In this course the student will learn how to use Google Colab and Python's machine learning library, sklearn, to create datasets and use ... In this course the student will learn how to use Google Colab and Python's machine learning library, sklearn, to create datasets and use them in machine learning enterprises. The datasets will be created in sklearn and they are comprised of classifications and regressions, being twenty in total. When the datasets have been created, machine learning techniques will be employed to make predictions on the labels. In addition, the concepts of supervised and unsupervised learning will be discussed. Although most of the examples will be of supervised learning, clustering will be brushed upon.

You have probably heard the terms'low-code' and'no-code' before. Low-code simply stands for a reduced amount of coding. A lot of elements can be simply dragged and dropped from the library. However, it is also possible to customize them by writing your own code, which gives increased flexibility. No-code platforms require no knowledge of programming at all.

You will find in this course a consice review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples using your own datasets.

Recently, I was looking for a toy dataset for my new book's chapter (you can subscribe to the updates here) on instance segmentation. And, I really wanted to have something like the Iris Dataset for Instance Segmentation so that I would be able to explain the model without worrying about the dataset too much. But, alas, it is not always possible to get a dataset that you are looking for. I actually ended up looking through various sources on the internet but inadvertently found that I would need to download a huge amount of data to get anything done. Given that is not at all the right way to go about any tutorial, I thought why not create my own dataset.

The use of artificial intelligence (AI) technologies has spread across the insurance value chain. In product development, it enables insurers to create more profitable and effective products based on insights from past claims and product uptake in the market. In underwriting, it creates a better understanding of risk for new and underserved markets. Even so, the potential of AI has yet to be fully realized. For now, AI's role in the industry is largely limited to optimizing existing business processes rather than developing new and disruptive business models.

Machine learning and artificial intelligence are complex subjects and while you might see them being mentioned every day, you might not necessarily understand how they work. Two years ago, Google launched a site called Teachable Machine, which let you train a simple model using their camera without any code. Now, it's launching an updated version so you can train more advanced models. The earlier version allowed you to train three classes through your camera. The new model, not only lets you define more than three classes, it also allows you to use images, audio clips, pose data, or your own dataset for the training.

Along with the latest PyTorch 1.3 release came with the next generation ground-up rewrite of its previous object detection framework, now called Detectron2. This tutorial will help you get started with this framework by training an instance segmentation model with your custom COCO datasets. If you know how to create COCO datasets, please read my previous post -- How to create custom COCO data set for instance segmentation. For a quick start, we will do our experiment in a Colab Notebook so you don't need to worry about setting up the development environment on your own machine before getting comfortable with Pytorch 1.3 and Detectron2. In the Colab notebook, just run those 4 lines to install the latest Pytorch 1.3 and Detectron2.

A few weeks ago, Facebook open-sourced its platform for object detection research, which they are calling Detectron. Object detection, wherein a machine learning algorithm detects the coordinates of objects in images, remains an ongoing challenge. To find algorithms that provide both sufficient speed and high accuracy is far from a solved problem. Detectron's ostensible purpose is to move a bit closer to that goal using community-led contributions. Google did something similar last year when they released the Tensorflow Object Detection API, which Filestack utilizes for its object detection models, so we thought we'd take a look at Detectron and see how it compares.

This post will explain the concepts of preparing a dataset for convnet using your own codes and helping you to learn the concepts much more intuitive way. Know your data and split it into right labels. Preprocess your image with resizing it and also implement some image augmentation techniques. Learning Capsule network is more interesting and also it showed a state-of-art performance on MNIST Dataset.

ATM is an open source software library under "The human data interaction project" at MIT. It is a distributed scalable AutoML system designed with ease of use in mind. ATM takes in data with pre-extracted feature vectors and labels (target column) in a simple CSV file format. It attempts to learn several classifiers (machine learning models to predict the label) in parallel. In the end, ATM returns a number of classifiers and the best classifier with a specified set of hyperparameters.