Lesson4_AWSML_Modeling.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/noahgift/aws-ml-guide/blob/master/Lesson4_AWSML_Modeling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "iwV-bBLUtTQu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Lesson 4 Machine Learning Modeling on AWS\n",
        "\n",
        "[Watch Lesson 4:  Machine Learning Modeling on AWS Video](https://learning.oreilly.com/videos/aws-certified-machine/9780135556597/9780135556597-ACML_01_04_00)"
      ]
    },
    {
      "metadata": {
        "id": "c_Id55m6Jsbu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Pragmatic AI Labs\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "e5p96AqpSDZa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![alt text](https://paiml.com/images/logo_with_slogan_white_background.png)\n",
        "\n",
        "This notebook was produced by [Pragmatic AI Labs](https://paiml.com/).  You can continue learning about these topics by:\n",
        "\n",
        "*   Buying a copy of [Pragmatic AI: An Introduction to Cloud-Based Machine Learning](http://www.informit.com/store/pragmatic-ai-an-introduction-to-cloud-based-machine-9780134863863) from Informit.\n",
        "*   Buying a copy of  [Pragmatic AI: An Introduction to Cloud-Based Machine Learning](https://www.amazon.com/Pragmatic-AI-Introduction-Cloud-Based-Learning/dp/0134863860) from Amazon\n",
        "*   Reading an online copy of [Pragmatic AI:Pragmatic AI: An Introduction to Cloud-Based Machine Learning](https://www.safaribooksonline.com/library/view/pragmatic-ai-an/9780134863924/)\n",
        "*  Watching video [Essential Machine Learning and AI with Python and Jupyter Notebook-Video-SafariOnline](https://www.safaribooksonline.com/videos/essential-machine-learning/9780135261118) on Safari Books Online.\n",
        "* Watching video [AWS Certified Machine Learning-Speciality](https://learning.oreilly.com/videos/aws-certified-machine/9780135556597)\n",
        "* Purchasing video [Essential Machine Learning and AI with Python and Jupyter Notebook- Purchase Video](http://www.informit.com/store/essential-machine-learning-and-ai-with-python-and-jupyter-9780135261095)\n",
        "*   Viewing more content at [noahgift.com](https://noahgift.com/)\n"
      ]
    },
    {
      "metadata": {
        "id": "bq4VmHjPpMOR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Load AWS API Keys"
      ]
    },
    {
      "metadata": {
        "id": "aWrzIk7WpRoh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Put keys in local or remote GDrive:  \n",
        "\n",
        "`cp ~/.aws/credentials /Users/myname/Google\\ Drive/awsml/`"
      ]
    },
    {
      "metadata": {
        "id": "hPWO_zyRopXN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Mount GDrive\n"
      ]
    },
    {
      "metadata": {
        "id": "XI73HZNLobp4",
        "colab_type": "code",
        "outputId": "66f597a9-6c94-4ffd-b323-21869a4eb988",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UNyzZwgmoxwm",
        "colab_type": "code",
        "outputId": "80a98691-3926-4666-82c7-f27f606fbfaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import os;os.listdir(\"/content/gdrive/My Drive/awsml\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['kaggle.json', 'credentials', 'config']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "fYu0ekUlqPk6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Install Boto"
      ]
    },
    {
      "metadata": {
        "id": "dJDDrUkWrYRY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip -q install boto3\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FpJhrpSQsK5E",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Create API Config"
      ]
    },
    {
      "metadata": {
        "id": "QxRwGOZtsN0-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.aws &&\\\n",
        "  cp /content/gdrive/My\\ Drive/awsml/credentials ~/.aws/credentials "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Kj977UW3rph_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Test Comprehend API Call"
      ]
    },
    {
      "metadata": {
        "id": "P-A8Cia-raT0",
        "colab_type": "code",
        "outputId": "4290a9a8-667f-4b06-edf3-aa4c2ae121f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "cell_type": "code",
      "source": [
        "import boto3\n",
        "comprehend = boto3.client(service_name='comprehend', region_name=\"us-east-1\")\n",
        "text = \"There is smoke in San Francisco\"\n",
