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The two main approaches will be presented: Supervised Learning, which uses labeled data to build predictive models with algorithms such as Regression, Decision Trees, and Neural Networks, and Unsupervised Learning, which explores hidden patterns in unlabeled data through techniques like clustering, association rules, and dimensionality reduction.\u003c\/p\u003e\n\n\u003cp\u003eThe module emphasizes practical applications that impact the real world, including Natural Language Processing (NLP) for virtual assistants and chatbots, AI in video games to create intelligent opponents and personalized worlds, and the convergence of robotics and data mining for autonomous manufacturing and Big Data analysis. Students will understand the data quality and interpretation challenges in each approach, highlighting the importance of validation and contextual knowledge.\u003c\/p\u003e\n\n\u003cp\u003eFinally, future trends and challenges will be discussed, such as hybrid techniques combining supervised and unsupervised methods, reinforcement learning, Explainable AI for transparent decision-making, and Generative AI, which requires human oversight and rigorous regulation to mitigate algorithmic biases. The module reinforces that ethical responsibility is just as essential as technical competence to ensure AI delivers real and social benefits.\u003c\/p\u003e","brand":"EstudeLivre","offers":[{"title":"Default Title","offer_id":43018546774093,"sku":"CONCEITOS-APRENDIZADO-MAQUINA-SUPERVISIONADO","price":30.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/0466\/6701\/files\/machine-learning-com-pessoas-3-2.png?v=1779364116"},{"product_id":"search-methods-for-problem-solving-blind-heuristic-and-competitive-search","title":"Search Methods for Problem Solving: Blind, Heuristic, and Competitive Search","description":"\u003cp\u003eIn this course, students will grasp the fundamentals of problem-solving in Artificial Intelligence, learning to define initial state, goal, possible actions, and associated costs. 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The module emphasizes that no method is universally superior and points to future trends, such as automatic heuristics via Machine Learning and hybrid systems for Big Data and advanced robotics.\u003c\/p\u003e","brand":"EstudeLivre","offers":[{"title":"Default Title","offer_id":43018546806861,"sku":"METODOS-BUSCA-RESOLUCAO-PROBLEMAS-IA","price":30.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/0466\/6701\/files\/m-todos-de-busca-ia.png?v=1779364119"},{"product_id":"history-and-foundations-of-artificial-intelligence-ai","title":"History and Foundations of Artificial Intelligence (AI)","description":"\u003cp\u003eIn this course, students will understand the historical evolution of AI, from ancient myths about thinking machines to the formal emergence of the discipline at the Dartmouth Conference (1956). Early advances are covered, such as the Perceptron, the Lisp language, and the ELIZA chatbot, as well as the periods known as the \"AI Winters,\" marked by technological limitations and funding cuts, and the rebirth of AI with expert systems.\u003c\/p\u003e\n\n\u003cp\u003eThe module also explores the Deep Learning era and the integration of AI into everyday life, including virtual assistants, recommendation algorithms, AlphaGo, and Generative AI (ChatGPT, Midjourney, and DALL-E). Students will study the emerging ethical debates around algorithmic biases, misinformation, privacy, and impacts on the job market, understanding the need for social responsibility and appropriate regulation.\u003c\/p\u003e\n\n\u003cp\u003eFinally, the technical foundations of AI are presented, including pattern recognition, Machine Learning, Deep Learning, and the distinction between Narrow AI, General AI (AGI), and Superintelligence. 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The importance of diversity initiatives and public policies to expand digital and professional inclusion will be discussed.\u003c\/p\u003e\n\n\u003cp\u003eThe module also addresses algorithmic racism, evidenced by biases in AI systems, content moderation, and facial recognition, including the case of Joy Buolamwini. Intersectional impacts on Black women and vulnerable communities are analyzed, highlighting the need for racial audits, algorithmic transparency, and rigorous application of Brazil's LGPD (General Data Protection Law) to safeguard rights and reduce discrimination.\u003c\/p\u003e\n\n\u003cp\u003eFinally, students will study public policies and affirmative action, such as laws on teaching Afro-Brazilian history, quotas in college programs and acceleration initiatives, and the coordination among civil society, companies, and governments. 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Structural challenges are discussed, such as the talent shortage and the need for continuous training and reskilling, preparing students to act strategically and competitively in the digital age.\u003c\/p\u003e","brand":"EstudeLivre","offers":[{"title":"Default Title","offer_id":43018546970701,"sku":"PRIMORDIOS-INFORMATICA-COMPUTADOR-ELETRONICO","price":30.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/0466\/6701\/files\/os-prim-rdios-da-inform-tica.png?v=1779364127"},{"product_id":"data-science-and-business-strategy-turning-data-into-strategic-decisions","title":"Data Science and Business Strategy: Turning Data into Strategic Decisions","description":"\u003cp\u003eIn this course, students will understand the fundamentals of Data Science, including the triad of statistics, programming\/technology, and business knowledge. 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The module highlights Data Storytelling, democratizing insights, integrating generative AI, and emerging trends, reinforcing that leadership must place data culture at the center of strategy to generate sustainable competitive advantage.\u003c\/p\u003e","brand":"EstudeLivre","offers":[{"title":"Default Title","offer_id":43018547003469,"sku":"CIENCIA-DADOS-ESTRATEGIA-NEGOCIOS","price":30.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/0466\/6701\/files\/ci-ncia-de-dados-e-estrat-gia-de-neg-cios.png?v=1779364129"},{"product_id":"introduction-to-analytical-data-thinking","title":"Introduction to Analytical Data Thinking","description":"\u003cp\u003eIn this course, students will understand analytical thinking as a critical and structured approach for turning raw data into actionable insights. 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Discrete probability distributions such as Binomial and Poisson, and continuous distributions, will be explored, highlighting the Normal, Uniform, and Exponential distributions, essential for modeling variable behavior and predicting outcomes.\u003c\/p\u003e\n\n\u003cp\u003eThe module also covers fundamental statistical measures, such as expected value, variance, and standard deviation, which summarize the behavior of distributions and aid in interpreting data. Statistical inference is introduced through the Central Limit Theorem, confidence intervals, and hypothesis tests, allowing information to be extrapolated from samples to entire populations with analytical rigor and uncertainty control.\u003c\/p\u003e\n\n\u003cp\u003eFinally, students will study practical applications of probability and statistics in real-world contexts, including industrial quality control, consumer behavior research, finance, and public health. 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