Accessing and Manipulating Data in R: Using R in Data Science and ML

Accessing and Manipulating Data in R Perhaps as you may have noted in following this series from the Art of Better Programming, we have gone through great lengths to provide knowledge and resources to you for learning data science. We began primarily with a variety of series that elaborated on data science with Python. WeContinue reading “Accessing and Manipulating Data in R: Using R in Data Science and ML”

Transition From Python To R: Using R in Data Science and ML

An Introduction To R The Art of Better Programming has given an extensive discussion of machine learning and data science from the perspective of Python. Studies show that Python is the most represented programming language in data science, exhibiting a use in over 50% of cases. However, if you want to be an employable dataContinue reading “Transition From Python To R: Using R in Data Science and ML”

Ridge Regression Linear Models: Topics of Machine Learning

Introduction to Ridge Regresison This present article moves away from traditional linear and polynomial regression to a more nuanced form of regression known as ridge regression. Previously, we spent a great deal of time reviewing traditional machine learning models and investigated the performance measures associated with these algorithms. We then took great lengths in examiningContinue reading “Ridge Regression Linear Models: Topics of Machine Learning”

Learning Curves in Machine Learning Models: Topics of Machine Learning

Introduction to Learning Curves For quite some time now in our machine learning series, we have belabored various aspects of regression algorithms in machine learning. Our first insight to this concept manifested in our initial article discussing supervised machine learning models, which may be found here. After exploring a variety of different machine learning models and embarkingContinue reading “Learning Curves in Machine Learning Models: Topics of Machine Learning”

Top Five Resources For Machine Learning: Topics of Machine Learning

Introduction to Machine Learning Resources Recently, we have received a large amount of requests for useful resources in attempting to understand machine learning from first principles. These requests inspired the creation of this article. Just so we can set the scene for you, we want to take a moment to reflect on the topics ofContinue reading “Top Five Resources For Machine Learning: Topics of Machine Learning”

Regression From the Mathematical Perspective: Topics of Data Modeling

Prologue Before we begin, take a second. Open this link in a new tab. Did you do it? I’ll wait. Oh, you opened it? Great. Sorry for the forwardness, but this book is one of the greatest tools I have used in taking myself from a beginner in programming to a full time data scientist.Continue reading “Regression From the Mathematical Perspective: Topics of Data Modeling”

Polynomial Regression Algorithms: Topics of Machine Learning

Introduction to Polynomial Regression The present article seeks to elucidate the myriad of features associated with polynomial regression with respect to its role in machine learning. We previously brought to light a variety of features associated with linear regression in machine learning models. We first provided an introduction to linear regression in this article. FollowingContinue reading “Polynomial Regression Algorithms: Topics of Machine Learning”

Stochastic Gradient Descent Algorithms: Topics of Machine Learning

Introduction to Stochastic Gradient Descent Our previous articles investigated the importance of linear regression and batch gradient descent in machine learning modeling, as well as the intricacies of Gradient Descent models. With respect to gradient descent, here we investigate linear regression as it relates to stochastic gradient descent machine learning. Before embarking on this discussion,Continue reading “Stochastic Gradient Descent Algorithms: Topics of Machine Learning”

Batch Gradient Descent Algorithms: Topics of Machine Learning

Introduction to Batch Gradient Descent Our previous article investigated the importance of linear regression in machine learning modeling, as well as the intricacies of Gradient Descent models. With respect to gradient descent, here we investigate linear regression as it relates to batch gradient descent machine learning. Before embarking on this discussion, we would like toContinue reading “Batch Gradient Descent Algorithms: Topics of Machine Learning”

Linear Regression and Academic Literature: 100 Days of Code (3/100)

Day 3 Of 100 Days of Code Our third day of this 100 Days of Code was filled with in depth analysis of linear regression and elaboration on gradient descent algorithms. What should first be noted is the fact that we successfully published two deeply interwoven articles. The first analyzed in depth linear regression algorithms,Continue reading “Linear Regression and Academic Literature: 100 Days of Code (3/100)”