        "comprehend.detect_sentiment(Text=text, LanguageCode='en')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ResponseMetadata': {'HTTPHeaders': {'connection': 'keep-alive',\n",
              "   'content-length': '160',\n",
              "   'content-type': 'application/x-amz-json-1.1',\n",
              "   'date': 'Thu, 22 Nov 2018 00:21:54 GMT',\n",
              "   'x-amzn-requestid': '9d69a0a9-edec-11e8-8560-532dc7aa62ea'},\n",
              "  'HTTPStatusCode': 200,\n",
              "  'RequestId': '9d69a0a9-edec-11e8-8560-532dc7aa62ea',\n",
              "  'RetryAttempts': 0},\n",
              " 'Sentiment': 'NEUTRAL',\n",
              " 'SentimentScore': {'Mixed': 0.008628507144749165,\n",
              "  'Negative': 0.1037612184882164,\n",
              "  'Neutral': 0.8582549691200256,\n",
              "  'Positive': 0.0293553676456213}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "metadata": {
        "id": "EBv9LzI9xnrF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 4.1 AWS ML Systems Overview"
      ]
    },
    {
      "metadata": {
        "id": "Qu_UeD92AEPB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Machine Learning at AWS"
      ]
    },
    {
      "metadata": {
        "id": "xCsczQCqAJBR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "* [AWS ML Services Video](https://www.aws.training/learningobject/video?id=16207&trk=gs_card)\n",
        "* [AWS Machine Learning Overview Video](https://www.aws.training/learningobject/video?id=16363&trk=gs_card)\n",
        "\n",
        "* Thousands of Engineers working on Machine Learning at AWS\n"
      ]
    },
    {
      "metadata": {
        "id": "SDAq-AnzLNHW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### What is Machine Learning?"
      ]
    },
    {
      "metadata": {
        "id": "6t1mMbl8LQsz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![aiml](https://user-images.githubusercontent.com/58792/48925854-4de55d00-ee7d-11e8-8656-f92b2a2d3aed.png)\n",
        "\n",
        "*   Subset of AI\n",
        "*   Deep Learning is subset of Machine Learning\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "seXc9BlVLZpz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### What can Machine Learning Do?\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "iVsOZlTDOeAM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Make predictions\n",
        "* Optimize utility functions\n",
        "* Extract hidden data structures\n",
        "* Classify data"
      ]
    },
    {
      "metadata": {
        "id": "mXxBpaedOfvv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Machine Learning Use Cases"
      ]
    },
    {
      "metadata": {
        "id": "HWCc2hTbOf2p",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Fraud Detection\n",
        "\n",
        "  "
      ]
    },
    {
      "metadata": {
        "id": "yI4kPKU1PpkG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "  - Mine data\n",
        "  - Identify hidden patterns and create labels (unsupervised learning)\n",
        "  - Train model\n",
        "  - Flag transaction as fraudulent\n",
        "  "
      ]
    },
    {
      "metadata": {
        "id": "WMmgxYymPCP3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Content Personalization\n"
      ]
    },
    {
      "metadata": {
        "id": "1QQntewZPoIT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "  - Use predictive analytics to recommend items (recommendation systems)"
      ]
    },
    {
      "metadata": {
        "id": "L27fzNNqPU78",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Target Marketing"
      ]
    },
    {
      "metadata": {
        "id": "nR0j74UtPYcR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Use custom activity to choose revelant email campaigns\n",
        "* cross-selling\n",
        "* upselling"
      ]
    },
    {
      "metadata": {
        "id": "cmOGo30EPzyI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Categorization"
      ]
    },
    {
      "metadata": {
        "id": "1tc01wYZP10m",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Matching hiring managers and resumes\n",
        "* Unstructured content --> Machine Learning Model --> Categorized documents"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "ZcEl2FAfQMUv"
      },
      "cell_type": "markdown",
      "source": [
        "##### Customer Service"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "esZyYtFCQMUx"
      },
      "cell_type": "markdown",
      "source": [
        "* Analyze social media traffic to route customers to customer support\n",
