Machine Learning For Dummies
Overview
The most human-friendly book on machine learning
Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning—the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you'll dive into the tools, code, and math that make machine learning go—and you'll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to.
- Piece together what machine learning is, what it can do, and what it can't do
- Learn the basics of machine learning code and how it integrates with large datasets
- Understand the mathematical principles that AI uses to make itself smarter
- Consider real-world applications of machine learning and write your own algorithms
With clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.
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About The Author
Luca Massaron is a data science, machine learning, and artificial intelligence expert. He’s the author of Artificial Intelligence For Dummies, Deep Learning For Dummies, and Machine Learning For Dummies.
John Paul Mueller was a long-time tech author whose credits include previous editions of this book along with Artificial Intelligence For Dummies and Algorithms For Dummies.
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machine learning for dummies
CHEAT SHEET
Machine learning is an incredible technology that you use more often than you think today and that has the potential to do even more tomorrow. The interesting thing about machine learning is that Python makes the task easier than most people realize because it comes with a lot of built-in and extended support (through the use of libraries, datasets, and other resources).
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There are some great machine learning packages such as caret (R) and NumPy (Python). Of course, these are good, versatile packages you can use to begin your machine learning journey. It’s important to have more than a few tools in your toolbox, which is where the suggestions found here come into play.
Cloudera Oryx
Cloudera Oryx is a machine learning project for Apache Hadoop that provides you with a basis for performing machine learning tasks.
Machine Learning
Now that you’re machine learning algorithm has finished learning from the data obtained using Python or R, you’re pondering the results from your test set and wondering whether you can improve them or have really reached the best possible outcome. There are a number of checks and actions that hint at methods you can use to improve machine learning performance and achieve a more general predictor that’s able to work equally well with your test set or new data.
Machine learning comes in many different flavors, depending on the algorithm and its objectives. You can divide machine learning algorithms into three main groups based on their purpose:
Supervised learning
Unsupervised learning
Reinforcement learning
Supervised learning
Supervised learning occurs when an algorithm learns from example data and associated target responses that can consist of numeric values or string labels, such as classes or tags, in order to later predict the correct response when posed with new examples.
Machine Learning
The validation approach to machine learning is an examination of a possible remedy to in-sampling bias. In-sampling bias can happen to your data before machine learning is put into action, and it causes high variance of the following estimates. In addition, you should be aware of leakage traps that can occur when some information from the out-of-sample passes to in-sample data.
Machine Learning
It’s entirely possible to obtain a generic copy of Python and add all the required machine learning libraries to it. The process can be difficult because you need to ensure that you have all the required libraries in the correct versions to guarantee success. In addition, you need to perform the configuration required to make sure that the libraries are accessible when you need them.
Machine Learning
You need to keep your machine learning goals in mind when choosing an R distribution. R is a combination of an environment and a language. It’s a form of the S programming language, which John Chambers originally created at Bell Laboratories to make working with statistics easier. Rick Becker and Allan Wilks eventually added to the S programming language as well.
Machine Learning
Machine learning involves the use of many different algorithms. This table gives you a quick summary of the strengths and weaknesses of various algorithms.
Algorithm
Best at
Pros
Cons
Random Forest
Apt at almost any machine learning problem
Bioinformatics
Can work in parallel
Seldom overfits
Automatically han
You can apply a machine learning algorithm to a complex set of images, called the Labeled Faces in the Wild dataset that contains images of famous people collected over the Internet. You must download the dataset from the Internet, using the Scikit-learn package in Python. The package mainly contains photos of well-known politicians.
Machine Learning
As an example of how you can use an SVM to work out a complex problem for machine learning, here you find a demonstration of a handwritten recognition task and how to solve it using a nonlinear kernel, the RBF. The SVM algorithm learns from the digits dataset available from the module datasets in the Scikit-learn package.
Ideally, in machine learning you can get the best results when your features don’t completely correlate with each other and each one has some predictive power with respect to the response you’re modeling. In reality, your features often do correlate with each other, displaying a high degree of redundancy in the information available to the dataset.