Gradient Descent Model Machine Learning: Topics of Machine Learning

Introduction to Gradient Descent Our previous article investigated the importance of linear regression in machine learning modeling, but here we focus on implementing this technique. In particular, we investigate linear regression as it relates to gradient descent machine learning. Before embarking on this discussion, we would like to provide a brief overview on the analysesContinue reading “Gradient Descent Model Machine Learning: Topics of Machine Learning”

Linear Regression Models: Topics of Machine Learning

An Introduction to Linear Regression The present article endeavors to explore the intricacies of linear regression models in machine learning. Before embarking on this discussion, we would like to provide a brief overview on the analyses explored in the Topics of Machine Learning series. Much of our investigative efforts have centered on one of twoContinue reading “Linear Regression Models: Topics of Machine Learning”

Multi-Class Classification and Literature: 100 Days of Code (2/100)

Day 2 Of 100 Days of Code We are now underway in this journey of better understanding this coding ecosystem we inhabit. If you’ve followed along the Art of Better Programming thus far, you are well aware that our primary focus to date has been a thorough elaboration on the implementation of machine learning modelsContinue reading “Multi-Class Classification and Literature: 100 Days of Code (2/100)”

Multi-Class Classification In Machine Learning: Topics of Machine Learning

Introduction to Multi-Class Classification Previous Efforts of Machine Learning Series The Topics of Machine Learning series took a bit of hiatus to explore performance measures after examining the various performance measures to quantify efficiency of machine learning models. This particular deviates from this tune to explore multi-class classification. Briefly, thus far in the Topics of Machine Learning series, weContinue reading “Multi-Class Classification In Machine Learning: Topics of Machine Learning”

Performance Measurement in Machine Learning: 100 Days of Code (1/100)

The Motivation The Art of Better Programming has grown beyond what we had ever anticipated. With several collaborators now working together to bring programmers unique material to follow along in their training endeavors, the sky’s the limit right now. With that being said, for those who are just now joining us on this journey, weContinue reading “Performance Measurement in Machine Learning: 100 Days of Code (1/100)”

The ROC Curve in Measuring Algorithmic Performance: Topics of Machine Learning

Introduction to the ROC Curve Previous Efforts The Topics of Machine Learning series has steadily moved from overarching principles of machine learning models to the bits and pieces which drive these models to function. With our previous article having investigated the implementation of the confusion matrix and the role of precision and recall, we now move toContinue reading “The ROC Curve in Measuring Algorithmic Performance: Topics of Machine Learning”

Precision and Recall in Algorithmic Performance: Topics of Machine Learning

Introduction to Precision and Recall The Topics of Machine Learning series has steadily moved from overarching principles of machine learning models to the bits and pieces which drive these models to function. With our previous article having investigated the implementation of the confusion matrix, we now move to conceptualizing the underlying theory of precision and recall. ThusContinue reading “Precision and Recall in Algorithmic Performance: Topics of Machine Learning”

Confusion Matrix and Measurement of Algorithmic Recall: Topics of Machine Learning

Introduction to the Confusion Matrix The Topics of Machine Learning series has invested a healthy amount of effort in providing the various tools available for amplifying machine learning sophistication. This article follows suit, providing a technique for validating your model’s performance with the implementation of the confusion matrix. Within this Topics of Machine Learning series, we elaboratedContinue reading “Confusion Matrix and Measurement of Algorithmic Recall: Topics of Machine Learning”

Cross-Validation and Measurement of Algorithmic Accuracy: Topics of Machine Learning

Introduction to Cross-Validation With the Topics of Machine Learning series in mind, our present discussion addresses a particular technique of performance measurement, the cross-validation methodology. Within this Topics of Machine Learning series, we elaborated extensively on the various machine learning model categories that exist. Our first article to this series provided an overview to all of theseContinue reading “Cross-Validation and Measurement of Algorithmic Accuracy: Topics of Machine Learning”

An Introduction to the MNIST Data Set for Machine Learning: Topics of Machine Learning

An Introduction to the MNIST Data Set The present article elaborates on the essential features of the MNIST data set for training machine learning models. Within this Topics of Machine Learning series, we elaborated extensively on the various machine learning model categories that exist. Our first article to this series provided an overview to allContinue reading “An Introduction to the MNIST Data Set for Machine Learning: Topics of Machine Learning”