        "* Predictive routing of customer emails"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "L81LNGGpQth4"
      },
      "cell_type": "markdown",
      "source": [
        "#### Machine Learning Concepts"
      ]
    },
    {
      "metadata": {
        "id": "Cl6ejyv-lA-Z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![Ml Modeling](https://user-images.githubusercontent.com/58792/49088318-dc573700-f20d-11e8-825c-d034f060b395.png)"
      ]
    },
    {
      "metadata": {
        "id": "UDx9LcAERBGV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Combination of Methods and Systems**"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "cRWNOJ_JQth6"
      },
      "cell_type": "markdown",
      "source": [
        "* **Predict**\n",
        "  - Predict new data based on observed data\n",
        "* **Extract**\n",
        "  - Extract hidden structure from data\n",
        "* **Summarize**\n",
        "  - Summarize data into executive brief\n",
        "* **Optimize**\n",
        "  - Optimize an action, given a cost function and observed data\n",
        "* **Adapt**\n",
        "  - Adapt based on observed data"
      ]
    },
    {
      "metadata": {
        "id": "UzOX1NXvVajB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Machine Learning Types"
      ]
    },
    {
      "metadata": {
        "id": "egpHjCJbVa4f",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![Machine Learning Types](https://user-images.githubusercontent.com/58792/48926326-c77f4a00-ee81-11e8-8066-84c922df39d2.png)"
      ]
    },
    {
      "metadata": {
        "id": "kEvX7IQEVkX_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Supervised Learning\n",
        "  - Labels are known for inputs and outputs\n",
        "  - Model \"learns\" to generalize from data (records)\n",
        "  - Human needs to tell model which outputs are correct (to train model)\n",
        "  - Input --> Model --> Output/Prediction\n",
        "  - Classification (Category like \"red\", \"blue\") and Regression (Numerical i.e. Housing price)\n",
        " \n",
        "* Unsupervised Learning\n",
        "  - Labels are not known\n",
        "  - Lables \"hidden structures\" are discovered\n",
        "  - Self-organization\n",
        "  - Clustering\n",
        " \n",
        "* Reinforcement Learning\n",
        "  - Model learns by interacting with environment\n",
        "  - Learns to take action to maximize total reward\n",
        "  - Inspired by behavioral biology\n",
        " \n",
        "  \n",
        "  "
      ]
    },
    {
      "metadata": {
        "id": "j_gRca1vByuL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Frameworks & Infrastructure"
      ]
    },
    {
      "metadata": {
        "id": "WiYj4WIOD0dr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* All Major frameworks\n",
        "  - Apache MXNet\n",
        "  - Caffe and Caffe 2\n",
        "  - Tensorflow"
      ]
    },
    {
      "metadata": {
        "id": "jhSFhnKtDawl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Machine learning platforms"
      ]
    },
    {
      "metadata": {
        "id": "TMgKoKi7o32f",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Fully managed platform for building models with own data\n",
        "  - Apache Spark on EMR\n",
        "  - SparkML (25 PetaByte models, Zillow and Netflix) \n",
        "  - Sagemaker\n",
        "  - Amazon Machine Learning"
      ]
    },
    {
      "metadata": {
        "id": "g2DKkOCHDaz4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### API-driven services"
      ]
    },
    {
      "metadata": {
        "id": "1IPzubP6o4hQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Examples include:\n",
        "\n",
        "\n",
        "\n",
        "*   Comprehend (NLP)\n",
        "*   Rekognition (Computer Vision)\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "1hrzi0nhDa3V",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Models"
      ]
    },
    {
      "metadata": {
        "id": "p3QgVrxPo5Hl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Can be stored in S3\n",
        "* Can be A/B tested"
      ]
    },
    {
      "metadata": {
        "id": "fCPtd7bADa6X",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Optimized Instances & Machine Images"
      ]
    },
    {
      "metadata": {
        "id": "mS5BmQ0JDa9-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Deep Learning AMIs\n",
        "* Containers"
      ]
    },
    {
      "metadata": {
        "id": "7kXTB2KgyuVD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 4.2 Feature Engineering\n"