After backpropagation, the next improvement in neural networks led to deep learning in machines. Research continued in spite of AI winter and neural networks started to take advantage of the developments in CPUs and GPUs (the graphic processing units better known for their application in gaming but which are actually powerful computing units for matrix and vector calculations).
Machine Learning
The driving force behind optimization in machine learning is the response from a function internal to the algorithm, called the cost function. You may see other terms used in some contexts, such as loss function, objective function, scoring function, or error function, but the cost function is an evaluation function that measures how well the machine learning algorithm maps the target function that it’s striving to guess.
Machine Learning
Machine learning on images works because it can rely on features to compare pictures and associate an image with another one (because of similarity) or to a specific label (guessing, for instance, the represented objects). People can easily choose a car or a tree when they see one in a picture. Even if it’s the first time that you see a certain kind of tree or car, you can correctly associate it with the right object (labeling) or compare it with similar objects in memory (image recall).
Machine Learning
Although machines learn from data, no magic recipe exists in the world of algorithms (as the “no free lunch” theorem states) when it comes to data. Even sophisticated and advanced learning functions hit the wall and underperform when you don’t support them with the following:
Large enough quantities of data that are suitable for the algorithm you use
Clean, well-prepared data suitable for use in machine learning
Data quantity is beneficial in learning when it explains bias and variance trade-offs.
Machine Learning
When working with R and Python for machine learning, you gain the benefit of not having to reinvent the wheel when it comes to algorithms. There is a library available to meet your specific needs — you just need to know which one to use. This table provides you with a listing of the libraries used for machine learning for both R and Python.
Machine Learning
Having many features to work with may seem to address the need for machine learning to understand a problem fully. However, just having features doesn’t solve anything; you need the right features to solve problems. Keep reading to ensure that you have the right features when performing machine learning tasks.
Machine Learning
Even if you have enough examples at hand for training both simple and complex machine learning algorithms, they must present complete values in the features, without any missing data. Having an incomplete example makes connecting all the signals within and between features impossible. Missing values also make it difficult for the algorithm to learn during training.
Machine Learning
To create great solutions, machine learning models trade off between simplicity (implying a higher bias) and complexity (generating a higher variance of estimates). If you intend to achieve the best predictive performance, you do need to find a solution in the middle by understanding what works better, which you do by using trial and error on your data.
Machine Learning
There are a number of different algorithms you can use for machine learning. However, finding the specific algorithm you want to know about can be difficult. This table provides you with the online location for information about the algorithms used in machine learning.
Algorithm
Type
Python/R URL
Naive Bayes
Supervised classification, online learning
http://scikit-learn.
Machine learning is an incredible technology that you use more often than you think today and that has the potential to do even more tomorrow. The interesting thing about machine learning is that Python makes the task easier than most people realize because it comes with a lot of built-in and extended support (through the use of libraries, datasets, and other resources).
Weka (also available at Sourceforge.net) is a collection of machine learning algorithms written in Java and developed at the University of Waikato, New Zealand. The main purpose of Weka is to perform data-mining tasks, and initially, schools used it as a learning tool. Now the tool is included as part of the Pentaho business intelligence suite where Weka is used for business intelligence.
Machine Learning
You have a few possible strategies to handle missing data effectively for machine learning. Your strategy may change if you have to handle missing values in quantitative (values expressed as numbers) or qualitative features. Qualitative features, although also expressed by numbers, are in reality referring to concepts, so their values are somewhat arbitrary and you cannot meaningfully take an average or other computations on them.
Machine Learning
Machine learning may well be the future of technology. To survive, a technology must prove useful. In fact, it must prove more than useful; it must meet perceived needs in a manner that existing technologies don’t as well as build a base of adherents who provide a monetary reason to continue investing in the technology.
Machine Learning
Sometimes the raw data you obtain from various sources won’t have the features needed to perform machine learning tasks. When this happens, you must create your own features in order to obtain the desired result. Creating a feature doesn’t mean creating data from thin air. You create new features from existing data.
Machine Learning
Facial recognition is important for machine learning. The capability to recognize a face in the crowd has become an essential tool for many professions. For example, both the military and law enforcement rely on it heavily. Of course, facial recognition has uses for security and other needs as well.This example looks at facial recognition in a more general sense.
