> Clusters are defined by their distance matrix, e.g. the distances between points. This distance can be measured a few ways. Euclidean clusters are defined by the average of the point values, and contain a 'centroid' or center point. Distances are thus measured by the distance to that centroid. Non-Euclidean distances refer to 'clustroids', the point closest to other points. Clustroids in turn can be defined in various ways.
> Clusters are defined by their distance matrix, e.g. the distances between points. This distance can be measured in a few ways. Euclidean clusters are defined by the average of the point values, and contain a 'centroid' or center point. Distances are thus measured by the distance to that centroid. Non-Euclidean distances refer to 'clustroids', the point closest to other points. Clustroids in turn can be defined in various ways.
@ -321,7 +321,7 @@ In preparation for the next lesson, make a chart about the various clustering al
## Review & Self Study
Before you apply clustering algorithms, as we have learned, it's a good idea to understand the nature of your dataset. Read more onn this topic [here](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)
Before you apply clustering algorithms, as we have learned, it's a good idea to understand the nature of your dataset. Read more on this topic [here](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)
[This helpful article](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/) walks you through the different ways that various clustering algorithms behave, given different data shapes.
It can be argued that TextBlob's translation is far more exact, in fact, than the 1932 French translation of the book by V. Leconte and Ch. Pressoir:
"C'est une vérité universelle qu'un celibataire pourvu d'une belle fortune doit avoir envie de se marier, et, si peu que l'on sache de son sentiment à cet egard, lorsqu'il arrive dans une nouvelle residence, cette idée est si bien fixée dans l'esprit de ses voisins qu'ils le considèrent sur-le-champ comme la propriété légitime de l'une ou l'autre de leurs filles."
"C'est une vérité universelle qu'un célibataire pourvu d'une belle fortune doit avoir envie de se marier, et, si peu que l'on sache de son sentiment à cet egard, lorsqu'il arrive dans une nouvelle résidence, cette idée est si bien fixée dans l'esprit de ses voisins qu'ils le considèrent sur-le-champ comme la propriété légitime de l'une ou l'autre de leurs filles."
In this case, the translation informed by ML does a better job than the human translator who is unnecessarily putting words in the original author's mouth for 'clarity'.
Now that you have a explored the dataset in detail, it's time to filter the columns and then use NLP techniques on the dataset to gain new insights about the hotels.
Now that you have explored the dataset in detail, it's time to filter the columns and then use NLP techniques on the dataset to gain new insights about the hotels.
@ -101,7 +101,7 @@ Clean the data just a bit more. Add columns that will be useful later, change th
### Tag columns
The `Tag` columns is problematic as it is a list (in text form) stored in the column. Unfortunately the order and number of sub sections in this column are not always the same. It's hard for a human to identify the correct phrases to be interested in, because there are 515,000 rows, and 1427 hotels, and each has slightly different options a reviewer could choose. This is where NLP shines. You can scan the text and find the most common phrases, and count them.
The `Tag` column is problematic as it is a list (in text form) stored in the column. Unfortunately the order and number of sub sections in this column are not always the same. It's hard for a human to identify the correct phrases to be interested in, because there are 515,000 rows, and 1427 hotels, and each has slightly different options a reviewer could choose. This is where NLP shines. You can scan the text and find the most common phrases, and count them.
Unfortunately, we are not interested in single words, but multi-word phrases (e.g. *Business trip*). Running a multi-word frequency distribution algorithm on that much data (6762646 words) could take an extraordinary amount of time, but without looking at the data, it would seem that is a necessary expense. This is where exploratory data analysis comes in useful, because you've seen a sample of the tags such as `[' Business trip ', ' Solo traveler ', ' Single Room ', ' Stayed 5 nights ', ' Submitted from a mobile device ']` , you can begin to ask if it's possible to greatly reduce the processing you have to do. Luckily, it is - but first you need to follow a few steps to ascertain the tags of interest.
@ -176,7 +176,7 @@ Removing these tags is step 1, it reduces the total number of tags to be conside
| Stayed 9 nights | 1293 |
| ... | ... |
There are a huge variety of rooms, suites, studios, apartments and so on. They all mean the roughly the same thing and not relevant to you, so remove them from consideration.
There are a huge variety of rooms, suites, studios, apartments and so on. They all mean roughly the same thing and not relevant to you, so remove them from consideration.
> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
Reinforcement learning involves three important concepts: the agent, some states, and a set of actions per state. By executing an action in a specified state, the agent is given a reward. Again imagine the computer game Super Mario. You are Mario, you are in a game level, standing next to a cliff edge. Above you is a coin. You being Mario, in a game level, at a specific position ... that's your state. Moving one step to the right (an action) will take you over the edge, and that would give you a low numerical score. However, pressing the jump button would let score a point and you would stay alive. That's a positive outcome and that should award you a positive numerical score.
Reinforcement learning involves three important concepts: the agent, some states, and a set of actions per state. By executing an action in a specified state, the agent is given a reward. Again imagine the computer game Super Mario. You are Mario, you are in a game level, standing next to a cliff edge. Above you is a coin. You being Mario, in a game level, at a specific position ... that's your state. Moving one step to the right (an action) will take you over the edge, and that would give you a low numerical score. However, pressing the jump button would let you score a point and you would stay alive. That's a positive outcome and that should award you a positive numerical score.
By using reinforcement learning and a simulator (the game), you can learn how to play the game to maximize the reward which is staying alive and scoring as many points as possible.
@ -192,7 +192,7 @@ Here γ is the so-called **discount factor** that determines to which extent you
## Learning Algorithm
Given the equation above, we can now write pseudo-code for our leaning algorithm:
Given the equation above, we can now write pseudo-code for our learning algorithm:
* Initialize Q-Table Q with equal numbers for all states and actions
* Set learning rate α ← 1
@ -280,7 +280,7 @@ walk(m,qpolicy_strict)
> **Task 1:** Modify the `walk` function to limit the maximum length of path by a certain number of steps (say, 100), and watch the code above return this value from time to time.
> **Task 2:** Modify the `walk` function so that it does not go back to the places where is has already been previously. This will prevent `walk` from looping, however, the agent can still end up being "trapped" in a location from which it is unable to escape.
> **Task 2:** Modify the `walk` function so that it does not go back to the places where it has already been previously. This will prevent `walk` from looping, however, the agent can still end up being "trapped" in a location from which it is unable to escape.
Piyush Mohan
3 years ago
committed by
GitHub