      ]
    },
    {
      "metadata": {
        "id": "itASk_VSuAaC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Feature Engineering Overview"
      ]
    },
    {
      "metadata": {
        "id": "ZkLYBaBUuFVY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   Feature is an attribute used in a prediction model\n",
        "*   Feature engineering is the creation and curation of these attributes\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "0gpFkMsDo-nC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Explore Features"
      ]
    },
    {
      "metadata": {
        "id": "5zSCcP27pGtm",
        "colab_type": "code",
        "outputId": "e2cb8cd1-ba21-43ca-b78e-4898d2ed91c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/noahgift/aws-ml-guide/master/data/banking.csv\")\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>month</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>...</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>emp_var_rate</th>\n",
              "      <th>cons_price_idx</th>\n",
              "      <th>cons_conf_idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr_employed</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>44</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.4y</td>\n",
              "      <td>unknown</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>aug</td>\n",
              "      <td>thu</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.4</td>\n",
              "      <td>93.444</td>\n",
              "      <td>-36.1</td>\n",
              "      <td>4.963</td>\n",
              "      <td>5228.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>53</td>\n",
              "      <td>technician</td>\n",
              "      <td>married</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>nov</td>\n",
              "      <td>fri</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>93.200</td>\n",
              "      <td>-42.0</td>\n",
              "      <td>4.021</td>\n",
              "      <td>5195.8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>management</td>\n",
              "      <td>single</td>\n",
              "      <td>university.degree</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>jun</td>\n",
              "      <td>thu</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>success</td>\n",
              "      <td>-1.7</td>\n",
              "      <td>94.055</td>\n",
              "      <td>-39.8</td>\n",
              "      <td>0.729</td>\n",
              "      <td>4991.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>39</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>apr</td>\n",
              "      <td>fri</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>93.075</td>\n",
              "      <td>-47.1</td>\n",
              "      <td>1.405</td>\n",
              "      <td>5099.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55</td>\n",
              "      <td>retired</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.4y</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>cellular</td>\n",
              "      <td>aug</td>\n",
              "      <td>fri</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>success</td>\n",
              "      <td>-2.9</td>\n",
              "      <td>92.201</td>\n",
              "      <td>-31.4</td>\n",
              "      <td>0.869</td>\n",
              "      <td>5076.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   age          job  marital          education  default housing loan  \\\n",
              "0   44  blue-collar  married           basic.4y  unknown     yes   no   \n",
              "1   53   technician  married            unknown       no      no   no   \n",
              "2   28   management   single  university.degree       no     yes   no   \n",
              "3   39     services  married        high.school       no      no   no   \n",
              "4   55      retired  married           basic.4y       no     yes   no   \n",
              "\n",
              "    contact month day_of_week ...  campaign  pdays  previous     poutcome  \\\n",
              "0  cellular   aug         thu ...         1    999         0  nonexistent   \n",
              "1  cellular   nov         fri ...         1    999         0  nonexistent   \n",
              "2  cellular   jun         thu ...         3      6         2      success   \n",
              "3  cellular   apr         fri ...         2    999         0  nonexistent   \n",