Machine Learning
The real world of machine learning relies heavily on huge datasets. Imagine trying to wend your way through the enormous data generated just by the sales made by Amazon.com every day. The point is that you need products that help you manage these huge datasets in a manner that makes them easier to work with and faster to process.
Just as simplicity of formulations is a problem in machine learning, automatically resorting to mapping very intricate formulations doesn’t always provide a solution. In fact, you don’t know the true complexity of the required response mapping (such as whether it fits in a straight line or in a curved one). Therefore, just as simplicity may create an unsuitable response, it’s also possible to represent the complexity in data with an overly complex mapping.
Machine Learning
You can find more than a few articles that discuss the loss of jobs that machine learning and its associated technologies will cause. Robots already perform a number of tasks that used to employ humans, and this usage will increase over time. You must have also considered how those new uses could potentially cost you or a loved one a job.
Machine Learning
Being able to validate a machine learning hypothesis effectively allows further optimization of your chosen algorithm. The algorithm provides most of the predictive performance on your data, given its ability to detect signals from data and fit the true functional form of the predictive function without overfitting and generating much variance of the estimates.
Machine Learning
You can put text processing into use for machine learning with classification tasks. When you classify texts, you assign a document to a class because of the topics it discusses.
You can discover the topics in a document in different ways. The simplest approach is prompted by the idea that if a group of people talks or writes about a topic, the people tend to use words from a limited vocabulary because they refer or relate to the same topic.
Machine Learning
Sometimes, machine learning requires that you will need to resort to cross-validation. A noticeable problem with the train/test set split is that you’re actually introducing bias into your testing because you’re reducing the size of your in-sample training data. When you split your data, you may be actually keeping some useful examples out of training.
The gradient descent algorithm offers a perfect example of how machine learning works. You can provide it with an intuitive image, not just a mathematical formulation. Moreover, though it is just one of many possible methods, gradient descent is a widely used approach that’s applied to a series of machine learning algorithms such as linear models, neural networks, and gradient boosting machines.
Machine Learning
Some sites online would have you believe that statistics and machine learning are two completely different technologies. For example, when you read Statistics vs. Machine Learning, fight!, you get the idea that the two technologies are not only different, but downright hostile toward each other. The fact is that statistics and machine learning have a lot in common and that statistics represents one of the five tribes (schools of thought) that make machine learning feasible.
Machine Learning
In a perfect world, you could perform a test on data that your machine learning algorithm has never learned from before. However, waiting for fresh data isn’t always feasible in terms of time and costs.
As a first simple remedy, you can randomly split your data into training and test sets. The common split is from 25 to 30 percent for testing and the remaining 75 to 70 percent for training.
Machine Learning
Although statistics are based on the expectation that features have certain value distributions, machine learning generally doesn’t have such constraints. A machine learning algorithm doesn’t need to know beforehand the type of data distribution it will work on, but learns it directly from the data used for training.
Recognizing text is an important part of machine learning. Before a computer can do anything with text, it must be able to read the text in some manner. Categorical data is a type of short text that you represent using binary variables, that is, variables coded using one or zero values according to whether a certain value is present in the categorical variable.
Machine Learning
Machine learning will have a significant impact on e-commerce. Sentiment is difficult to catch because humans use the same words to express even opposite sentiments. The expression you convey is a matter of how you construct your thoughts in a phrase, not simply the words used. Even though dictionaries of positive and negative words do exist and are helpful, they aren’t decisive because word context matters.
Machine Learning
Humans have a terrible time visualizing abstract data, and sometimes machine learning output becomes extremely abstract. You can use a graphic output tool so that you can visualize how the data actually appears. Knime and RapidMiner excel at the task by helping you to easily produce high-quality graphics. Their use for various kinds of data mining tasks also distinguishes both of these products from other products.
Machine Learning
At first sight when approaching machine learning, image files appear as unstructured data made up of a series of bits. The file doesn’t separate the bits from each other in any way. You can’t simply look into the file and see any image structure because none exists. As with other file formats, image files rely on the user to know how to interpret the data.
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