Collaborating Authors

Restaurant Reviews Analysis Model Based on ML Algorithms


This article was published as a part of the blog. In this article, we will be dealing with the Restaurant reviews dataset. In this dataset, there are reviews from the customers which are either positive or negative. And now we are going to build a machine learning model using both Support Vector Classifier(SVC) and Count Vectorizer methods. And finally, this model is going to predict whether the given review is either positive or negative.

Embedding Machine Learning Models to Web Apps (Part-1)


The best way to learn data science is by doing it, and there's no other alternative . From this post, I am going to reflect my learning on how I developed a machine learning model, which can classify movies reviews as positive or negative, and how I embed this model to a Python Flask web application. The ultimate goal is to sail through an end to end project. I firmly believe at the end of this post, you'll be equipped with all the necessary skill that need to embed an ML model to a web application. I came across this end to project on the book, "Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition"[1] by Sebastian Raschka and Vahid Mirjalili.

How to Build your First Machine Learning Model in Python


So what machine learning model are we building today? In this article, we are going to be building a regression model using the random forest algorithm on the solubility dataset. After model building, we are going to apply the model to make predictions followed by model performance evaluation and data visualization of its results. So which dataset are we going to use? The default answer may be to use a toy dataset as an example such as the Iris dataset (classification) or the Boston housing dataset (regression).



This article is for current and aspiring machine learning practitioners looking to implement solutions to real-world machine learning problems. It is an introductory article suitable for beginners with no previous knowledge of machine learning or artificial intelligence (AI). This is the first article on my series "Machine Learning with Python". I will introduce the fundamental concepts of Machine Learning, its applications and how to set up our working environment as well as a hands on practices on a simple project. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.

Projects to Learn Natural Language Processing - Analytics Vidhya


Machines understanding language fascinates me, and that I often ponder which algorithms Aristotle would have accustomed build a rhetorical analysis machine if he had the possibility. If you're new to Data Science, getting into NLP can seem complicated, especially since there are many recent advancements within the field. While a computer can be quite good at finding patterns and summarizing documents, it must transform words into numbers before making sense of them. This transformation is highly required because math doesn't work very well on words and machines "learn" thanks to mathematics. Before the transformation of the words into numbers, Data cleaning is required.