              "4  cellular   aug         fri ...         1      3         1      success   \n",
              "\n",
              "  emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  nr_employed  y  \n",
              "0          1.4          93.444          -36.1      4.963       5228.1  0  \n",
              "1         -0.1          93.200          -42.0      4.021       5195.8  0  \n",
              "2         -1.7          94.055          -39.8      0.729       4991.6  1  \n",
              "3         -1.8          93.075          -47.1      1.405       5099.1  0  \n",
              "4         -2.9          92.201          -31.4      0.869       5076.2  1  \n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "id": "2DDt9lD1tj0V",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Correlation Heatmap"
      ]
    },
    {
      "metadata": {
        "id": "sUrn45ibtLQB",
        "colab_type": "code",
        "outputId": "8e9bf443-b991-496e-a7f3-44ea3b490aae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3ae31b4a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HLX0REREXeLKlv2nTJmJiYgCIiIjgzJkz5OTk5Dtm/PjxPP/883/pGpT0RUREXODJpH/i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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd3ae30a4a8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "BVhRu73hoXzN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Yellowbrick Road Feature Rank"
      ]
    },
    {
      "metadata": {
        "id": "ivyYbUShqcNa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Can use [yellowbrick road](http://www.scikit-yb.org/en/latest/index.html) to visualize feature importance"
      ]
    },
    {
      "metadata": {
        "id": "E9-SpimcocjY",
        "colab_type": "code",
        "outputId": "a902e72b-9dad-4df5-e059-54b80655aef7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 920
        }
      },
      "cell_type": "code",
      "source": [
        "from yellowbrick.features import Rank2D\n",
        "visualizer = Rank2D(algorithm=\"pearson\", size=(1080, 1080))\n",
        "visualizer.fit_transform(pd.get_dummies(df))\n",
        "visualizer.poof()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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t2jUyMjL44osv8pxKfFOxYsVo1qwZCxcuBODAgQOcPn2aOnXqsG7dOrZv3862bdus04y3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g+NtlOZ2uKnFOl/MWUVy9iWXptMna6ZCFAeBDFmzzydZNAK/VIYqzIpt3i0++XVrcdcK4H8Q5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F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\nxxJtJ/DZtAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd3ad024f28>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "1CrVJeZSyyEs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Demo AWS Machine Learning Service"
      ]
    },
    {
      "metadata": {
        "id": "0cS8_6ukOX6Q",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "*  [Tutorial:  Amazon ML to Predict Reponses to a ML Offer](https://docs.aws.amazon.com/machine-learning/latest/dg/tutorial.html)\n",
        "\n",
        "*   Create Training Datasource (Steps Below)\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "xGCPq-utzDcV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "*   Prepare Data \n",
        " ** - Create S3 Bucket & Create folder**\n",
        "  ![s3bucket](https://user-images.githubusercontent.com/58792/49109353-548a2080-f23f-11e8-8700-5fb512005655.png)\n",
        "  \n",
        "**  - Upload CSV files**\n",
        "![csv_file](https://user-images.githubusercontent.com/58792/49109351-548a2080-f23f-11e8-82fa-ade76fe73bdc.png)\n",
        "\n",
        "  \n",
        " \n",
        "**  - Create Data Source **\n",
        "\n",
        "![create data source](https://user-images.githubusercontent.com/58792/49109714-45f03900-f240-11e8-8272-f65d4eafe2ca.png)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "QQbOCseJ32jF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Infer the Schema**\n",
        "\n",
        "Amazon Machine Learning Service infers the proper schema\n",
        "\n",
        "\n",
        "![Infer the Schema](https://user-images.githubusercontent.com/58792/49111125-15120300-f244-11e8-9116-abeb5288fa3d.png)\n"
      ]
    },
    {
      "metadata": {
        "id": "X29iBfUR5ah0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Select the Target**\n",
        "\n",
        "\n",
        "![target](https://user-images.githubusercontent.com/58792/49111259-74701300-f244-11e8-94aa-124f9fb640c1.png)"
      ]
    },
    {
      "metadata": {
