Fix a few typos (#233)

Author: hendrikmakait

01x_lazy.ipynb

@@ -214,7 +214,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Your task is to recreate this graph again using the delayed function on the original Python code. The three functions you want to delay are `pd.read_csv`, `len` and `sum`.. "
+    "Your task is to recreate this graph again using the delayed function on the original Python code. The three functions you want to delay are `pd.read_csv`, `len` and `sum`. "
    ]
   },
   {
@@ -558,7 +558,7 @@
    "source": [
     "Any Dask object, such as `total`, above, has an attribute which describes the calculations necessary to produce that result. Indeed, this is exactly the graph that we have been talking about, which can be visualized. We see that it is a simple dictionary, in which the keys are unique task identifiers, and the values are the functions and inputs for calculation.\n",
     "\n",
-    "`delayed` is a handy mechanism for creating the Dask graph, but the adventurous may wish to play with the full fexibility afforded by building the graph dictionaries directly. Detailed information can be found [here](http://dask.pydata.org/en/latest/graphs.html)."
+    "`delayed` is a handy mechanism for creating the Dask graph, but the adventurous may wish to play with the full flexibility afforded by building the graph dictionaries directly. Detailed information can be found [here](http://dask.pydata.org/en/latest/graphs.html)."
    ]
   },
   {

06_distributed_advanced.ipynb

@@ -630,10 +630,10 @@
    "metadata": {},
    "source": [
     "The trouble with this approach is that Dask is meant for the execution of large datasets/computations - you probably can't simply run the whole thing \n",
-    "in one local thread, else you wouldn't have used Dask in the first place. So the code above should only be used on a small part of the data that also exihibits the error. \n",
+    "in one local thread, else you wouldn't have used Dask in the first place. So the code above should only be used on a small part of the data that also exhibits the error. \n",
     "Furthermore, the method will not work when you are dealing with futures (such as `f`, above, or after persisting) instead of delayed-based computations.\n",
     "\n",
-    "As an alternative, you can ask the scheduler to analyze your calculation and find the specific sub-task responsible for the error, and pull only it and its dependnecies locally for execution."
+    "As an alternative, you can ask the scheduler to analyze your calculation and find the specific sub-task responsible for the error, and pull only it and its dependencies locally for execution."
    ]
   },
   {

08_machine_learning.ipynb

@@ -243,7 +243,7 @@
     "\n",
     "Dask can talk to scikit-learn (via joblib) so that your *cluster* is used to train a model. \n",
     "\n",
-    "If you run this on a laptop, it will take quite some time, but the CPU usage will be satisfyingly near 100% for the duration. To run faster, you would need a disrtibuted cluster. That would mean putting something in the call to `Client` something like\n",
+    "If you run this on a laptop, it will take quite some time, but the CPU usage will be satisfyingly near 100% for the duration. To run faster, you would need a distributed cluster. That would mean putting something in the call to `Client` something like\n",
     "\n",
     "```\n",
     "c = Client('tcp://my.scheduler.address:8786')\n",