Science-Technology Books

This course covers various algorithm design techniques, emphasizing the development of efficient algorithms for complex problems.

Delve into deep learning techniques and their applications in investment. This course covers neural networks, deep reinforcement learning, and advanced deep learning models.

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This course covers advanced AI topics and concepts. Explore complex AI models and their applications in various industries, all without coding.

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Explores advanced functional programming concepts in Haskell, including type system extensions (GADTs, type families), monad transformers, concurrency, and domain-specific languages (DSLs). Students will gain hands-on experience in building robust an

Gain an overview of AI and machine learning concepts. Learn about the history, key principles, and basic algorithms of AI and ML.

This book will appeal to all those who are curious to know more about the wonders pertaining to the mind-blowing effects of our nearest star, on the natural events that occur on the Earth, on plants, animals and human beings. Read more

Learn the fundamentals of PHP, a popular server-side scripting language. Covers syntax, variables, and basic web development concepts.

Learn advanced AI techniques for risk management in investment. This course covers predictive analytics, anomaly detection, and AI-driven risk mitigation strategies.

Focuses on practical application through projects covering advanced topics like template metaprogramming, game development, GUI programming, and parallel computing. Students will gain expertise in designing efficient and scalable C++ applications for

This course covers implementation and application of data structures using Java, focusing on object-oriented principles and efficient coding.

Explore advanced trading strategies enhanced by AI. This course covers high-frequency trading, sentiment analysis, and AI-driven market predictions.

Learn how to build and train neural networks using Keras. Understand the principles of deep learning and how to implement neural network architectures.

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This course provides a comprehensive introduction to the field of machine learning. It covers key concepts, algorithms, and applications, including supervised and unsupervised learning. Students will learn how to build and evaluate machine learning m

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Develop more complex applications with Django. Covers advanced topics like authentication, deployment, and performance optimization.

This course explores highly complex data structures and algorithms, preparing students for solving intricate problems in real-world applications.

This course focuses on the essential Python programming skills needed for machine learning. Students will learn to use important libraries such as NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. The course also cover