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## Complete MATLAB Course: Beginner to Advanced! If you would like to get started using MATLAB, you are going to LOVE this 4+ hour video course! Go from beginner to advanced with tutorials covering basic MATLAB programming, loading and saving data, data visualization, signal & image processing and data science applications.

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• What is Matlab, how to download Matlab, and where to find help
• Introduction to the Matlab basic syntax, command window, and working directory
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• Solving linear equations using Matlab
• For loops, while loops, and if statements
• Exploring different types of data
• Plotting data using the Fibonacci Sequence
• Plots useful for data analysis
• How to load and save data
• Subplots, 3D plots, and labeling plots
• Sound is a wave of air particles
• Reversing a signal
• The Fourier transform lets you view the frequency components of a signal
• Fourier transform of a sine wave
• Applying a low-pass filter to an audio stream
• To store images in a computer you must sample the resolution
• Basic image manipulation including how to flip images
• Convolution allows you to blur an image
• A Gaussian filter allows you reduce image noise and detail
• Blur and edge detection using the Gaussian filter
• Introduction to Matlab & probability
• Measuring probability
• Generating random values
• Continuous variables
• Mean and variance
• Gaussian (normal) distribution
• Test for normality
• 2 sample tests
• Multivariate Gaussian

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## Data Preprocessing for Machine Learning Using MATLAB + FREE Udemy Coupon! https://www.udemy.com/data-preprocessing-for-machine-learning-using-matlab/

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• Introduction to the course
• Introduction to MATLAB
• Importing a data-set into MATLAB
• Deletion strategies
• Using mean and mode
• Considering as a special value
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• Random value imputation

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## Machine Learning for Data Science Using MATLAB Get The Complete MATLAB Course Bundle for 1 on 1 help!

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This course is for you if you want to have a real feel of the Machine Learning techniques without having to learn all of the complicated math. Additionally, this course is also for you if you have had previous hours of machine learning theory but could never figure out how to implement and solve data science problems with it.

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Below is a complete list of topics covered in the video. You will find the timestamps on YouTube.

• Introduction
• Intro to MATLAB
• Intro to Data Preprocessing
• Importing Data into MATLAB
• Handling Missing Data Part 1
• Handling Missing Data Part 2
• Feature Scaling
• Outliers Part 1
• Outliers Part 2
• Dealing with Categorical Data Part 1
• Dealing with Categorical Data Part 2
• Data Preprocessing Template
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## Learn MATLAB Episode #31: Multivariate Gaussian Posted on

## Learn MATLAB Episode #30: 2 Sample Tests In this lecture we’re going to continue our talk about hypothesis testing of Gaussian distributed data. So to elaborate on how hypothesis tests work we’re going to talk about the hypotheses a little bit more. So there are two hypotheses when we’re doing a statistical test. There’s a null hypothesis which we call h-naught, and it usually represents the control data or random data that results purely from chance. The second hypothesis is the alternative hypothesis. So this is the thing we’re trying to prove, generally. For example, if we’re doing an experiment where we’re testing a drug, the drug actually working would be the alternative hypothesis. And so the alternative hypothesis is the hypothesis that sample observations are influenced by some non-random cause. So now suppose we have some random data, so let’s say R1 equals randn. Let’s say it as a hundred points, a mean of zero, and a variance of one. Now let’s say we have a another data set which is maybe 20 points, but this one’s going to have a different mean. So let’s say it’s mean is one, and let’s increase its variance a little bit. Okay, so, we have two distributions that are Gaussian distributed. The first one has 0 mean and one variance, and the second one has a mean of one but a standard deviation of two. So, how do we compare these two distributions? There is a test called the ttest, or the two-sided t-test, or the two-sample t-test which does what we want, and it again returns a hypothesis and a p-value. So we can try this see ttest2(R1,R2). Ok, so, we reject the null hypothesis in this case with a very, very small p-value. Remember it only has to be less than five percent for us to reject the null hypothesis. So let’s try some things. Let’s have less data points for R1. Okay, and let’s do our ttest again. So notice how we still reject the null hypothesis but our p value has increased, so it’s less significant than before. Alright, so, now let’s do the same thing for R2, let’s say this now only has 10 points. Let’s do the ttest again. Alright, so, this is still significant. Let’s increase the variance. Alright, so, I had to increase the variance a lot to get an insignificant p value, so that’s one thing about when you’re comparing two gaussian distributions you can’t really say one is bigger than the other if they’re spread out a lot. So let’s put the variance back for R2, but let’s say the mean is now less far away from R1’s mean. Let’s do our ttest again, and so this also gives us an insignificant p value. So that’s another fact about the ttest is you also can’t tell if two distributions are different if they are very close together. If they’re very far apart, so the mean is, let’s say we do the ttest again, we now get a very small p-value.

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## Learn MATLAB Episode #29: Hypothesis Testing Posted on

## Learn MATLAB Episode #28: Gaussian (Normal) Distribution Posted on

## Learn MATLAB Episode #27: Mean and Variance Posted on

## Learn MATLAB Episode #26: Continuous Variables In this lecture we’re going to talk about continuous variables, so we’ve talked about discrete variables up until now. Discrete variables can only take on distinct values, but continuous variables can take on any value. So with continuous variables we don’t have a notion of probabilities for exact values because X can take on an infinite number of values, so the probability of equaling any specific exact value is zero. We can have probabilities for ranges though. So, for example, we can say the probability of X being between 3.13 and 3.15 is greater than zero. We have a useful function called the cumulative distribution function, or the CDF, that helps us measure such probabilities. We usually label this function as big F of X, and so the definition of F(X) is it’s the probability that the random variable big X is greater than negative infinity, but less than little x. Note that the probability of big X being between negative infinity and positive infinity is 1 since X has to take on a value, therefore the value of big F of positive infinity is equal to 1. Now how about going back to our original problem if we want to calculate the probability that X is between 3.13 and 3.15. That would just be big F of 3.15 minus big F of 3.13. So now let’s talk about the other useful function when we’re talking about continuous variables. This one’s called the probability density function, or the PDF. We usually denote it by little f of X, and it is defined as the derivative of big F of X with respect to X, so it’s like the slope of big F of X. Note that this function can be greater than one since it’s not a probability, it is a probability density f of X, little f of X does have to be greater than or equal to 0 though. So here’s one example where little f of X can be bigger than one. So, let’s say little f of X is uniform between zero and 0.1, so that means if you try to sample from this random variable X you’ll always get a value between zero and 0.1, and the probability of any particular value is equal to all the others. Now I’m going to claim that little f of x has to equal 10 if X is between 0 and 0.1, and 0 otherwise. Now why is this, because big F of X. Since little f of X is the derivative of big F of X, big F of X is the integral of little f of X. In the integral we can take the constant out and then calculate the integral from 0 to X. Now we know that from above big F of infinity has to equal 1, so the integral from minus infinity to infinity equals to 1, but since little f of x is 0 after 0.1. we can just take the integral from 0 to 0.1. That gives us 0.1c, and if we solve for c, c equals 10. Therefore, we’ve seen a scenario where little f of X can have a value greater than one because it’s a probability density, and not a probability value. Later on in this course we’ll look at more complex continuous distributions.

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## Learn MATLAB Episode #25: Birthday Paradox 