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### Two Methods for Performing Graphical Residuals Analysis

An essential part of a regression analysis is to understand if we can use a linear model or not for solving our ML problem. There are many ways to do this, and, generally, we have to use multiple ways to understand if our data are really linear distributed. In this article, we will see two different graphical methods for analyzing the residuals in a regression problem: but those are just two methods useful for understanding if our data are linearly distributed. You can use just one of these methods, or even both, but you will need the help of other metrics to better validate your hypothesis (the model to be used is linear): we'll see other methods in future articles. But first of all…what are the residuals in a regression problem?

### Pan-African Artificial Intelligence and Smart Systems

This book constitutes the refereed post-conference proceedings of the First International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2021, which was held in Windhoek, Namibia, in September 2021. The 17 revised full papers presented were carefully selected from 41 submissions. The theme of PAAISS 2021 was "Advancing AI research in Africa" and the papers are arranged according to subject areas: Deep Learning; Classification and Pattern Recognition; Neural Networks and Support Vector Machines; Smart Systems.

### Popular Machine Learning Algorithms - KDnuggets

When starting out with Data Science, there is so much to learn it can become quite overwhelming. This guide will help aspiring data scientists and machine learning engineers gain better knowledge and experience. I will list different types of machine learning algorithms, which can be used with both Python and R. Linear Regression is the simplest Machine learning algorithm that branches off from Supervised Learning. It is primarily used to solve regression problems and make predictions on continuous dependent variables with the knowledge from independent variables. The goal of Linear Regression is to find the line of best fit, which can help predict the output for continuous dependent variables.

### K-Medoid Clustering (PAM)Algorithm in Python

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)

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

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.

### What is machine learning ML?

Machine learning algorithms are behind almost every artificial intelligence technology advancement and application on the market in today's world .

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

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

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

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