Building recommender systems today requires specialized expertise in analytics, machine learning and software engineering, and learning new skills and tools is difficult and time-consuming. In this notebook, we will start from scratch, covering some basic fundamental techniques and implementations in Python. I build the recommendation system using the collaborative filtering technique. This would help the user to identify the content they like.
Before we get started building the recommendation system, we need to understand the following concepts which we would be using while building the recommendation system -
A recommender system, or a recommendation system (sometimes replacing ‘system’ with a synonym such as platform or engine), is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item
Recommender systems are used in a variety of areas, with commonly recognized examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders. …
The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality.
The following program help in identifying such news articles programmatically if a news article is Fake or Not. Let us first understand the two feature extraction technique I have used to build the model—
Twitter has become an important communication channel in times of emergency. The iniquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Because of this, more agencies are interested in programmatically monitoring Twitter (i.e. disaster relief organizations and news agencies).
But, it’s not always clear whether a person’s words are actually announcing a disaster. The following program helps in identifying a tweet programmatically if a tweet conveys disaster info or not.
Before we build the model it is important for us to understand few concepts in NLP (Natural Language Processing)
We often face a situation while trying to improve the accuracy of the neural network we end up overfitting the model on the training data. This leads to a poor prediction when we run the model of the test data. Hence I take a dataset and apply these techniques that not only improve the accuracy but also handles the overfitting issues.
In this article, we’ll use the following techniques to train a state-of-the-art model in less than 5 minutes to achieve over 95% accuracy in classifying images from the Fruit 360 dataset :
While we develop the Convolutional Neural Networks (CNN) to classify the images, It is often observed the model starts overfitting when we try to improve the accuracy. Very frustrating, Hence I list down the following techniques which would improve the model performance without overfitting the model on the training data.
In my previous blog, I developed a feed-forward neural network to train on CIFAR 10 dataset. As a feed-forward neural network not being powerful on image dataset. We achieved an accuracy of 50%. I will build a CNN model from scratch and validate its performance on CIFAR 10 dataset. But before we get started I will try answering few fundamental questions.
If you are someone who wanted to get started with FFNN (feed forward neural networks)but not quite sure which dataset to pick to begin with, then you are at the right place. We see Neural network implementations in classical machine learning to deep neural networks. Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management, Let us start by asking couple of fundamental questions —
If you are someone who wanted to get started with PyTorch but not quite sure which dataset to pick to begin with, then you are at the right place. We see PyTorch implementations in classical machine learning to deep neural networks. I can’t wait to get started but before we get started, let us start by answering a couple of fundamental questions—
If you are getting started with machine learning and looking for dataset to work with to test you skills and understanding then you are at right place. The Swedish auto insurance dataset is ideal for beginners as the volume of data is low (just 63 records) and you don’t have to do minimal feature engineering to understand its relation with the labels (or the final output).
The Swedish Auto Insurance Dataset involves predicting the…
Data Science Practitioner | Machine Learning | Deep Learning