Collaborating Authors

How to Become a Machine Learning Specialist in Under 20 Hours from This FREE LinkedIn Course


If you are interested to become a Machine Learning Specialist, you are in the right place, because here we have the best LinkedIn course that you will love it. Machine Learning proves to be the future of our civilization, something that will help us to elevate our achievements to the next level, and explore new things, and all in all increase the quality of our life. The job positions in Machine Learning areas are one of the highest paying in the whole IT industry due to the fact that it requires knowledge in Mathematics, Statistics, Computer Science, and Software Engineering all combined. Now, to gain knowledge in all of these fields can be time-consuming due to all of those are sciences in themselves. However, there are huge corporations that have a huge need for experts in these areas and do not have the time that it takes to create these experts as we've already mentioned.

Decision Tree vs. Random Forest - Which Algorithm Should you Use?


Let's start with a thought experiment that will illustrate the difference between a decision tree and a random forest model. Suppose a bank has to approve a small loan amount for a customer and the bank needs to make a decision quickly. The bank checks the person's credit history and their financial condition and finds that they haven't re-paid the older loan yet. Hence, the bank rejects the application. But here's the catch – the loan amount was very small for the bank's immense coffers and they could have easily approved it in a very low-risk move. Therefore, the bank lost the chance of making some money.

Python for Machine Learning and Data Mining


Data Mining and Machine Learching are a hot topics on business intelligence strategy on many companies in the world. These fields give to data scientists the opportunity to explore on a deep way the data, finding new valuable information and constructing intelligence algorithms who can "learn" since the data and make optimal decisions for classification or forecasting tasks. This course is focused on practical approach, so i'll supply you useful snippet codes and i'll teach you how to build professional desktop applications for machine learning and datamining with python language. We'll also manage real data from an example of a real trading company and presenting our results in a professional view with very illustrated graphical charts. We'll initiate at the basic level covering the main topics of Python Language and also the needing programs to develop our applications.

Machine Learning: An Introduction to Decision Trees


Machine Learning for trading is the new buzz word today and some of the tech companies are doing wonderful unimaginable things with it. Today, we're going to show you, how you can predict stock movements (that's either up or down) with the help of'Decision Trees', one of the most commonly used ML algorithms. Decision trees in Machine Learning are used for building classification and regression models to be used in data mining and trading. A decision tree algorithm performs a set of recursive actions before it arrives at the end result and when you plot these actions on a screen, the visual looks like a big tree, hence the name'Decision Tree'. Basically, a decision tree is a flowchart to help you make decisions.

What are Decision Tree Algorithms? 🌳


This article will cover one of the most advanced algorithms and most widely used in analytical applications. This is an extensive subject, as we have several algorithms and various techniques for working with decision trees. On the other hand, these algorithms are among the most powerful in Machine Learning and are easy to interpret. So, let's start by defining what decision trees are and their representation through machine learning algorithms. For decision tree learning models, we will study some algorithms with C4.5, C5.0, CART, and ID3.