Machine Learning

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The Machine Learning Course is currently the most practical course available on the web. Rather than conducting memorization, we have structured lessons systematically, which will help with problem-solving with realistic goals. This course aims to help you start from the beginning and become a Machine Learning Engineer. This course is a must for you if you want to:

  1. Gain a theoretical understanding of data analysis and how to apply it in MATLAB.
  2. Develop theoretical principles as well as MATLAB implementations of machine learning algorithms.
  3. Understand how to use the learner app in MATLAB for machine learning.

This course is built for both complete beginners who had never programmed before and experienced programmers who want to extend their career opportunities by understanding Machine Learning. This course takes a very realistic approach, and we'll start from the beginning. After a few introductory tutorials, we'll dive straight into coding. Furthermore, the course is jam-packed with hands-on activities focused on real-life scenarios. As a result, you'll not only learn the theory, but you'll also get some experience while designing your models. By the end of the course, you'll be able to create Machine Learning Model.

Machine Learning is a Premium Course from MATLAB Helper. Book here.

Machine Learning

Data Handling in MATLAB

4 Lessons

Learn how to Import data interactively into MATLAB using a system clipboard. Following ways, we can use to import data interactively. 1) Click on the import data tab in the variable section of the Home tab. 2) By choosing a file from the directory. 3) uiimport command will open a dialog box in the MATLAB environment.

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Learn to import different files and data into MATLAB using programming.

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Learn to export different data into files from MATLAB using programming.

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Standard arrays represent valuable data structures for storing objects. Arrays cannot be used to memorize both numbers and strings in the same object. We can solve this issue by using cell arrays and structure arrays.

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Data Preparation & Preprocessing

9 Lessons

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Learn how to access, replace and remove any particular attribute or any observation from the table.

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Exploratory Data Analysis will provide a systematic approach to extract information or summarize critical data characteristics.

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Learn how to use Mean, median, and mode terms for getting information about the tendency of data distribution.

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Dispersion measures provide information on the spread of data around the middle. In this section, we can evaluate dispersion measurements with the aid of variance and standard deviation terminology.

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Skewness tells us the skewed magnitude and direction of the data set. Compared to a regular bell curve, Kurtosis tells us how high and sharp the central peak is.

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Learn the graphical way to get insight from the data. A graph is intended for the rapid visualization of a data set.

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An outlier is a piece of data that is abnormally distant from other points. In other words, this is the data that lies outside the other values in the data set. So, making a machine learning model without removing the outlier value makes the model a bias. Essential steps are needed to make the model unbiased to remove outliers from the data set. By deleting or replacing the outlier's value, we can vanish outliers from the data. However, it is not recommended to delete any information from the data method.

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Most of the practical data sets are categorical. We cannot directly apply categorical data to modeling. Most machine learning algorithms allow only numeric data to be used while modeling. So, to make the categorical data acceptable to ML, we have to perform some operations on the categorical data.

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Feature scaling is one of the steps we have to follow before starting the actual machine learning model to make data well. With the help of feature scaling methods, we scale input data to the same level. Thus, different ranges and units of measurement problems of input features are avoided.

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Transformation Techniques & Feature Selection

2 Lessons

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Transformation and Feature Selection Techniques play an essential role in improving the accuracy of the model. Both techniques are not necessary, and our model could still run without them. But the use of these techniques may make it possible for the model to produce better results. In this lesson, we focus on when and how to use transformation techniques.

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Machine learning feature selection aims to find the best collection of features to create an efficient model from the data collected. It is almost rare that all the variables or features in the dataset are helpful when creating a machine learning model in real life. Adding unnecessary variables decreases the model's potential and may also decrease the model's overall precision. Adding more and more variables or attributes to the model often increases the model's overall complexity.

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Introduction to Machine Learning Algorithms

2 Lessons

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Machine learning is a branch of artificial intelligence that allows computers to learn and improve independently without being explicitly programmed. When we use the traditional programming methodology, we get output after applying the programmatic structure to the input data. However, in machine learning, we provide input and output, and the machine learning structure writes the program for us. Auto-complete text, time-series analysis, video recommendation, email screening, and many more machine learning applications are likely to come up in our interactions.