        "id": "18M9wnli-uul",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "lgXraZ2kyyJ7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 4.3 Train a Model"
      ]
    },
    {
      "metadata": {
        "id": "eoVN1pgkVCC5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Create 70/30 Split and Train Model in sklearn"
      ]
    },
    {
      "metadata": {
        "id": "YbPYgGivg4Hf",
        "colab_type": "code",
        "outputId": "990cf354-7f76-41db-ca20-9b5639612f8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "banking_df = pd.read_csv(\"https://raw.githubusercontent.com/noahgift/aws-ml-guide/master/data/banking.csv\")\n",
        "banking_df.columns #y column will be predicted"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
              "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
              "       'previous', 'poutcome', 'emp_var_rate', 'cons_price_idx',\n",
              "       'cons_conf_idx', 'euribor3m', 'nr_employed', 'y'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "nXc6SdlBhtTW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### One Hot Encode with sklearn\n",
        "\n",
        "[One Hot Encode Categories](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html#sklearn.feature_extraction.DictVectorizer)"
      ]
    },
    {
      "metadata": {
        "id": "y4EUINbITk_W",
        "colab_type": "code",
        "outputId": "382836d7-57f7-4fec-9f24-454fd635d978",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.feature_extraction import DictVectorizer\n",
        "\n",
        "dvec = DictVectorizer(sparse=False)\n",
        "vectorized_banking_df = dvec.fit_transform(df.transpose().to_dict().values())\n",
        "train, test = train_test_split(vectorized_banking_df, test_size=0.3)\n",
        "\n",
        "print(f\" 70% Split data structure: {type(train)} and shape: {train.shape}\")\n",
        "print(f\" 30% Split data structure: {type(test)} and shape: {test.shape}\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 70% Split data structure: <class 'numpy.ndarray'> and shape: (28831, 64)\n",
            " 30% Split data structure: <class 'numpy.ndarray'> and shape: (12357, 64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Lb4LqxBHahIQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Use Logistic Regression to train model on 70% train data"
      ]
    },
    {
      "metadata": {
        "id": "MIxWTdPzZhUa",
        "colab_type": "code",
        "outputId": "3080b99b-6f2c-4751-a7e7-a6d4e3b59740",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "X = train[:,:-1]  #drop the predict 'y'\n",
        "print(f\" Drop last column in X {X.shape}\")\n",
        "Y = train[:, -1] #select only predict variable 'y'\n",
        "print(f\" Select last column in Y {Y.shape}\")\n",
        "model = LogisticRegression()\n",
        "model.fit(X, Y)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Drop last column in X (28831, 63)\n",
            " Select last column in Y (28831,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
              "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
              "          tol=0.0001, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "OMNcsGAwyyM7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Demo AWS Machine Learning Service"
      ]
    },
    {
      "metadata": {
        "id": "uZKB2jDWOY44",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   [Tutorial (Continued): Amazon ML to Predict Reponses to a ML Offer](https://docs.aws.amazon.com/machine-learning/latest/dg/tutorial.html)\n",
        "\n",
        "*   **Create ML Model**\n",
        "\n",
        "![ML model settings](https://user-images.githubusercontent.com/58792/49115892-ddf61e80-f250-11e8-9ec4-d0576e7c1e87.png)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "R0to0Hr3yyQE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 4.4 Evaluate a Model"
      ]
    },
    {
      "metadata": {
        "id": "XBW7qnx-qo-v",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Classification Model Evaluation Metrics"
      ]
    },
    {
      "metadata": {
        "id": "2PBRDe14q8nk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "* **Precision**\n",
        "\n",
        "* **Recall**\n",
        "\n",
        "* **F1**:  harmonic mean of precision and recall\n",
        "\n",
        "* **AUC ([Area Under the Curve](https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html#measuring-ml-model-accuracy))**\n",
        "\n",
        "Measures the ability of a model to predict higher scores for positive observations vs negative observations.\n",
