Decision Tree Learning: Instructional Materials


Random Forest Algorithm in Machine Learning

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Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. Random Forest Algorithm in Machine Learning: Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.


Learn Machine Learning with Weka Udemy

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This is the bite size course to learn Weka and Machine Learning. You will learn Machine Learning which is the Model and Evaluation of CRISP Data Mining Process. You will learn Linear Regression, Kmeans Clustering, Agglomeration Clustering, KNN, Naive Bayes, Neural Network in this course.


A gentle introduction to decision trees using R

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Most techniques of predictive analytics have their origins in probability or statistical theory (see my post on Naïve Bayes, for example). In this post I'll look at one that has more a commonplace origin: the way in which humans make decisions. When making decisions, we typically identify the options available and then evaluate them based on criteria that are important to us. The intuitive appeal of such a procedure is in no small measure due to the fact that it can be easily explained through a visual. The tree structure depicted here provides a neat, easy-to-follow description of the issue under consideration and its resolution.


Introduction to Machine Learning for Coders: Launch · fast.ai

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The course, recorded at the University of San Francisco as part of the Masters of Science in Data Science curriculum, covers the most important practical foundations for modern machine learning. There are 12 lessons, each of which is around two hours long--a list of all the lessons along with a screenshot from each is at the end of this post. There are some excellent machine learning courses already, most notably the wonderful Coursera course from Andrew Ng. But that course is showing its age now, particularly since it uses Matlab for coursework. This new course uses modern tools and libraries, including python, pandas, scikit-learn, and pytorch.


Ensemble Machine Learning in Python: Random Forest, AdaBoost

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In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.


Fundamentals of Decision Trees in Machine Learning

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A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. In this course, we'll explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables.



Data Mining with R: Go from Beginner to Advanced!

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This is a "hands-on" business analytics, or data analytics course teaching how to use the popular, no-cost R software to perform dozens of data mining tasks using real data and data mining cases. It teaches critical data analysis, data mining, and predictive analytics skills, including data exploration, data visualization, and data mining skills using one of the most popular business analytics software suites used in industry and government today. The course is structured as a series of dozens of demonstrations of how to perform classification and predictive data mining tasks, including building classification trees, building and training decision trees, using random forests, linear modeling, regression, generalized linear modeling, logistic regression, and many different cluster analysis techniques. The course also trains and instructs on "best practices" for using R software, teaching and demonstrating how to install R software and RStudio, the characteristics of the basic data types and structures in R, as well as how to input data into an R session from the keyboard, from user prompts, or by importing files stored on a computer's hard drive. All software, slides, data, and R scripts that are performed in the dozens of case-based demonstration video lessons are included in the course materials so students can "take them home" and apply them to their own unique data analysis and mining cases.


Ensemble Machine Learning in Python: Random Forest, AdaBoost

#artificialintelligence

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.


Machine Learning: An Introduction to Decision Trees

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A decision tree is one of the widely used algorithms for building classification or regression models in data mining and machine learning. A decision tree is so named because the output resulting from it is the form of a tree structure. Consider a sample stock dataset as shown in the table below. The dataset comprises of Open, High, Low, Close Prices and Volume indicators (OHLCV) for the stock. Let us add some technical indicators (RSI, SMA, LMA, ADX) to this dataset.