Goto

Collaborating Authors

Statistical Learning


K-Medoid Clustering (PAM)Algorithm in Python

#artificialintelligence

Clustering of large-scale data is key to implementing segmentation-based algorithms. Segmentation can include identifying customer groups to facilitate targeted marketing, identifying prescriber groups to allow health care players to reach out to them with the right messaging, and identifying patterns or abnormal values in the data. K-Means is the most popular clustering algorithm adopted across different problem areas, mostly owing to its computational efficiency and ease of understanding the algorithm. K-Means relies on identifying cluster centers from the data. It alternates between assigning points to these cluster centers using the Euclidean distance metric and recomputes the cluster centers till a convergence criterion is achieved.


What is "Stochastic" in Stochastic Gradient Descent (SGD)

#artificialintelligence

Over the past 5 months, I had been reading the book Probability Essentials by Jean Jacod and Philip Protter, and the more time I spent on it, more I started to treat every encounter with Probability with a rigorous perspective. Recently, I was reading a paper in Deep Learning and the authors were talking about Stochastic Gradient Descent (SGD), which got me thinking, why is it called "stochastic"? Where is the randomness in it? Disclaimer: I won't be trying to explain any mathematical bits in this article solely because it is a pain to add equations. I hope the reader has some familiarity with the mathematical bits of the Gradient Descent algorithm and its variants. I'll provide a brief introduction where necessary, but won't be going into much detail.


CS229: Machine Learning - AI Summary

#artificialintelligence

CS229: Machine Learning Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control.


Traditional vs Deep Learning Algorithms in the Telecom Industry -- Cloud Architecture and Algorithm Categorization

#artificialintelligence

The unprecedented growth of mobile devices, applications and services have placed the utmost demand on mobile and wireless networking infrastructure. Rapid research and development of 5G systems have found ways to support mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Moreover inference from heterogeneous mobile data from distributed devices experiences challenges due to computational and battery power limitations. ML models employed at the edge-servers are constrained to light-weight to boost model performance by achieving a trade-off between model complexity and accuracy. Also, model compression, pruning, and quantization are largely in place.


A Beginner's Guide to AutoML - Solita Data

#artificialintelligence

Automated Machine Learning (AutoML) is a concept that provides the means to utilise existing data and create models for non-Machine Learning experts. In addition to that, AutoML provides Machine Learning (ML) professionals ways to develop and use effective models without spending time on tasks such as data cleaning and preprocessing, feature engineering, model selection, hyperparameter tuning, etc. Before we move any further, it is important to note that AutoML is not some system that has been developed by a single entity. Several organisations have developed their own AutoML packages. These packages cover a broad area, and targets people at different skill levels.


Exploring the Dow-Jones Industrial Average using Linear Regression

#artificialintelligence

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. It's free, we don't spam, and we never share your email address.


What Are the Most Important Preprocessing Steps in Machine Learning and Data Science?

#artificialintelligence

Data Science and Machine Learning has been the latest talk right now and companies are looking for data scientists and machine learning engineers to handle their data and make significant contributions to them. Whenever data is given to data scientists, they must take the right steps to process them and ensure that the transformed data can be used to train various machine learning models optimally while ensuring maximum efficiency. It is often found that the data that is present in real-world is oftentimes incomplete and inaccurate along with containing a lot of outliers which some machine learning models cannot handle, leading to suboptimal training performance. It is also important to note that there might be duplicate rows or columns in the data which must be dealt with before giving it to machine learning models. Addressing these issues along with many others can be crucial, especially when one wants to improve model performance and generalizing ability of the model.


My First Impression Trying Python on Browser

#artificialintelligence

Whenever we debate with other devs about the best programming language, we talk about JavaScript and Python for hours. Both are powerful, flexible languages that are dominating the world today. But a dead end to Python is its inability to run on browsers. JavaScript (JS), with the discovery of Node, runs on almost any platform. It even has modules to build machine learning algorithms.


Predict Ads Click - Practice Data Analysis and Logistic Regression Prediction - Projects Based Learning

#artificialintelligence

In this project we will be working with a data set, indicating whether or not a particular internet user clicked on an Advertisement. We will try to create a model that will predict whether or not they will click on an ad based off the features of that user. Welcome to this project on predict Ads Click in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id. In this project, we explore Apache Spark and Machine Learning on the Databricks platform. I am a firm believer that the best way to learn is by doing.


Techinfoplace Softwares Pvt Ltd.

#artificialintelligence

Problem Statement A target marketing campaign for a bank was undertaken to identify a segment of customers who are likely to respond to an insurance product. Here, the target variable is whether or not the customers bought insurance product and it depends on factors like Product usage in three months, demographics, transaction patterns as like deposit amount, checking account, a branch of the bank, Residential information (like urban, rural) and so on.