Science-Technology Books

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

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

Explore the fundamentals of machine learning using Scikit-Learn. Learn how to build, train, and evaluate basic ML models.

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

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

Master deep learning concepts using PyTorch. Learn about advanced neural network architectures, model optimization, and deployment techniques.

Covers advanced Haskell topics such as software transactional memory (STM), parallel and concurrent programming with Haskell, and advanced type system features. Students will gain proficiency in building high-performance and reliable software systems

Explore Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks using TensorFlow and PyTorch. Learn their applications in sequence modelling.

Learn the basics of Ruby programming language. Covers syntax, object-oriented programming concepts, and basic scripting.

This course introduces fundamental algorithms and their applications, providing a strong foundation for problem-solving and algorithmic thinking.

Master the techniques for deploying AI models in production environments. Learn about model serving, monitoring, and maintenance.

This course provides an overview of how AI is transforming the investment industry. Students will learn the basic concepts of AI, its applications in investment, and the potential benefits and challenges.

Learn the fundamentals of machine learning and how it can be applied to investment strategies. This course covers basic machine learning concepts, techniques, and tools.

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Learn the basics of version control using Git and GitHub. Covers essential commands, branching, merging, and collaboration techniques.

This course covers basic data structures using Python, emphasizing practical implementation and application.

This course provides an introduction to algorithmic trading and its applications. Students will learn the basics of trading algorithms, strategies, and tools.

Dive deeper into advanced machine learning techniques and their applications in investment. This course covers complex models, feature engineering, and model optimization.

Build on your Python knowledge with intermediate concepts and techniques. Covers modules, file handling, and object-oriented programming.

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