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Getting Started with MATLAB Machine Learning Udemy

@machinelearnbot

MATLAB is the language of choice for many researchers and mathematics experts when it comes to machine learning. This video will help beginners build a foundation in machine learning using MATLAB. You'll start by getting your system ready with the MATLAB environment for machine learning and you'll see how to easily interact with the MATLAB workspace. You'll then move on to data cleansing, mining, and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll learn about the different types of regression technique and how to apply them to your data using the MATLAB functions.


Mastering Machine Learning with MATLAB Udemy

@machinelearnbot

MATLAB is the language of choice for many researchers and mathematics experts for Machine Learning. This video course will help you build a foundation in Machine Learning using MATLAB. Then, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. Finally, you will learn to put it all together through real-world cases covering major Machine Learning algorithms and will now be an expert in performing Machine Learning with MATLAB. Giuseppe Ciaburro holds a Master's degree in chemical engineering from Università degli Studi di Napoli Federico II, and a Master's degree in acoustic and noise control from Seconda Università degli Studi di Napoli.


The steps in the machine learning workflow

@machinelearnbot

From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. Each problem is unique, so it can be challenging to manage raw data, identify the right data to include in the model, train multiple types of models, and perform model assessments. Machine learning uses algorithms that learn from data to help make better decisions; however,it is not always obvious what the best machine learning algorithm is going to be for a particular problem. Luckily, information such as variable importance and model assessment tools can help us decide which machine learning techniques to apply. Examples of machine learning techniques include clustering, where objects are grouped into bins with similar traits; regression, where relationships among variables are estimated; and classification, where a trained model is used to predict a categorical response.


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#artificialintelligence

Each problem is unique, so it can be challenging to manage raw data, identify the right data to include in the model, train multiple types of models, and perform model assessments. Machine learning uses algorithms that learn from data to help make better decisions; however,it is not always obvious what the best machine learning algorithm is going to be for a particular problem. Examples of machine learning techniques include clustering, where objects are grouped into bins with similar traits; regression, where relationships among variables are estimated; and classification, where a trained model is used to predict a categorical response. Figure 1: Examples of machine learning include clustering, where objects are grouped into bins with similar traits, and regression, where relationships among variables are estimated.


What's New in MATLAB Data Analytics - MATLAB & Simulink

#artificialintelligence

Use neighborhood component analysis (NCA) to choose features for machine learning models. Manipulate and analyze data that is too big to fit in memory. Perform support vector machine (SVM) and Naive Bayes classification, create bags of decision trees, and fit lasso regression on out-of-memory data. Process big data with tall arrays in parallel on your desktop, MATLAB Distributed Computing Server, and Spark clusters. Manipulate, compare, and store text data efficiently .