Effective amazon machine learning book pdf download

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This expertise translates into the choice of algorithm powering the Amazon ML service. The Stochastic Gradient Descent SGD algorithm is the algorithm powering Amazon ML linear models and is ultimately responsible for the accuracy of the predictions generated by the service.

The SGD algorithm is one of the most robust, resilient, and optimized algorithms. It has been used in many diverse environments, from signal processing to deep learning and for a wide variety of problems, since the s with great success. The SGD has also given rise to many highly efficient variants adapted to a wide variety of data contexts.

We will come back to this important algorithm in a later chapter; suffice it to say at this point that the SGD algorithm is the Swiss army knife of all possible predictive analytics algorithm.

Overall results show that the Amazon ML performance is on a par with other MLaaS platforms, but also with scripted solutions based on popular machine learning libraries such as scikit-learn. For a given problem in a specific context and with an available dataset and a particular choice of a scoring metric, it is probably possible to code a predictive model using an adequate library and obtain better performances than the ones obtained with Amazon ML.

But what Amazon ML offers is stability, absence of coding, and a very solid benchmark record, as well as a seamless integration with the Amazon Web Services ecosystem that already powers a large portion of the Internet.

These prices do not include fees related to the data storage S3, Redshift, or RDS , which are charged separately. During the creation of your model, Amazon ML gives you a cost estimation based on the data source that has been selected. Data Science, predictive analytics, machine learning -- these terms are used in many ways and sometimes overlap each other.

What they actually refer to is not always obvious. Data science can be seen as an evolution from data mining and data analytics. Data mining is about exploring data to discover patterns that potentially lead to decisions and actions at the business level. Data science englobes data analytics and regroups a wider scope of domains, such as statistics, data visualization, predictive analytics, software engineering, and so on, under one very large umbrella.

Predictive analytics is the art of predicting future events based on past observations. It requires your data to be organized in a certain way with predictor variables and outcomes well identified. As the Danish politician Karl Kristian Steincke once said, " Making predictions is difficult especially about the future.

Predictive analytics applications are diverse and far ranging: predicting consumer behavior, natural events weather, earthquakes, and so on , people's behavior or health, financial markets, industrial applications, and so on. Predictive analytics relies on supervised learning, where data and labels are given to train the model. Machine learning comprises the tools, methods, and concepts for computers to optimize models used for predictive analytics or other goals.

Machine learning's scope is much larger than predictive analytics. Three different types of machine learning are usually considered:. The difference between supervised and unsupervised learning in the context of binary classification and clustering is illustrated in the following two figures:.

The reader will notice at this point that the book is titled Amazon Machine Learning and not Amazon Predictive Analytics. This is a bit misleading, as machine learning covers many applications and problems besides predictive analytics. However, calling the service machine learning leaves the door open for Amazon to roll out future services that are not focused on predictive analytics. The following figure maps out the relationships between data science terms:.

Predictive analytics can be very simple. We introduce a very simple example of a predictive model in the context of binary classification based on a simple threshold.

Imagine that a truck transporting small oranges and large grapefruits runs off the road; all the boxes of fruits open up, and all the fruits end up mixed together. Equipped with a simple weighing scale and a way to roll the fruits out of the truck, you want to be able to separate them automatically based on their weights. You have some information on the average weights of small oranges 96g and large grapefruits g. According to the USDA, the average weight of a medium-sized orange is grams, while a larger orange weighs approximately grams, and a smaller one around 96 grams.

You have a robust reliable, predictive model that can be applied to all your mixed up fruits to separate them. Note that in this case, you've set the threshold with an educated guess.

There was no machine learning involved. In machine learning, the models learn by themselves. Instead of setting the threshold yourself, you let your program evolve and calculate the weight separation threshold of fruits by itself. For that, you would set aside a certain number of oranges and grapefruits.

This is called the training dataset. It's important that this training dataset has roughly the same number of oranges and grapefruits. And you let the machine decide the threshold value by itself. A possible algorithm could be along these lines:.

Assuming that your training dataset is representative of all the remaining fruits and that you have enough fruits, the threshold would converge under certain conditions to the best average between all the fruit weights.

A value which you use to separate all the other fruits depending on whether they weight more or less than the threshold you estimated. The following plot shows the convergence of this crude algorithm to estimate the average weight of the fruits:. This problem is a typical binary classification model.

