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DATA SCIENCE ETHICS AND RESPONSIBLE AIEITHICAL CONSIDERATIONS IN DATA SCIENCE AND AI
It is possible to assert that we have now fully entered the era of artificial intelligence (AI), given the current state of technology and society. Whether or not we are aware of its presence, a growing proportion of the activities that comprise our day-to-day lives include the application of technology that makes use of artificial intelligence. AI can be found in our "smart" phones and TVs, personalized recommendations for products in online shops or other businesses use AI techniques, natural language processing can be used for automatic translation, voice recognition, and generation, and the intelligent assistants of Google, Amazon, Apple, and Microsoft (such as Alexa, Siri, and Cortana) ...
DATA STRUCTURES FOR MODERN APPLICATIONS
The book contains the following chapters: Chapter 1: Introduction Chapter 2: Data Structures And Algorithms Chapter 3: Data Structures And Its Applications In C Chapter 4: Computational Geometry Problems Chapter 5: Multidimensional Spatial Data Structures Chapter 6: Binary Space Partitioning Trees
REINFORCEMENT LEARNING: FOUNDATIONS, ALGORITHMS AND APPLICATIONS
Reinforcement learning, sometimes known as RL, is a catchall word that refers to both a learning problem and a subfield in machine learning. In the context of a problem involving learning, this refers to the process of determining how to guide a computer toward an arbitrary numerical objective. The process of reinforcement learning may be seen in its usual application in the controller is provided with both the present state of the system under their control as well as the reward earned from the most recent transition. After that, the system will calculate an answer and then provide it to you. Because of this, the system goes through a state transition, and the process starts all over again....
MACHINE LEARNING EXPLAINED: A PRACTICAL GUIDE TO DATA-DRIVEN DECISION MAKING
During the course of the process of making a choice, we rely on a variety of presumptions, premises, and the circumstances; all of this is directed by the goal that is related with the decision itself. However, the premises and the knowledge of the corporation are dependent on our data since they are an essential component of our organization as a system. The context and the assumptions are both external factors that are beyond the control of any decision maker. Both the background and the assumptions represent outside forces that are not within the control of any decision maker. A prominent example of a conceptual error is the misunderstanding that exists between data and information, which...
PRACTICAL MACHINE LEARNING APPLICATIONS
It is not feasible to arrive at an accurate estimate of the total quantity of knowledge that has been accumulated as a direct consequence of man's activity. Every single day, millions of new tuples are added to the databases, and each of those tuples represents an observation, an experience that can be learned from it, and a situation that may occur again in the future in a way that is comparable to the one it happened in when it was first observed. As human beings, we have the innate capacity to gain knowledge from our experiences, and this is something that occurs constantly throughout our lives. Nevertheless, what does place when the number of occurrences to which we are exposed is more t...
DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION
Deep learning has developed as a useful approach for data mining tasks such as unsupervised feature learning and representation. This is thanks to its ability to learn from examples with no prior guidance. Unsupervised learning is the process of discovering patterns and structures in unlabeled data without the use of any explicit labels or annotations. This type of learning does not require the data to be annotated or labelled. This is especially helpful in situations in which labelled data are few or nonexistent. Unsupervised feature learning and representation have seen widespread application of deep learning methods such as auto encoders and generative adversarial networks (GANs). These a...
KEY PERSONALITY TRAITS FOR SUCCESSFUL LEADERSHIP
There is a segment of the workday that is dedicated to employees discussing their supervisor. It is possible for these to range from positive statements, such as "She will allow me to participate in that executive program and then I will have the opportunity to apply for the job in Hong Kong," to pessimistic statements, such as "You won't believe what he did this time!"—and with a perplexed expression—"He spent a considerable amount of time on the phone once more." These brief views into the working world may seem to be nothing more than the froth that floats on the surface of corporate life; nonetheless, they may really indicate a great deal about the potential for success that groups a...
MACHINE LEARNING MASTERY: ALGORITHMS, APPLICATIONS AND INSIGHTS
Machine learning is an area of artificial intelligence (AI) that focuses on the development of algorithms and models that allow computers to learn and make predictions or judgments without being explicitly programmed. This is accomplished by teaching the computer to learn from its own experiences. The creation and development of computer systems that are able to automatically analyze and understand complicated data in order to enhance their performance over time is the focus of this field. The foundation of machine learning is the construction of mathematical models that are capable of gaining knowledge from data. These models are educated using a collection of instances that have been label...
FOUNDATION OF DATA SCIENCE
The 1960s saw the beginning of computer science as an academic field of study. The programming languages, compilers, and operating systems, as well as the mathematical theory that underpinned these fields, were the primary focuses of this course. Finite automata, regular expressions, context-free languages, and computability were some of the topics that were addressed in theoretical computer science courses. In the 1970s, the study of algorithms became an essential component of theory when it had previously been neglected. The goal was to find practical applications for computers. At this time, a significant shift is taking place, and more attention is being paid to the diverse range of appl...
