## An Introduction to Image Recognition

You can access the full course here: Convolutional Neural Networks for Image Classification Intro to Image Recognition Letâ€™s get started by learning a bit about the topic itself. Image recognition is, at its heart, image classification so we will use these terms interchangeably throughout this course. We see images or real-world items and we classify … Read more

## How to do Cluster Analysis with Python

You can access the full course here: Data Insights with Cluster Analysis Part 1 In this video we are going to discuss Cluster Analysis. We will discuss the following topics: Intro to Cluster Analysis â€“ what is it, what are it’s different applications, the kinds of algorithms we can expect. K-means clustering Density-based Spatial Clustering … Read more

## Using Neural Networks for Regression: Radial Basis Function Networks

Neural Networks are very powerful models for classification tasks. But what about regression? Suppose we had a set of data points and wanted to project that trend into the future to make predictions. Regression has many applications in finance, physics, biology, and many other fields. Radial Basis Function Networks (RBF nets) are used for exactly … Read more

## Face Recognition with Eigenfaces

Face recognition is ubiquitousÂ in science fiction: the protagonist looks at a camera, and the camera scans his or her face to recognize the person. More formally, we can formulate face recognition as a classification task, where the inputs are images and the outputs are people’s names. We’re going to discuss a popular technique for face … Read more

## Clustering with Gaussian Mixture Models

Clustering is an essential part of any data analysis. Using an algorithm such as K-Means leads toÂ hard assignments, meaning that each point is definitively assigned a cluster center. This leads to some interesting problems: what if the true clusters actually overlap? What about data that is more spread out; how do we assign clusters then? … Read more

## Perceptrons: The First Neural Networks

Neural Networks have become incredibly popular over the past few years, and new architectures, neuron types, activation functions, and training techniques pop up all the time in research. But without a fundamental understanding of neural networks, it can be quite difficult to keep up with the flurry of new work in this area. To understand … Read more

## Text Classification Tutorial with Naive Bayes

The challenge of text classification is to attach labels to bodies of text, e.g., tax document, medical form, etc. based on the text itself. For example, think of your spam folder in your email. How does your email provider know that a particular message is spam or “ham” (not spam)? We’ll take a look at … Read more

## Classification with Support Vector Machines

One of the most widely-used and robust classifiers is the support vector machine. Not only can it efficiently classify linear decision boundaries, but it can also classify non-linear boundaries and solve linearly inseparable problems. We’ll be discussing the inner workings of this classification jack-of-all-trades. We first have to review the perceptron so we can talk … Read more

## An Introduction to Machine Learning

You can access the full course here: Machine Learning for Beginners with TensorFlow Intro to Machine Learning Now that we know what the course is all about, letâ€™s learn a bit about the main topic: machine learning. What is machine learning? Machine learning is the study of statistics and algorithms aimed at performing a task … Read more

## How to Build a Spam Detector

You can access the full course here: Build a Spam Detector AI with Text Classification Transcript Part 1 Hello everybody my name is Mohit Deshpande, and in this course we’ll be building an AI that would be able to determine if an input email is spam or not. And so you can see in … Read more