Types of Machine Learning: Recommender Systems

Recommender systems are a category of Machine Learning algorithms that predicts the rating or preferences of a user over a collection of items.
Recommender systems algorithms include:
- Collaborative filtering
- Content-based filtering
- Session-based
- Session-based recommender systems
- Reinforcement learning for recommender systems
- Multi-criteria recommender systems
- Risk-aware recommender systems
- Mobile recommender systems

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Rodolfo Mendes 🇧🇷

Rodolfo Mendes 🇧🇷 Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @rodolfo_mmendes

13 Jun
Types of Machine Learning: Deep Learning

Deep Learning is a branch of Machine Learning specialized in Artificial Neural Networks with multiple intermediary layers.
Neural Networks with multiple layers can approximate very complex non-linear functions. That's why Deep Learning has been very successful in areas like CV (Computer Visions), NLP (Natural Language Processing), and Reinforcement Learning.
Deep Learning architectures include:
- Convolutional Neural Networks
- Recurrent Neural Networks
Read 7 tweets
12 Jun
Types of Machine Learning: Time Series Analysis

The typical regression task predicts the value of a target variable based on the values of one or more feature variables. For example, predicting the price of a house based on its characteristics like size, number of rooms, etc.
But in some cases, we want to predict the value of a variable based on its past values. In our example, we would predict the price of a house based on its previous prices instead of its characteristics.
We call this type of modeling Time Series Analysis. A Time Series is a collection of observations - values of a variable - ordered along the time. The time elapsed between observations varies years, months, days, seconds, or even milliseconds.
Read 7 tweets
9 Jun
𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴

In the reinforcement learning paradigm, the learning process is a loop in which the agent reads the state of the environment and then executes an action.
Then the environment returns its new state and a reward signal, indicating if the action was correct or not. The process continues until the environment reaches a terminal state or if a maximum number of iterations is executed.
These are some of the main concepts in Reinforcement Learning:
Read 10 tweets
8 Jun
Machine Learning Applications: Topic Discovery with Clustering

Given a set of documents, a common task is to group them accordingly to topics or common subjects. A human agent can create a hierarchy of subjects and assign each document to its corresponding topic.
However, a clustering algorithm can create this structure automatically and more precisely. We can apply hierarchical clustering algorithms to group similar documents as we build the hierarchy between them.
We don't need to set the subjects nor the topic hierarchy previously. Instead, they are discovered as we perform the clustering.
Read 5 tweets
26 May
Machine Learning Thread: Exploratory Data Analysis

For me, exploratory data analysis is the most challenging task when building a machine learning model, especially for beginners.

(1/n)
A result of the No-Free-Lunch-Theorem is that there's no single model that will perform well for every dataset. In other words, there's no silver bullet Machine Learning Algorithm.

(2/n)
The practical consequence is that we need to make a LOT of human decisions when building our model: which algorithm to use, which features to use, which features to discard, apply normalization, regularization, hyperparameters to tune.

(3/n)
Read 6 tweets
26 May
Machine Learning Applications: Stroke Prediction

The Stroke Prediction Dataset @kaggle is an example of how Machine Learning can be used for disease prediction.

kaggle.com/fedesoriano/st…
The dataset comprises more than 5,000 observations of 12 attributes representing clinical conditions of patients like heart disease, hypertension, glucose, smoking, and others. For each observation, there's also a binary target variable indicating if a patient had a stroke.
By training typical classification algorithms like logistic regression, KNN, SVM classifiers, decision trees, or others, we can build a model to predict the stroke.
Read 5 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!

:(