Cocalc 05 Ipynb

📚 The CoCalc Library - books, templates and other resources This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book! < Feature Engineering | Contents | In Depth: Linear Regression > The previous four sections have given a general overview of the concepts of machine learning.

In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification. This notebook is part of Bite Size Bayes, an introduction to probability and Bayesian statistics using Python. License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) The following cell downloads utils.py, which contains some utility function we'll need. If everything we need is installed, the following cell should run with no error messages. In the previous notebook I presented Theorem 4, which is a way to compute the probability of a disjunction (or operation) using the probability of a conjunction (and operation).

The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book! < Further Resources | Contents | What Is Machine Learning? > In many ways, machine learning is the primary means by which data science manifests itself to the broader world. Machine learning is where these computational and algorithmic skills of data science meet the statistical thinking of data science, and the result is a collection of approaches to inference and data exploration that are...

The term "machine learning" is sometimes thrown around as if it is some kind of magic pill: apply machine learning to your data, and all your problems will be solved! As you might expect, the reality is rarely this simple. While these methods can be incredibly powerful, to be effective they must be approached with a firm grasp of the strengths and weaknesses of each method, as well as a grasp of general concepts... This chapter will dive into practical aspects of machine learning, primarily using Python's Scikit-Learn package. This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here. Nor is it meant to be a comprehensive manual for the use of the Scikit-Learn package (for this, you can refer to the resources listed in Further Machine Learning Resources).

Rather, the goals of this chapter are: This final part is an introduction to the very broad topic of machine learning, mainly via Python's Scikit-Learn package. You can think of machine learning as a class of algorithms that allow a program to detect particular patterns in a dataset, and thus "learn" from the data to draw inferences from it. This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here. Nor is it meant to be a comprehensive manual for the use of the Scikit-Learn package (for this, you can refer to the resources listed in Further Machine Learning Resources). Rather, the goals here are:

To introduce the fundamental vocabulary and concepts of machine learning To introduce the Scikit-Learn API and show some examples of its use To take a deeper dive into the details of several of the more important classical machine learning approaches, and develop an intuition into how they work and when and where they are applicable Much of this material is drawn from the Scikit-Learn tutorials and workshops I have given on several occasions at PyCon, SciPy, PyData, and other conferences. Any clarity in the following pages is likely due to the many workshop participants and co-instructors who have given me valuable feedback on this material over the years! if statement inside a if statement or if-elif or if-else are called as nested if statements.

In the above example, i iterates over the 0,1,2,3,4. Every time it takes each value and executes the algorithm inside the loop. It is also possible to iterate over a nested list illustrated below. A use case of a nested for loop in this case would be, As the name says. It is used to break out of a loop when a condition becomes true when executing the loop.

This continues the rest of the loop. Sometimes when a condition is satisfied there are chances of the loop getting terminated. This can be avoided using continue statement. CoCalc: Collaborative Calculations and Data Science This notebook comes from A Whirlwind Tour of Python by Jake VanderPlas (OReilly Media, 2016). This content is licensed CC0.

The full notebook listing is available at https://github.com/jakevdp/WhirlwindTourOfPython. < Basic Python Semantics: Operators | Contents | Built-In Data Structures > When discussing Python variables and objects, we mentioned the fact that all Python objects have type information attached. Here we'll briefly walk through the built-in simple types offered by Python. We say "simple types" to contrast with several compound types, which will be discussed in the following section. Python's simple types are summarized in the following table:

We'll take a quick look at each of these in turn. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book! < What Is Machine Learning? | Contents | Hyperparameters and Model Validation > There are several Python libraries which provide solid implementations of a range of machine learning algorithms.

One of the best known is Scikit-Learn, a package that provides efficient versions of a large number of common algorithms. Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation. A benefit of this uniformity is that once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is very straightforward. This section provides an overview of the Scikit-Learn API; a solid understanding of these API elements will form the foundation for understanding the deeper practical discussion of machine learning algorithms and approaches in the... We will start by covering data representation in Scikit-Learn, followed by covering the Estimator API, and finally go through a more interesting example of using these tools for exploring a set of images of... The techniques in the last chapter are very powerful, but they only work with one variable or dimension.

Gaussians represent a mean and variance that are scalars - real numbers. They provide no way to represent multidimensional data, such as the position of a dog in a field. You may retort that you could use two Kalman filters from the last chapter. One would track the x coordinate and the other the y coordinate. That does work, but suppose we want to track position, velocity, acceleration, and attitude. These values are related to each other, and as we learned in the g-h chapter we should never throw away information.

Through one key insight we will achieve markedly better filter performance than was possible with the equations from the last chapter. In this chapter I will introduce you to multivariate Gaussians - Gaussians for more than one variable, and the key insight I mention above. Then, in the next chapter we will use the math from this chapter to write a complete filter in just a few lines of code. In the last two chapters we used Gaussians for a scalar (one dimensional) variable, expressed as N(μ,σ2)\mathcal{N}(\mu, \sigma^2)N(μ,σ2). A more formal term for this is univariate normal, where univariate means 'one variable'. The probability distribution of the Gaussian is known as the univariate normal distribution

What might a multivariate normal distribution be? Multivariate means multiple variables. Our goal is to be able to represent a normal distribution across multiple dimensions. I don't necessarily mean spatial dimensions - it could be position, velocity, and acceleration. Consider a two dimensional case. Let's say we believe that x=2x = 2x=2 and y=17y = 17y=17.

This might be the x and y coordinates for the position of our dog, it might be the position and velocity of our dog on the x-axis, or the temperature and wind speed at... It doesn't really matter. We can see that for NNN dimensions, we need NNN means, which we will arrange in a column matrix (vector) like so: Therefore for this example we would have

People Also Search

• CoCalc -- 05.05-Naive-Bayes.ipynb
• CoCalc -- 05_test.ipynb
• CoCalc -- 5-probability.ipynb
• CoCalc -- 05.00-Machine-Learning.ipynb
• CoCalc -- 05.ipynb
• Collaborative Calculations - CoCalc
• CoCalc -- 05-Built-in-Scalar-Types.ipynb
• CoCalc -- 05.02-Introducing-Scikit-Learn.ipynb
• CoCalc -- 05-Multivariate-Gaussians.ipynb