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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...
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...
DATA SCIENCE: FOUNDATION & FUNDAMENTALS
The academic field of computer science did not develop as a separate subject of study until the 1960s after it had been in existence since the 1950s. The mathematical theory that underpinned the fields of computer programming, compilers, and operating systems was one of the primary focuses of this class. Other important topics were the various programming languages and operating systems. Context-free languages, finite automata, regular expressions, and computability were a few of the topics that were discussed in theoretical computer science lectures. The area of study known as algorithmic analysis became an essential component of theory in the 1970s, after having been mostly overlooked for ...
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) ...
THE ART OF INTELLIGENT MACHINES UNLEASHING THE POWER OF MACHINE LEARNING
Intelligent machines, also known as artificial intelligence (AI) systems, are a fascinating area of study and development that integrates computer science, mathematics, and cognitive science to create machines that can simulate human-like intellect and conduct. This field of study and development aims to produce machines that can create intelligent machines that can simulate human-like intelligence and behavior. These computers are programmed to perceive, learn, reason, and make judgments in a manner that is either comparable to or superior to the cognitive powers of humans. Machine learning is a subsection of artificial intelligence that focuses on the development of algorithms and models t...
REINFORCEMENT LEARNING FUNDAMENTALS - LEARNING THROUGH REWARDS AND PUNISHMENTS
Reinforcement learning is a subfield within the broader domain of machine learning. The crux of the matter is in selecting the optimal course of action to maximize prospective profitability within a given set of conditions. It is utilized by various software and computers to determine the optimal course of action or action route to effectively respond to a given event. In the process of supervised learning, the training data includes the ground truth, and the model is trained using the correct response. In contrast, in the context of reinforcement learning, the absence of a definitive correct answer is seen. Instead, the reinforcement agent exercises its discretion in selecting the appropria...
ARTIFICIAL INTELLIGENCE: A MODERN APPROACH
Here we try to define artificial intelligence (AI) and explain why we think it deserves more attention than other worthy research topics; obviously, this is a prerequisite to doing any kind of study in this area. We humans take great pride in our intelligence; in fact, we call ourselves Homo sapiens, which means "man the wise." Human cognition has long baffled scientists, who have sought to explain how a little particle of stuff like us can see, understand, predict, and control an enormous and complex cosmos. Beyond that, the field of artificial intelligence (AI) aims to do more than just understand; it aims to build intelligent objects. One of the newest innovations in engineering and scien...
MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING
Machine learning is an area of artificial intelligence that focuses on teaching computers how to learn without being explicitly instructed to do so. This ability allows computers to acquire knowledge and competence via experience rather than being taught to do so. In recent years, as a consequence of the many different applications it has in a broad variety of fields, it has become an increasingly important topic of debate as a result of the multiple practical uses it has. Throughout the course of this blog, we will discuss how machine learning is being utilized to address difficulties in the real world, as well as study the principles of machine learning and go into more advanced topics. Wh...
UNLEASHING THE POWER OF AI: A COMPREHENSIVE GUIDE TO ADVANTAGES AND DISADVANTAGES IN DIFFERENT INDUSTRIES
The term "artificial intelligence" (AI) refers to a category of computing technologies that have become increasingly advanced in recent years. This study presents an overview that is easily understandable of how it works, why it is important, and what we can do as a reaction to the difficulties that it poses. Since the beginning of the field of artificial intelligence, the capacity to behave in a way that gives the impression of intelligence has been the primary emphasis of the concept of AI. Several variations of the 'Turing test' determine that computers are intelligent when people are unable to distinguish between their behaviors and those of a person. The disruptive power of artificial i...
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
