Exercise

# TED talk recommender

In this exercise, we will build a recommendation system that suggests TED Talks based on their transcripts. You have been given a `get_recommendations()`

function that takes in the title of a talk, a similarity matrix and an `indices`

series as its arguments, and outputs a list of most similar talks. `indices`

has already been provided to you.

You have also been given a `transcripts`

series that contains the transcripts of around 500 TED talks. Your task is to generate a cosine similarity matrix for the tf-idf vectors of the talk transcripts.

Consequently, we will generate recommendations for a talk titled '5 ways to kill your dreams' by Brazilian entrepreneur Bel Pesce.

Instructions

**100 XP**

- Initialize a
`TfidfVectorizer`

with English stopwords. Name it`tfidf`

. - Construct
`tfidf_matrix`

by fitting and transforming`transcripts`

. - Generate the cosine similarity matrix
`cosine_sim`

using`tfidf_matrix`

. - Use
`get_recommendations()`

to generate recommendations for '5 ways to kill your dreams'.