July has come and gone (some time ago, at this point), but we're going to revisit and rundown the top posts on KDnuggets for the month. Far and away the most popular post of July was Machine Learning Algorithms Explained in Less Than 1 Minute Each by Nisha Arya. Looking to get the ELI5 on some of the most popular algorithms? KDnuggets Editor Matthew Mayo shared a pair of well-received posts highlighting Python courses. The posts are Free Python Automation Course and Free Python Crash Course, the contents of which are self-explanatory.

Researchers at Stanford have developed an Artificial Intelligence (AI) model,' StockBot', which uses LSTMs to predict stock prices with gains higher than the most aggressive ETFs Comprehensive Guide to GitHub for Data Scientists The 8 Neural Network Architectures #MachineLearning Researchers Need to Learn #KDnuggets Vaex: Pandas but 1000x faster #KDnuggets 10 Modern Data Engineering Tools #KDnuggets Researchers at Stanford have developed an Artificial Intelligence (AI) model,' StockBot', which uses LSTMs to predict stock prices with gains higher than the most aggressive ETFs Machine Learning Algorithms Explained in Less Than 1 Minute Each by Nisha Arya Free Python Automation Course by Matthew Mayo Decision Tree Algorithm, Explained by Nagesh Singh Chauhan Parallel Processing Large File in Python by Abid Ali Awan The 5 Hardest Things to Do in SQL by Josh Berry Linear Algebra for Data Science by Benjamin O. Tayo Free Python Crash Course by Matthew Mayo 15 Python Coding Interview Questions You Must Know For Data Science by Nate Rosidi Top Programming Languages and Their Uses by Claire D. Costa Naive Bayes Algorithm: Everything You Need to Know by Nagesh Singh Chauhan

Linear Regression: For statistical techniques, linear regression is used in which the value of the dependent variable is predicted through independent variables. A relationship is formed by mapping the dependent and independent variable on a line, and that line is called the regression line, which is represented by Y a*X b where Y Dependent variable (for example, weight) X Independent Variable (e.g., height) b Intercept and a slope. Logistic Regression: In logistic regression, we have a lot of data whose classification is done by building an equation. This method is used to find the discrete dependent variable from the set of independent variables. Its goal is to find the best fit set of parameters. In this classifier, each feature is multiplied by a weight, and then all are added.

Machine Learning Algorithms Explained in Less Than 1 Minute Each; Parallel Processing Large File in Python; Free Python Automation Course; How Does Logistic Regression Work?; 12 Most Challenging Data Science Interview Questions

This article will explain some of the most well known machine learning algorithms in less than a minute - helping everyone to understand them! One of the simplest Machine learning algorithms out there, Linear Regression is used to make predictions on continuous dependent variables with knowledge from independent variables. A dependent variable is the effect, in which its value depends on changes in the independent variable. You may remember the line of best fit from school - this is what Linear Regression produces. A simple example is predicting one's weight depending on their height.

The prevalence of machine learning has been increasing tremendously in recent years due to the high demand and advancements in technology. The potential of machine learning to create value out of data has made it appealing for businesses in many different industries. Most machine learning products are designed and implemented with off-the-shelf machine learning algorithms with some tuning and minor changes. In this post, I will cover the most common algorithms in the first two categories. Note: Although deep learning is a sub-field of machine learning, I will not include any deep learning algorithms in this post.

If you think deep, you'd realize the whole process of predictive modeling is a war. Consider the data set as your opponent. Your win will depend on the intuitive usage of your knowledge & strategy to get highest accuracy. So, how many times have you won this battle? I realized it last week.

If you think deep, you'd realize the whole process of predictive modeling is a war. Consider the data set as your opponent. Your win will depend on the intuitive usage of your knowledge & strategy to get highest accuracy. So, how many times have you won this battle? I realized it last week.