If we had not two but three types of fruits lemons, oranges, and grapefruit , we would have a multiclass classification problem. In this example, we only have one predictor: the weight of the fruit. We could add another predictor such as the diameter. This would result in what is called a multivariate classification problem.

Other classic prediction algorithms include Support Vector Machines, Bayes classifiers, Random forests and so on. Each algorithm has its strength and set of assumptions on the dataset.

Amazon ML does two types of predictive analytics: classification and regression. As discussed in the preceding paragraph, classification is about predicting a finite set of labels or categories for a given set of samples. In regression problems, the outcome has continuous values. Predicting age, weight, stock prices, salaries, rainfall, temperature, and so forth are all regression problems. We talk about multiple regression when there are several predictors and multivariate regression when the predictions predict several values for each sample.

Amazon ML does univariate regression and classification, both binary and multiclass, but not multilabel. Amazon ML uses a linear regression model for regression, binary, and multiclass predictions. Using the logistic regression model extends continuous regression to classification problems. Here, x is the predictor, y is the outcome, and a, b are the model parameters.

Each predicted value y is continuous and not bounded. How can we use that model to predict classes which are by definition categorical values? Take the example of binary predictions.

The method is to transform the continuous predictions that are not bounded into probabilities, which are all between 0 and 1. We then associate these probabilities to one of the two classes using a predefined threshold.

This model is called the logistic regression model—misleading name as logistic regression is a classification model and not a regression one. To transform continuous not bounded values into probabilities, we use the sigmoid function defined as follows:. This function transforms any real number into a value within the [0,1] interval. Its output can, therefore, be interpreted as a probability:. Logistic regression is, by nature, a Binary classifier. There are several strategies to transform a binary classifier into a multi class classifier.

The one versus all OvA technique consists in selecting one class as positive and all the others as negative to go back to a binary classification problem. Once the classification on the first class is carried out, a second class is selected as the positive versus all the others as negative.

This process is repeated N-1 times when there are N classes to predict. The following set of plots shows:. That available data needs to be accessible and meaningful in order for the algorithm to extract information. Let's consider a simple example. Imagine that we want to predict the market price of a house in a given city.

We can think of many variables that would be predictors of the price of a house: the number of rooms or bathrooms, the neighborhood, the surface, the heating system, and so on. These variables are called features, attributes, or predictors. The value that we want to predict is called the outcome or the target. If we want our predictions to be reliable, we need several features. Predicting the price of a house based on its surface alone would not be very efficient. Many other factors influence the price of a house and our dataset should include as many as possible with conditions.

It's often possible to add large numbers of attributes to a model to try to improve the predictions. For instance, in our housing pricing prediction, we could add all the characteristics of the house bathroom, superficies, heating system, the number of windows. Some of these variables would bring more information to our pricing model and increase the accuracy of our predictions, while others would just add noise and confuse the algorithm.

Adding new variables to a predicting model does not always improve the predictions. What You Will Learn Master key Java machine learning libraries, and what kind of problem each can solve, with theory and practical guidance. Explore powerful techniques in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining. Apply machine learning to real-world data with methodologies, processes, applications, and analysis.

Techniques and experiments developed around the latest specializations in machine learning, such as deep learning, stream data mining, and active and semi-supervised learning. Build high-performing, real-time, adaptive predictive models for batch- and stream-based big data learning using the latest tools and methodologies.

Get a deeper understanding of technologies leading towards a more powerful AI applicable in various domains such as Security, Financial Crime, Internet of Things, social networking, and so on. In Detail Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in.

If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning.

Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.

Style and approach A practical guide to help you explore machine learning—and an array of Java-based tools and frameworks—with the help of practical examples and real-world use cases. Probability is the bedrock of machine learning. Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know.

Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.

Covering recent advances in next-generation computing, the book focuses on recent developments in intelligent computing, such as linguistic computing, statistical computing, data computing and ambient applications. Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member.

Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively improve predictive modeling performance using ensemble algorithms. An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions.

Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages.

Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.

Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift including decision theoretic and Bayesian perspectives , and present algorithms for covariate shift.

Step-by-step tutorials on generative adversarial networks in python for image synthesis and image translation. Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides.

In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way.

For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks. Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data.