Examining XML Data With Cancer Clinical Trial Data: Topics of Data Analytics

Introduction to XML Data The Topics of Data Analytics series inclines toward stipulating the most essential tools of analyzing data. We are still at an embryonic point in this investigation, but have elaborated in particular on two important subjects. We first began exploring the features of CSV files and CSV derived data. This discussion wasContinue reading “Examining XML Data With Cancer Clinical Trial Data: Topics of Data Analytics”

Future Endeavors For The Art of Better Programming

Looking Back This blog has tackled a wide variety of subjects, all motivated to improve the programmer’s toolset for all their data science endeavors. For examining all of this content, check out the following link. These matters of discussion include: Multivariate Calculus: Vector Calculus Partial Derivatives Linear Algebra Gaussian Systems Linear Combinations Coding Projects WebContinue reading “Future Endeavors For The Art of Better Programming”

A Guide to Probability With Biological Data: Topics of Data Modeling

Prologue Before we begin, take a second. Open this link in a new tab. Did you do it? I’ll wait. Oh, you opened it? Great. Sorry for the forwardness, but this book is one of the greatest tools I have used in taking myself from a beginner in programming to a full time data scientist.Continue reading “A Guide to Probability With Biological Data: Topics of Data Modeling”

The Correlation Coefficient with Biological Data: Topics of Data Modeling

Introduction to the Correlation Coefficient Our discussion in this series on data modeling focuses on statistical tools which function to statistically represent biological data. The initial articles gave an initial foray into the topic, addressing the uses of statistics and a bit of insight into the concept of sampling. Following this, we expanded upon the generalContinue reading “The Correlation Coefficient with Biological Data: Topics of Data Modeling”

Working with JSON Data in Modeling Crime: Topics of Data Analytics

Introduction to JSON Data The Topics of Data Analytics series has been working thoroughly to deliver the tools that provide the greatest utility. The series began by explicating the dynamics behind working with CSV derived data. We provided insight into what CSV data consists of and the structure that defines it. Furthermore, we expanded onContinue reading “Working with JSON Data in Modeling Crime: Topics of Data Analytics”

Instance and Model-Based Learning: Topics of Machine Learning

Introducing Instance and Model-Based Learning Our series on Machine Learning has elaborated on a variety of topics related to the subject. Firstly, we began by providing an overview to the various machine learning systems that appear in data science, as well as the algorithms associated with these systems. You can explore this overview, which alsoContinue reading “Instance and Model-Based Learning: Topics of Machine Learning”

Differentiating Batch and Online Learning: Topics of Machine Learning

Introduction to Batch and Online Learning Our series on Machine Learning has elaborated on a variety of topics related to the subject. Firstly, we began by providing an overview to the various machine learning systems that appear in data science, as well as the algorithms associated with these systems. You can explore this overview, whichContinue reading “Differentiating Batch and Online Learning: Topics of Machine Learning”

A Guide to Working with CSV Data: Topics of Data Analytics

Introduction to CSV Data The Data Analytics series intends to provide individuals with helpful tools for approaching data analysis and manipulation. Our introduction to this series focuses specifically on working with CSV data, as these files are perhaps the simplest in terms of malleability. CSV data structures are principally organize such that data associates directlyContinue reading “A Guide to Working with CSV Data: Topics of Data Analytics”

Unsupervised Machine Learning Models: Topics of Machine Learning

Introduction to Unsupervised Machine Learning Our initial article on this subject introduced our presently comprehensive machine learning series. This article provided an overview to all the various machine learning systems and the functions they execute. The machine learning models extrapolated on therein include supervised, unsupervised, batch, online, instance-based, and model-based learning methodologies. Our subsequent articlesContinue reading “Unsupervised Machine Learning Models: Topics of Machine Learning”

Supervised Machine Learning Algorithms: Topics of Machine Learning

Introduction to Supervised Machine Learning We now reach a point where we intend to get deeper into the various methodologies of machine learning. Our first article of this series presented all of the general categories of machine learning and their implications. Therein, we documented the various mechanisms and algorithms associated with these categories. They includedContinue reading “Supervised Machine Learning Algorithms: Topics of Machine Learning”