modifies gramatical errors in learning notebook

Author: SnShine

learning.ipynb

@@ -38,7 +38,7 @@
     "\n",
     "In Supervised Learning the agent observeses some example input-output pairs and learns a function that maps from input to output.\n",
     "\n",
-    "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the aggent. The agent then learns a function that maps from an input image to one of those strings.\n",
+    "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the agent. The agent then learns a function that maps from an input image to one of those strings.\n",
     "\n",
     "* **Unsupervised Learning**:\n",
     "\n",
@@ -48,7 +48,7 @@
     "\n",
     "* **Reinforcement Learning**:\n",
     "\n",
-    "In Reinforcement Learning the agent from a series of reinforcements—rewards or punishments.\n",
+    "In Reinforcement Learning the agent learns from a series of reinforcements—rewards or punishments.\n",
     "\n",
     "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it."
    ]