" learns the relationship between the attributes of the data and its outcome\n",
    "\n",
    "2. Then, make **predictions** on **new data** for which the label is unknown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Supervised learning](images/01_supervised_learning.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The primary goal of supervised learning is to build a model that \"generalizes\": It accurately predicts the **future** rather than the **past**!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions about Machine Learning\n",
    "\n",
    "- How do I choose **which attributes** of my data to include in the model?\n",
    "- How do I choose **which model** to use?\n",
    "- How do I **optimize** this model for best performance?\n",
    "- How do I ensure that I'm building a model that will **generalize** to unseen data?\n",
    "- Can I **estimate** how well my model is likely to perform on unseen data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources\n",
    "\n",
    "- Book: [An Introduction to Statistical Learning](https://www.statlearning.com/) (section 2.1, 14 pages)\n",
    "- Video: [Learning Paradigms](https://www.youtube.com/watch?v=mbyG85GZ0PI&t=2162s) (13 minutes, starting at 36:02)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comments or Questions?\n",
    "\n",
    "- Email: <kevin@dataschool.io>\n",
    "- Website: https://www.dataschool.io\n",
    "- Twitter: [@justmarkham](https://twitter.com/justmarkham)\n",
    "\n",
    "© 2021 [Data School](https://www.dataschool.io). All rights reserved."
   ]
  }
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