        "\n",
        "![AUC Example from AWS](https://user-images.githubusercontent.com/58792/49120374-274e6a00-f261-11e8-80da-7128e9feaeb6.png)\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "-ycT0EMXoBd-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Using Yellowbrick road to evaluate model"
      ]
    },
    {
      "metadata": {
        "id": "GLlskrFWoFlQ",
        "colab_type": "code",
        "outputId": "b41a1046-7a5c-4243-a744-dd5cfc987e6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "cell_type": "code",
      "source": [
        "from yellowbrick.classifier import ClassificationReport\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "visualizer = ClassificationReport(model)\n",
        "visualizer.fit(X, Y)\n",
        "visualizer.score(X, Y)\n",
        "visualizer.poof()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WrKkxY8bI3d1d33zzjUJDQ1W5cmUNHz5c27Zt00svvaR3331XXl5eat26tT0QSlKjRo3U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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f206a181e80>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "BMA3ujntuidt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Generate Cross-Validated Precision and Recall"
      ]
    },
    {
      "metadata": {
        "id": "x5bguV5runFZ",
        "colab_type": "code",
        "outputId": "519e38a5-d913-45fd-a2e9-9e4a59491b07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.metrics import precision_score, recall_score\n",
        "\n",
        "y_train_pred = cross_val_predict(model, X, Y, cv=3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "htSYYj7yvRVZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Precision**"
      ]
    },
    {
      "metadata": {
        "id": "TG-CeAKzvUim",
        "colab_type": "code",
        "outputId": "1033dd39-4d43-4de8-b2c9-735bc49571ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "precision_score(Y, y_train_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6702619414483821"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "dbqG5KK_vfxO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Recall**"
      ]
    },
    {
      "metadata": {
        "id": "4UdexHgivf7D",
        "colab_type": "code",
        "outputId": "124064ae-9fbd-479a-a551-da92131b0901",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "recall_score(Y, y_train_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3987167736021998"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "BSP4ZTpjvzHZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**F1 Score**"
      ]
    },
    {
      "metadata": {
        "id": "pp9H_By-v175",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AOp4jY56v4Zi",
        "colab_type": "code",
        "outputId": "53e23e90-f03a-4331-af41-75d842e4e4da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "f1_score(Y, y_train_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5000000000000001"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "bK0RxC6lxIaG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**AUC**"
      ]
    },
    {
      "metadata": {
        "id": "skjj9SpLxKPz",
        "colab_type": "code",
        "outputId": "92136158-3c74-4c08-9842-6c1ff826ae08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "roc_auc_score(Y, y_train_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.68679871859545"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "3XzlNBqRyyWj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Demo AWS Machine Learning Service"
      ]
    },
    {
      "metadata": {
        "id": "4-EBK2hrOZ_L",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "**Evaluatate Model**\n",
        "\n",
        "\n",
        "![Evaluating Model](https://user-images.githubusercontent.com/58792/49120960-a80e6580-f263-11e8-9413-98b52b811476.png)\n",
        "\n",
        "**Explore Performance**\n",
        "\n",
        "\n",
        "![Explore Performance](https://user-images.githubusercontent.com/58792/49121094-13583780-f264-11e8-9e17-5e0c20e23841.png)"
      ]
    },
    {
      "metadata": {
        "id": "x0BjMM43xuub",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Evaluating Model\n",
        "* Explore Performance"
      ]
    },
    {
      "metadata": {
        "id": "kD9MppHKy5Ma",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 4.5 Tune a Model"
      ]
    },
    {
      "metadata": {
        "id": "s-RxvF9Ny9Vy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### [Demo] AWS Machine Learning Service"
      ]
    },
    {
      "metadata": {
