Machine Learning For Dummies Cheat Sheet

Machine Learning For Dummies

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). With that in mind, this Cheat Sheet helps you access the most commonly needed reminders for making your machine learning experience fast and easy.

Locate the Algorithm You Need

Machine learning requires the use of a large number of algorithms to perform various tasks. However, finding the specific algorithm you want to know about could be difficult. The following table provides you with an online location for information about the most common algorithms.

Algorithm	Type	Python/R URL
Naïve Bayes	Supervised classification, online learning	https://scikit-learn.org/stable/modules/naive_bayes.html
PCA	Unsupervised	https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
SVD	Unsupervised	https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html
K-means	Unsupervised	https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
K-Nearest Neighbors	Supervised regression and classification	https://scikit-learn.org/stable/modules/neighbors.html
Linear Regression	Supervised regression, online learning	https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
Logistic Regression	Supervised classification, online learning	https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
Neural Networks	Unsupervised Supervised regression and classification	https://scikit-learn.org/dev/modules/neural_networks_supervised.html
Support Vector Machines	Supervised regression and classification	https://scikit-learn.org/stable/modules/svm.html
Adaboost	Supervised classification	https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
Gradient Boosting	Supervised regression and classification	https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
Random Forest	Supervised regression and classification	https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html

Choose the Right Algorithm

Machine Learning For Dummies, 2nd Edition discusses a lot of different algorithms, and it may seem at times as if it will never run out. The following table provides you with a quick summary of the strengths and weaknesses of the various algorithms.

Algorithm	Best at	Pros	Cons
Random Forest	Apt at almost any machine learning problem Bioinformatics	Can work in parallel Seldom overfits Automatically handles missing values if you impute using a special number No need to transform any variable No need to tweak parameters	Difficult to interpret Weaker on regression when estimating values at the extremities of the distribution of response values Biased in multiclass problems toward more frequent classes
Gradient Boosting	Apt at almost any machine learning problem Search engines (solving the problem of learning to rank)	It can approximate most nonlinear function Best in class predictor Automatically handles missing values No need to transform any variable	It can overfit if run for too many iterations Sensitive to noisy data and outliers Doesn’t work at its best without parameter tuning
Linear regression	* Baseline predictions * Econometric predictions * Modelling marketing responses	* Simple to understand and explain * It seldom overfits * Using L1 & L2 regularization is effective in feature selection * Fast to train * Easy to train on big data thanks to its stochastic version	* You have to work hard to make it fit nonlinear functions * Can suffer from outliers
Support Vector Machines	Character recognition Image recognition Text classification	Automatic non-linear feature creation Can approximate complex non-linear functions Works only with a portion of the examples (the support vectors)	Difficult to interpret when applying non-linear kernels Suffers from too many examples, after 10,000 examples it starts taking too long to train
K-Nearest Neighbors	Computer vision Multilabel tagging Recommender systems Spell checking problems	Fast, lazy training Can naturally handle extreme multiclass problems (like tagging text)	Slow and cumbersome in the predicting phase Can fail to predict correctly due to the curse of dimensionality
Adaboost	Face detection	Automatically handles missing values No need to transform any variable It doesn’t overfit easily Few parameters to tweak It can leverage many different weak-learners	Sensitive to noisy data and outliers Never the best in class predictions
Naive Bayes	Face recognition Sentiment analysis Spam detection Text classification	Easy and fast to implement, doesn’t require too much memory and can be used for online learning Easy to understand Takes into account prior knowledge	Strong and unrealistic feature independence assumptions Fails estimating rare occurrences Suffers from irrelevant features
Neural Networks	Image recognition Language recognition and translation Speech recognition Vision recognition	It can approximate any non-linear function Robust to outliers It can work with image, text and sound data	It requires you to define a network architecture Difficult to tune because of too many parameters and you have also to decide the architecture of the network Difficult to interpret Easy to overfit
Logistic regression	Ordering results by probability Modelling marketing responses	Simple to understand and explain It seldom overfits Using L1 & L2 regularization is effective in feature selection The best algorithm for predicting probabilities of an event Fast to train Easy to train on big data thanks to its stochastic version	You have to work hard to make it fit non-linear functions Can suffer from outliers
SVD	Recommender systems	Can restructure data in a meaningful way	Difficult to understand why data has been restructured in a certain way
PCA	Removing collinearity Reducing dimensions of the dataset	Can reduce data dimensionality	Implies strong linear assumptions (components are a weighted summations of features)
K-means	Segmentation	Fast in finding clusters Can detect outliers in multiple dimensions	Suffers from multicollinearity Clusters are spherical, can’t detect groups of other shape Unstable solutions, depends on initialization

