01_machine_learning_intro.ipynb

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     "source": [
      "# What is machine learning, and how does it work?\n",
      "*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
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     "source": [
      "![Machine learning](images/01_robot.png)"
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     "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\"?"
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     "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"
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      "## 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"
     ]
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     "source": [
      "![Spam filter](images/01_spam_filter.png)"
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      "**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\""
     ]
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     "source": [
      "![Clustering](images/01_clustering.png)"
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      "## 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"
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     "source": [
      "![Supervised learning](images/01_supervised_learning.png)"
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     "source": [
      "The primary goal of supervised learning is to build a model that \"generalizes\": It accurately predicts the **future** rather than the **past**!"
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     "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?"
     ]
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     "source": [
      "## Resources\n",
      "\n",
      "- Book: [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (section 2.1, 14 pages)\n",
      "- Video: [Learning Paradigms](http://work.caltech.edu/library/014.html) (13 minutes)"
     ]
    },
    {
     "cell_type": "markdown",
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     "source": [
      "## Comments or Questions?\n",
      "\n",
      "- Email: <kevin@dataschool.io>\n",
      "- Website: http://dataschool.io\n",
      "- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
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