Fix typo

introduction_to_applying_machine_learning/linear_time_series_forecast/linear_time_series_forecast.ipynb

@@ -31,7 +31,7 @@
     "\n",
     "Forecasting is potentially the most broadly relevant machine learning topic there is.  Whether predicting future sales in retail, housing prices in real estate, traffic in cities, or patient visits in healthcare, almost every industry could benefit from improvements in their forecasts.  There are numerous statistical methodologies that have been developed to forecast time-series data, but still, the process for developing forecasts tends to be a mix of objective statistics and subjective interpretations.\n",
     "\n",
-    "Properly modeling time-series data takes a great deal of care.  What's the right level of aggregation to model at?  Too granular and the signal gets lost in the noise, too aggregate and importent variation is missed.  Also, what is the right cyclicality?  Daily, weekly, monthly?  Are there holiday peaks?  How should we weight recent versus overall trends?\n",
+    "Properly modeling time-series data takes a great deal of care.  What's the right level of aggregation to model at?  Too granular and the signal gets lost in the noise, too aggregate and important variation is missed.  Also, what is the right cyclicality?  Daily, weekly, monthly?  Are there holiday peaks?  How should we weight recent versus overall trends?\n",
     "\n",
     "Linear regression with appropriate controls for trend, seasonality, and recent behavior, remains a common method for forecasting stable time-series with reasonable volatility.  This notebook will build a linear model to forecast weekly output for US gasoline products starting in 1991 to 2005.  It will focus almost exclusively on the application.  For a more in-depth treatment on forecasting in general, see [Forecasting: Principles & Practice](https://robjhyndman.com/uwafiles/fpp-notes.pdf).\n",
     "\n",