Get the Right Package

When working with Python, you gain the benefit of not having to reinvent the wheel when it comes to algorithms. There is a package available to meet your specific needs—you just need to know which one to use. The following table provides you with a listing of common Python packages. When you want to perform any algorithm-related task, simply load the package needed for that task into your programming environment.

Adaboost: ensemble.AdaBoostClassifier and sklearn.ensemble.AdaBoostRegressor
Gradient Boosting: ensemble.GradientBoostingClassifier and sklearn.ensemble.GradientBoostingRegressor
K-means: cluster.KMeans and sklearn.cluster.MiniBatchKMeans
K-Nearest Neighbors: neighbors.KNeighborsClassifier and sklearn.neighbors.KNeighborsRegressor
Linear regression: linear_model.LinearRegression, sklearn.linear_model.Ridge, sklearn.linear_model.Lasso, sklearn.linear_model.ElasticNet, and sklearn.linear_model.SGDRegressor
Logistic regression: linear_model.LogisticRegression and sklearn.linear_model.SGDClassifier
Naive Bayes: naive_bayes.GaussianNB. sklearn.naive_bayes.MultinomialNB, and sklearn.naive_bayes.BernoulliNB
Neural Networks: keras
Principal Component Analysis (PCA): sklearn.decomposition.PCA
Random Forest: ensemble.RandomForestClassifier. sklearn.ensemble.RandomForestRegressor, sklearn.ensemble.ExtraTreesClassifier, and sklearn.ensemble.ExtraTreesRegressor
Support Vector Machines (SVMs): svm.SVC, sklearn.svm.LinearSVC, sklearn.svm.NuSVC, sklearn.svm.SVR, sklearn.svm.LinearSVR, sklearn.svm.NuSVR, and sklearn.svm.OneClassSVM
Singular Value Decomposition (SVD): decomposition.TruncatedSVD and sklearn.decomposition.NMF

Differentiating Learning Types

Algorithms are said to learn, but it’s important to know how they learn because they most definitely don’t learn in the same way that humans do. 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: 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. The supervised approach is, indeed, similar to human learning under the supervision of a teacher. The teacher provides good examples for the student to memorize, and the student then derives general rules from these specific examples.
Unsupervised learning: Occurs when an algorithm learns from plain examples without any associated response, leaving the algorithm to determine the data patterns on its own. This type of algorithm tends to restructure the data into something else, such as new data features that may represent a class or some new values helpful for additional analysis or for the training a predictive model.
Reinforcement learning: Occurs when you sequentially present the algorithm with examples that lack labels, as in unsupervised learning. However, you accompany each example with positive or negative feedback according to the solution the algorithm proposes. Reinforcement learning is connected to applications for which the algorithm must make decisions (so that the product is prescriptive, not just descriptive, as in unsupervised learning), and the decisions bear consequences.

About This Article

This article is from the book:

Machine Learning For Dummies ,

About the book authors:

John Mueller has produced 114 books and more than 600 articles on topics ranging from functional programming techniques to working with Amazon Web Services (AWS). Luca Massaron, a Google Developer Expert (GDE),??interprets big data and transforms it into smart data through simple and effective data mining and machine learning techniques.

This article can be found in the category:

Machine Learning ,

Article Categories

Book Categories

Collections