Minor copy-edits

Author: jakevdp

notebooks/03.02-Data-Indexing-and-Selection.ipynb

@@ -185,7 +185,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``DataFrame`` objects can even be modified with a dictionary-like syntax.\n",
+    "``Series`` objects can even be modified with a dictionary-like syntax.\n",
     "Just as you can extend a dictionary by assigning to a new key, you can extend a ``Series`` by assigning to a new index value:"
    ]
   },

notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb

@@ -425,7 +425,7 @@
    "metadata": {},
    "source": [
     "To look at this in another light, consider what happens if we use these two models to predict the y-value for some new data.\n",
-    "In the following figures, the red points indicate data that is omitted from the training set:\n",
+    "In the following diagrams, the red/lighter points indicate data that is omitted from the training set:\n",
     "\n",
     "![](figures/05.03-bias-variance-2.png)\n",
     "[figure source in Appendix](06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff-Metrics)\n",

notebooks/05.08-Random-Forests.ipynb

@@ -145,7 +145,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice that after the first split, every point in the upper branch is yellow, so there is no need to further subdivide this branch.\n",
+    "Notice that after the first split, every point in the upper branch remains unchanged, so there is no need to further subdivide this branch.\n",
     "Except for nodes that contain all of one color, at each level *every* region is again split along one of the two features."
    ]
   },
@@ -519,7 +519,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here the true model is shown in the smooth gray curve, while the random forest model is shown in red.\n",
+    "Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve.\n",
     "As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model!"
    ]
   },
@@ -727,6 +727,7 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",