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The first and most crucial step in choosing an algorithm is understanding your data. Before you begin to consider the various algorithms, you must first become acquainted with your results. One easy way to do this is to visualize the data, look for patterns, analyze its behavior, and, most importantly, its size.

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Unsupervised Learning Algorithms

5 Lessons

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K-means is a centroid-based or distance-based algorithm in which the distances between points are calculated to allocate a point to a cluster. Each cluster in K-Means is associated with a centroid. This algorithm aims to find groups in the data, with K representing the number of groups.

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Learn How to implement the k-means algorithm in MATLAB.

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One of the most common and straightforward clustering techniques is hierarchical clustering. There are two kinds of clustering algorithms:1) Agglomerative ClusteringEach data point is first treated as a separate cluster in this method. Similar clusters merge with other clusters in each iteration until one cluster or K clusters are created.2) Divisive ClusteringStrictly speaking, Divisive Hierarchical Clustering is the exact opposite of Agglomerative Hierarchical Clustering. We treat all data points as a single cluster in Divisive Hierarchical Clustering. We separate the data points that are not identical from the cluster in each iteration. Each cluster is made up of separated data points. We'll end up with n clusters. Divisive Hierarchical Clustering is named after the fact that we divide single clusters into n clusters.

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Learn how to implement hierarchical clustering in MATLAB.

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The main goal of principal component analysis (PCA) is to minimize the dimensionality of a data set made up of several variables associated with each other, either strongly or weakly, while preserving as much variance as possible. To get principal components generally, we transform higher dimensional data to lower-dimensional data while transformed data is maintaining equal information.

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Regression

7 Lessons

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In statistics and machine learning, linear regression is one of the most well-known and well-understood techniques. We can use statistics to estimate the coefficients in simple linear regression when there is only one input. Making predictions is as straightforward as solving the equation for a given set of inputs because the representation is a linear equation.

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Learn how to implement simple linear regression in MATLAB.

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Multiple Linear Regression (MLR) is similar to Simple Linear Regression. It uses two or more variables to predict the outcome of another variable instead of just one.

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Learn how to implement multiple linear regression in MATLAB.

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Machine learning uses another method from statistics: logistic regression. It's the form of preference for binary classification issues.

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Learn how to implement Logistic Regression in MATLAB.

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Support vector machine, decision tree, random forest, and other algorithms are examples of algorithms that we can use to solve regression and classification problems. CART stands for classification and regression technique. These algorithms are known as CART models. In regression analysis, we hardly use CART models.

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Classification

10 Lessons

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K-Nearest Neighbour's is one of Machine Learning's most basic but crucial categorization algorithms. Pattern recognition, data mining, and intrusion detection are just a few of the applications it finds in the supervised learning domain.

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Learn how to implement K-Nearest Neighbors in MATLAB & Classification Learner App.

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Naïve Bayes is a simple supervised machine learning technique that employs Bayes theorem and strong independence assumptions between features.

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Learn how to implement Naïve Bayes in MATLAB & Classification Learner App.

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The Support Vector Machine (SVM) is a supervised machine learning technique applied to classification and regression problems. It is primarily used in classification issues.

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Learn how to implement Support Vector Machine in MATLAB & Classification Learner App.

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The decision tree algorithm is part of the supervised machine learning family of algorithms. We can use it to solve a classification problem or a regression problem.

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Learn how to implement Decision Tree in MATLAB & Classification Learner App.

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Ensemble modeling is an effective strategy for improving the model's performance. It is a method of bringing together different groups of learners to increase overall performance.

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Learn how to implement Ensemble Techniques in MATLAB & Classification Learner App.

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Conclusion

2 Lessons

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Summary of the machine learning course and future possibilities from author.

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Score 60%+ for completing the Machine Learning course & get the certificate.

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About the teacher

Anish Yadav

Artificial Intelligence Expert with experience of MATLAB & Simulink

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