08-conclusion.ipynb

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    "## Outlook\n",
    "### Approaching a machine learning problem\n",
    "### Humans in the loop"
   ]
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    "### From prototype to production"
   ]
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    "### Testing production systems"
   ]
  },
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    "### Building your own estimator"
   ]
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    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class MyTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, first_paramter=1, second_parameter=2):\n",
    "        # all parameters must be specified in the __init__ function\n",
    "        self.first_paramter = 1\n",
    "        self.second_parameter = 2\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        # fit should only take X and y as parameters\n",
    "        # even if your model is unsupervised, you need to accept a y argument!\n",
    "        \n",
    "        # Model fitting code goes here\n",
    "        print(\"fitting the model right here\")\n",
    "        # fit returns self\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        # transform takes as parameter only X\n",
    "        \n",
    "        # apply some transformation to X:\n",
    "        X_transformed = X + 1\n",
    "        return X_transformed"
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   "source": [
    "### Where to go from here\n",
    "#### Theory\n",
    "#### Other machine learning frameworks and packages\n",
    "#### Ranking, recommender systems, time series, and other kinds of learning\n",
    "#### Probabilistic modeling, inference and probabilistic programming"
   ]
  },
  {
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   "source": [
    "#### Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling to larger datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Honing your skills"
   ]
  },
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   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  }
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