Machine Learning For Rookies

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.

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.

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 whenx 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 to 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 THE AUTHOR

Vartul Mittal is Technology & Innovation Specialist. Vartul Mittal focuses on helping clients accelerate their digital transformation journey. He has 14+ years of Global Business Transformation experience in Management Consulting and Global In-house Centres in managing technology & business teams in Intelligent Automation, Advanced Analytics and Cloud Adoption. He is passionate about extending customer relationships beyond the current project with a longer term goal of becoming a trusted adviser and bringing greater value to businesses via digital disruption.

He has lived and worked across multiple countries and cultures involving senior client stakeholders from various industries like Financial Services Sector, FMCG and Retail. He has delivered engagements for Fortune 500 organizations such as Coca Cola India, Kotak Mahindra Bank, IBM, Royal Bank of Scotland, Standard Life Insurance, Citibank and Barclays. Vartul Mittal is also renowned speaker on Analytics, Automation, AI and Innovation among Top Global Universities and International Conferences.