"A text classifier is an automated means of determining some metadata about a document. Text classifiers are used for such diverse needs as spam filtering, suggesting categories for indexing a document created in a content management system, or automatically sorting help desk requests."
– John Graham-Cumming, Naive Bayesian Text Classification. Dr. Dobb's. May 1 2005.
In this tutorial, I'll show you how to create a single label classification model in Google AutoML. We'll be using a dataset of AI-generated faces from generated.photos. We'll be training our algorithm to determine whether a face is male or female. After that, we'll deploy our model to the cloud AND create the web browser version of the algorithm. First let's take a look at the data we'll be classifying (you can download it here).
Data Science is about explaining the past and predicting the future by means of data analysis. Data Science is a multi-disciplinary field which combines statistics, machine learning, artificial intelligence and database technology. This course provides the essential concepts and principles in data science. Students learns commonly used classification algorithms and how to use those algorithms to solve real world problems.
Many imbalanced classification tasks require a skillful model that predicts a crisp class label, where both classes are equally important. An example of an imbalanced classification problem where a class label is required and both classes are equally important is the detection of oil spills or slicks in satellite images. The detection of a spill requires mobilizing an expensive response, and missing an event is equally expensive, causing damage to the environment. One way to evaluate imbalanced classification models that predict crisp labels is to calculate the separate accuracy on the positive class and the negative class, referred to as sensitivity and specificity. These two measures can then be averaged using the geometric mean, referred to as the G-mean, that is insensitive to the skewed class distribution and correctly reports on the skill of the model on both classes. In this tutorial, you will discover how to develop a model to predict the presence of an oil spill in satellite images and evaluate it using the G-mean metric. Develop an Imbalanced Classification Model to Detect Oil Spills Photo by Lenny K Photography, some rights reserved. In this project, we will use a standard imbalanced machine learning dataset referred to as the "oil spill" dataset, "oil slicks" dataset or simply "oil."
WHAT YOU WILL LEARN Understand how to interpret the result of Logistic Regression model and translate them into actionable insight Learn how to solve real life problem using the different classification techniques Predict future outcomes basis past data by implementing Machine Learning algorithm Course contains a end-to-end DIY project to implement your learnings from the lectures The course "Machine Learning Basics: Classification models in Python" teaches you all the steps of creating a Classification model to solve business problems. Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Which all classification techniques are taught in this course?
Text classification implementation with TensorFlow can be simple. One of the areas where text classification can be applied -- chatbot text processing and intent resolution. I will describe step by step in this post, how to build TensorFlow model for text classification and how classification is done. Please refer to my previous post related to similar topic -- Contextual Chatbot with TensorFlow, Node.js and Oracle JET -- Steps How to Install and Get It Working. I would recommend to go through this great post about chatbot implementation -- Contextual Chatbots with Tensorflow.
In this tutorial, we will cover Natural Language Processing for Text Classification with NLTK & Scikit-learn. Remember the last Natural Language Processing project we did? We will be using all that information to create a Spam filter. This tutorial will also cover Feature Engineering and ensemble NLP in text classification. This project will use Jupiter Notebook running Python 2.7.
In this webinar, we'll answer real data science questions like this using Spotfire and TERR to make smarter decisions. For our next webinar, we'll be managing a hotel's marketing group, using classification methods inside of Spotfire. This is the fourth step in our five-part webinar series called the Building Blocks of Data Science. In this series, we will explore solving real data science questions using Spotfire and TERR. We'll walk you through the concepts and then do a live example using the technique.
But you don't know where to start, or perhaps you have read some theory, but don't know how to implement what you have learned. This tutorial will help you break the ice, and walk you through the complete process from importing and analysing a dataset to implementing and training a few different well known classification algorithms and assessing their performance. I'll be using a minimal amount of discrete mathematics, and aim to express details using intuition, and concrete examples instead of dense mathematical formulas. You can read why here. We will be classifying flower-species based on their sepal and petal characteristics using the Iris flower dataset which you can download from Kaggle here. Kaggle, if you haven't heard of it, has a ton of cool open datasets, and is a place where data scientists share their work which can be a valuable resource when learning.