        "id": "-6UtY2FmZOhf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* show tradeoffs"
      ]
    },
    {
      "metadata": {
        "id": "JFK7Q6MBy9Yj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 4.6 Understand ML Inference"
      ]
    },
    {
      "metadata": {
        "id": "ToTGRXbczAUO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### [Demo] AWS Machine Learning Service"
      ]
    },
    {
      "metadata": {
        "id": "Bl2Lc0t_Zft1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Refers to prediction\n",
        "* Sagemaker can auto-scale inference endpoints\n",
        "* Also Amazon Elastic Inference service:\n",
        "  - Can save up to 75% of cost of inference (predictions)\n",
        "  - Attaches to Sagemaker and EC2"
      ]
    },
    {
      "metadata": {
        "id": "s2HmQ3Q0zAW4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 4.7 Understand Deep Learning"
      ]
    },
    {
      "metadata": {
        "id": "VdKJTV38igkg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Intro to Deep Learning"
      ]
    },
    {
      "metadata": {
        "id": "dfMY_tASimdr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "[Intro to Deep Learning on AWS Video](https://www.aws.training/learningobject/video?id=16439&trk=gs_card)"
      ]
    },
    {
      "metadata": {
        "id": "jsvkrxtAjdoT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Brief History of Neural Networks\n",
        "\n",
        "* 1950-1998:  Academic Activity\n",
        "* 1998-2007:  AI Winter\n",
        "* 2007-Today:  \"The GPU Era\" (Moore's law over 2005)\n",
        "* Deep Learning is a \"multi-layered\" feed-forward neural network\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "HB0gINZRkPTP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Perform Storm for Deep Learning\n",
        "\n",
        "* Cloud Computing\n",
        "* End of Moore's Law (forces more GPU adoption)\n",
        "* Elastic Storage and Compute\n",
        "* Proliferation of data:  social networks, etc\n"
      ]
    },
    {
      "metadata": {
        "id": "pxmnqaDlz5O6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### What is a Neural Network?\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "yoArLRhY0dgO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![DNN](https://user-images.githubusercontent.com/58792/48964488-45645380-ef5d-11e8-8c84-f3ab8b238689.png)\n",
        "\n",
        "* Can be \"Deep\" Neural Network (multi-layered), but optional\n",
        "* Simple functions than be collectively trained (in layers) to learn complex functions\n",
        "* Input Layer --> Hidden Layer(s) ---> Output Layer\n",
        "\n",
        "![RNN](https://user-images.githubusercontent.com/58792/48964874-08e92580-ef66-11e8-9c91-4e16ef98ed1a.png)\n",
        "\n",
        "* RNN is used sequences like:  \n",
        "  - text\n",
        "  - speech\n",
        "  - translation "
      ]
    },
    {
      "metadata": {
        "id": "4LyfP2iVMSHz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Deep Learning Use Cases"
      ]
    },
    {
      "metadata": {
        "id": "IXi0LUxSMVBz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "* Text analysis (NLP)\n",
        "  - insider trading\n",
        "  - sentiment analysis\n",
        "  - regulatory compliance\n",
        "  - brand affinity\n",
        "  - intent analysis\n",
        "  \n",
        "* Time Series\n",
        "  - financial\n",
        "  - log analysis\n",
        "  - supply chain\n",
        "  - IoT\n",
        " \n",
        "* Recommendation Engines \n",
        "  - Social Meida"
      ]
    },
    {
      "metadata": {
        "id": "STZg1LvYzIy-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Deep Learning AMIs"
      ]
    },
    {
      "metadata": {
        "id": "BgtE7QetN__U",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "* Three Styles of Deep Learning AMIs\n",
        "  - Conda-based AMI\n",
        "  - Base AMI\n",
        "  - AMIs with source code\n",
        "* Available \"Pay as you Go\" (On Demand)\n",
        "* Available \"Spot Instance\"\n",
        "* Can perform [\"Multi-GPU training\"](https://docs.aws.amazon.com/dlami/latest/devguide/keras-mxnet.html)\n",
        "* Preloaded with Frameworks\n",
        "![DL Frameworks](https://user-images.githubusercontent.com/58792/48965039-24562f80-ef6a-11e8-9dc2-1b384bbfc610.png)\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "vv5c-aLoPwSS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}