PCL Platform

Practice-Centered Learning

Master AI with our curated curriculum and integrated compute resources,
designed for focused learning

Frontier AI Curriculum

Deep dive into Deep Learning Models, Generative and Agentic AI, developed by world-class industry experts.

Integrated Compute and Practice

One-click access to cloud GPU instances. Train your models directly within the platform using our pre-configured environments.

Industry-linked Projects

Solve actual business problems provided by our corporate partners. Build a portfolio that commands attention in the AI job market.

AI Courses and Specializations

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Recommended

Mathematics

Calculus

Calculus is a foundational mathematics course that introduces students to differential and integral calculus through practical applications and computational problem-solving. The course covers limits, derivatives, integrals, and their applications to optimization, related rates, area calculations, and volume computations. Students will master fundamental techniques including differentiation rules, integration methods, the Fundamental Theorem of Calculus, and series expansions. Through hands-on implementations using computational tools like Python, students will solve real-world problems in physics, engineering, economics, and data science, including motion analysis, optimization problems, and mathematical modeling. Students will explore how calculus principles underpin machine learning algorithms, including gradient descent optimization, back propagation in neural networks, and probability distributions in AI models.

Linear Algebra

Linear Algebra is a fundamental mathematics course that introduces students to vector spaces, matrices, and linear transformations through computational applications and real-world problem-solving. The course covers vector operations, matrix algebra, determinants, eigenvalues and eigenvectors, linear independence, and basis transformations. Students will master essential techniques including Gaussian elimination, matrix factorizations, least squares methods, and dimensionality reduction algorithms. Through hands-on implementations using Python libraries like NumPy, SciPy, and scikit-learn, students will tackle practical problems in computer graphics, data analysis, signal processing, and optimization. Students will explore how linear algebra forms the mathematical foundation of artificial intelligence, including principal component analysis, singular value decomposition in recommendation systems, and matrix operations in neural network computations.

ZZhenping

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Recommended

Core Programming AI1 mins

Algorithm and Data Structure

Algorithm and Data Structure course introduces students to the fundamental structures and algorithmic techniques that power modern artificial intelligence systems. Through a practice-centered approach, students learn to analyze algorithm efficiency, implement core data structures such as arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs, and understand how these structures enable scalable and efficient AI applications. Each topic is connected to real machine learning and data processing scenarios such as batch handling, vocabulary indexing, decision trees, top-k retrieval, and graph-based recommendations. By combining conceptual foundations, hands-on coding, PCL mini-projects, and AI-driven problem solving, the course equips students with essential algorithmic thinking and prepares them for advanced AI coursework, engineering roles, and technical interviews.

RRaymond

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Practice-Centered Learning

Platform highlights

Frontier AI Curriculum

Integrated Compute and Practice

Industry-linked Projects

AI Courses and Specializations

Calculus

Linear Algebra

Algorithm and Data Structure

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