Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data.
It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. Wikipedia
Why is machine learning important ?
Machine learning is usually applied to stock pricing prediction, marketing campaigns, scientific research, and it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products.
Many of today’s leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations.
Machine learning has become a significant competitive differentiator for many companies.
Best Machine learning free books in 2022
After you knew the importance of Machine Learning these days, we gathered you the best books to learn more about ML, and they all are free, that’s amazing right?
An Introduction to Machine Learning
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications.
The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues.
One chapter is dedicated to the popular genetic algorithms.
This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry.
The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming.
Numerous chapters have been expanded, and the presentation of the material has been enhanced.
The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
Machine Learning Yearning
AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work.
After reading Machine Learning Yearning, you will be able to:
- Prioritize the most promising directions for an AI project
- Diagnose errors in a machine learning system
- Build ML in complex settings, such as mismatched training/ test sets- Set up an ML project to compare to and/or surpass human-level performance
- Know when and how to apply end-to-end learning, transfer learning, and multi-task learning.
Machine Learning for Cyber Physical Systems
This book proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions.
It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018.
Cyber Physical Systems are characterized by their ability to adapt and to learn:
They analyze their environment and, based on observations, they learn patterns, correlations and predictive models.
Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis.
Machine Learning is the key technology for these developments.
Multivariate Statistical Machine Learning Methods for Genomic Prediction
This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists.
It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool.
To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.
The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool.
In addition, it weighs the advantages and disadvantages of each tool.
This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes.
This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches.
As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes.
Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user.
Automated Machine Learning
This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems.
The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters.
To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.
This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Interpretable Machine Learning
This book is about making machine learning models and their decisions interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression.
Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME.
All interpretation methods are explained in depth and discussed critically.
- How do they work under the hood?
- What are their strengths and weaknesses?
- How can their outputs be interpreted?
This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Python Machine Learning Projects
As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions – sometimes without final input from humans who may be impacted by these findings – it is crucial to invest in bringing more stakeholders into the fold.
This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all.
This book will set you up with a Python programming environment if you don’t have one already, then provide you with a conceptual understanding of machine learning in the chapter “An Introduction to Machine Learning.”
What follows next are three Python machine learning projects.
They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari.
Efficient Learning Machines
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models.
Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques.
Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning.
Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems.
Understanding Machine Learning
The subject of this book is automated learning, or, as we will more often call it, Machine Learning (ML).
That is, we wish to program computers so that they can “learn” from input available to them.
Roughly speaking, learning is the process of converting experience into expertise or knowledge.
The input to a learning algorithm is training data, representing experience, and the output is some expertise, which usually takes the form of another computer program that can perform some task.
Seeking a formal-mathematical understanding of this concept, we’ll have to be more explicit about what we mean by each of the involved terms:
- What is the training data our programs will access?
- How can the process of learning be automated?
- How can we evaluate the success of such a process (namely, the quality of the output of a learning program)?
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