Free download скачать Digital Brains The Rise of Artificial Intelligence
Published 8/2024
Created by EDUCBA Bridging the Gap
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 18 Lectures ( 2h 11m ) | Size: 1.4 GB

Unlock the power of Artificial Intelligence with our course, designed to transform beginners into AI-savvy professionals
What you'll learn:
AI Fundamentals: Understand the definition, history, and evolution of Artificial Intelligence, including key milestones and current advancements.
AI Importance and Applications: Explore the significance of AI in various sectors such as healthcare, finance, and transportation, and how AI is transforming
Ethics and Societal Impact: Examine the ethical considerations surrounding AI, including issues related to privacy, bias, and the societal impacts of AI tech
Logic and Reasoning: Learn the principles of logical reasoning and how they are applied in AI to make informed decisions and inferences.
Probability and Statistics: Gain knowledge in probability and statistical methods crucial for understanding and building AI models, including data analysis
Search Algorithms: Study search algorithms that are fundamental to AI problem-solving, such as depth-first search, breadth-first search, and A* search.
Knowledge Representation: Understand how knowledge is represented in AI systems, including methods like semantic networks, ontologies, and frames.
Machine Learning Basics: Learn the core concepts of machine learning, including supervised and unsupervised learning, and their applications in real-world
Reinforcement Learning: Explore reinforcement learning techniques, including types and algorithms, and understand how they are used to train models.
Practical Implementation: Apply theoretical knowledge to practical tasks such as developing AI models, implementing machine learning algorithms.
Requirements:
Basic Programming Knowledge: Familiarity with programming concepts and at least one programming language, such as Python or Java, is essential for implementing AI algorithms and models.
Mathematics Proficiency: A strong foundation in mathematics, particularly in areas like algebra, calculus, probability, and statistics, is necessary for understanding and applying AI techniques.
Understanding of Computer Science Fundamentals: Knowledge of fundamental computer science concepts such as data structures, algorithms, and basic principles of computer systems is beneficial.
Interest in Artificial Intelligence: A genuine interest in learning about AI technologies and their applications will enhance the learning experience and engagement in the course.
Problem-Solving Skills: Ability to approach and solve problems analytically, as AI often involves tackling complex challenges and developing solutions.
Familiarity with Data Analysis: Basic understanding of data analysis and manipulation, as working with data is a critical aspect of AI development.
Basic Knowledge of Machine Learning Concepts: While not mandatory, some introductory knowledge of machine learning concepts can be helpful for understanding more advanced topics covered in the course.
Willingness to Engage in Hands-On Projects: The course includes practical assignments and projects, so a willingness to actively participate and apply theoretical knowledge is important.
Description:
IntroductionThis course offers a detailed exploration of Artificial Intelligence (AI), ideal for those who are new to the field or seeking to enhance their foundational knowledge. Through a structured approach, students will gain insight into AI's history, applications, and ethical considerations, laying a strong groundwork for further study or practical application.Section 1: Introduction to Artificial IntelligenceIn this initial section, students will embark on a journey to understand what AI is and how it has evolved over time. The first lecture introduces the fundamental definition of AI and provides a historical overview, tracing its development from early concepts to contemporary advancements. The importance of AI is highlighted through its diverse applications, demonstrating its transformative impact across various sectors, from healthcare to finance. The section concludes with a critical examination of AI ethics and societal impacts, addressing concerns such as bias, privacy, and the future implications of AI technologies. This segment ensures that students not only grasp the technical aspects of AI but also appreciate its broader context and ethical considerations.Section 2: Foundations of Artificial IntelligenceThis section delves into the core principles that form the basis of AI. It starts with an introduction to essential concepts such as logic and reasoning, which are crucial for developing AI systems capable of making informed decisions. Students will then explore probability and statistics, foundational elements that enable AI models to handle uncertainty and make predictions. The course proceeds with an in-depth look at search algorithms, which are pivotal for problem-solving in AI. Knowledge representation and reasoning are also covered, focusing on how information is structured and utilized within AI systems to simulate human-like understanding. This section equips students with the theoretical and practical knowledge needed to comprehend and build AI systems.Section 3: Machine Learning in Artificial IntelligenceFocusing on machine learning, this section introduces students to one of the most dynamic and rapidly evolving areas of AI. The journey begins with an overview of machine learning, explaining its role and significance within the broader AI landscape. Students will then delve into supervised learning, where they learn how models are trained on labeled data to make predictions. Unsupervised learning follows, focusing on techniques that uncover hidden patterns in unlabeled data. The section also covers clustering methods and distance measures, essential for grouping data points and analyzing similarities. Dimensionality reduction techniques are explored to simplify complex datasets, while association rule learning provides insights into relationships between variables. The section concludes with reinforcement learning, discussing its types and applications, where agents learn to make decisions through trial and error. This comprehensive exploration of machine learning equips students with the skills to implement and experiment with various learning techniques in AI.ConclusionBy the end of the course, students will have a well-rounded understanding of AI, encompassing its historical development, core principles, and machine learning techniques. They will be able to apply this knowledge to analyze AI systems critically, understand their applications, and appreciate the ethical considerations involved. This foundation prepares students for advanced studies or practical application in AI projects.
Who this course is for:
Aspiring Data Scientists: Individuals looking to build a solid foundation in AI and machine learning to advance their careers in data science.
Software Developers: Professionals who want to integrate AI technologies into their software development projects and enhance their technical skill set.
Students in STEM Fields: Undergraduates or graduates in science, technology, engineering, or mathematics who are interested in exploring AI and its applications.
Technology Enthusiasts: People with a strong interest in emerging technologies and AI who want to understand how these technologies work and their impact on various industries.
Career Changers: Professionals from other fields who are considering a transition into the tech industry and want to gain expertise in AI.
Researchers: Individuals involved in academic or industrial research who need to understand AI techniques for their projects.
Business Analysts: Professionals who wish to leverage AI to gain insights from data and make informed business decisions.
Entrepreneurs: Those interested in starting a tech business or developing AI-driven products and services.
AI Enthusiasts: Individuals who are curious about AI and want a structured learning path to deepen their understanding of the subject.
Continuous Learners: Anyone committed to lifelong learning and staying current with advancements in technology and artificial intelligence.
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