AI News, List of Must – Read Free Data Science Books

List of Must – Read Free Data Science Books

Data science is an inter-disciplinary field which contains methods and techniques from fields like statistics, machine learning, Bayesian etc.

Author: Reza Nasiri Mahalati This compilation by Professor Sanjay emphasizes on applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems.

Author: Stephen Boyd and Lieven Vandenberghe This book provides a comprehensive introduction to the subject and shows in detail how such problems can be solved numerically with great efficiency.

Author: Sean Luke This is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts.

Author: Hal Daumé III CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).

List of Must — Read Free Data Science Books

Data science is an inter-disciplinary field which contains methods and techniques from fields like statistics, machine learning, Bayesian etc.

Author: Reza Nasiri Mahalati This compilation by Professor Sanjay emphasizes on applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems.

Author: Stephen Boyd and Lieven Vandenberghe This book provides a comprehensive introduction to the subject and shows in detail how such problems can be solved numerically with great efficiency.

Author: Sean Luke This is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts.

Author: Hal Daumé III CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).

Machine learning

Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to 'learn' (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.[1]

These analytical models allow researchers, data scientists, engineers, and analysts to 'produce reliable, repeatable decisions and results' and uncover 'hidden insights' through learning from historical relationships and trends in the data.[12]

Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: 'A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.'[13]

Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.

Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.[17]:708–710;

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge.

Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples).

The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.[19]

The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

An artificial neural network (ANN) learning algorithm, usually called 'neural network' (NN), is a learning algorithm that is vaguely inspired by biological neural networks.

They are usually used to model complex relationships between inputs and outputs, to find patterns in data, or to capture the statistical structure in an unknown joint probability distribution between observed variables.

Falling hardware prices and the development of GPUs for personal use in the last few years have contributed to the development of the concept of deep learning which consists of multiple hidden layers in an artificial neural network.

Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples.

Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.

Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to some predesignated criterion or criteria, while observations drawn from different clusters are dissimilar.

Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated for example by internal compactness (similarity between members of the same cluster) and separation between different clusters.

Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG).

Representation learning algorithms often attempt to preserve the information in their input but transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions, allowing reconstruction of the inputs coming from the unknown data generating distribution, while not being necessarily faithful for configurations that are implausible under that distribution.

Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features.

genetic algorithm (GA) is a search heuristic that mimics the process of natural selection, and uses methods such as mutation and crossover to generate new genotype in the hope of finding good solutions to a given problem.

In 2006, the online movie company Netflix held the first 'Netflix Prize' competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%.

Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data into a training and test sets (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set.

In comparison, the k-fold-cross-validation method randomly splits the data into k subsets where the k - 1 instances of the data subsets are used to train the model while the kth subset instance is used to test the predictive ability of the training model.

For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[50][51]

There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these 'greed' biases are addressed.[53]

Bayesian Reasoning and Machine Learning 1st Edition

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10 Free Must-Read Machine Learning E-Books For Data Scientists & AI Engineers

So you love reading but can’t afford to splurge too much money on books?

We begin the list by going from the basics of statistics, then machine learning foundations and finally advanced machine learning.

One of the stand-out features of this book is it covers the basics of Bayesian statistics as well, a very important branch for any aspiring data scientist.

Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani One of the most popular entries in this list, it’s an introduction to data science through machine learning. This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to this field.

Authors: Shai Shalev-Shwartz and Shai Ben-David This book gives a structured introduction to machine learning. It looks at the fundamental theories of machine learning and the mathematical derivations that transform these concepts into practical algorithms.

Following that, it covers a list of ML algorithms, including (but not limited to), stochastic gradient descent, neural networks, and structured output learning.

It takes a fun and visually entertaining look at social filtering and item-based filtering methods and how to use machine learning to implement them.

Authors: Anand Rajaraman and Jeffrey David Ullman As the era of Big Data rages on, mining data to gain actionable insights is a highly sought after skill. This book focuses on algorithms that have been previously used to solve key problems in data mining and which can be used on even the most gigantic of datasets.

It starts off by covering the history of neural networks before deep diving into the mathematics and explanation behind different types of NNs.

10 Free Must-Read Books for Machine Learning and Data Science

What better way to enjoy this spring weather than with some free machine learning and data science ebooks?

The list begins with a base of statistics, moves on to machine learning foundations, progresses to a few bigger picture titles, has a quick look at an advanced topic or 2, and ends off with something that brings it all together.

The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.

The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book.

The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

Avrim Blum, John Hopcroft, and Ravindran Kannan While traditional areas of computer science remain highly important, increasingly researchers of the future will be involved with using computers to understand and extract usable information from massive data arising in applications, not just how to make computers useful on specific well-defined problems.

With this in mind we have written this book to cover the theory likely to be useful in the next 40 years, just as an understanding of automata theory, algorithms, and related topics gave students an advantage in the last 40 years.

The textbook is laid out as a series of small steps that build on each other until, by the time you complete the book, you have laid the foundation for understanding data mining techniques.

But building a machine learning system requires that you make practical decisions: Historically, the only way to learn how to make these 'strategy' decisions has been a multi-year apprenticeship in a graduate program or company.

1. Introduction to Statistics

NOTE: This video was recorded in Fall 2017. The rest of the lectures were recorded in Fall 2016, but video of Lecture 1 was not available. MIT 18.650 Statistics ...

Data Augmentation explained

In this video, we explain the concept of data augmentation, as it pertains to machine learning and deep learning. We also point to another resource to show how ...

The Statistical Crisis in Science and How to Move Forward by Professor Andrew Gelman

Andrew Gelman, Higgins Professor of Statistics, Professor of Political Science, and Director of the Applied Statistics Center at Columbia University, delivers a ...

The Scientific Methods: Crash Course History of Science #14

Historically speaking, there is no one scientific method. There's more than one way to make knowledge. In this episode we're going to look at a few of those ...

24 - Bayesian inference in practice - posterior distribution: example Disease prevalence

This provides an introduction to how a posterior distribution can be derived from a binomial likelihood with a beta conjugate prior for the example of disease ...

Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8

Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I'll ...

What is Subjective (Bayesian) Probability?

This is a sample video from my tutorial course titled "What is Probability?". Click the link above to see the full table of ..

Null and Alternate Hypothesis - Statistical Hypothesis Testing - Statistics Course

Get the full course at: The student will learn how to write the null and alternate hypothesis as part of a hypothesis test in statistics

Factor Analysis - an introduction

This video provides an introduction to factor analysis, and explains why this technique is often used in the social sciences. Check out ...