Graduated with a Honors in Computer Science. Course: System Design, Advanced Algorithms, E-Commerce Systems, Computer Architechture, Data Mining, Machine Learning

Explores advanced methods for organizing and manipulating data, including trees, heaps, hash tables, and graphs, with a focus on algorithmic efficiency and real-world applications.

Covers the design and analysis of algorithms, including complexity, correctness, and optimization, with emphasis on sorting, searching, and graph algorithms.

Introduces cryptographic algorithms and protocols, including encryption, authentication, and public-key systems, with applications in secure communications.

Examines the architecture and technologies behind large-scale data storage and processing, including distributed systems, data mining, and analytics frameworks.

Focuses on the design and implementation of e-commerce platforms, covering web technologies, security, and transaction processing.

Provides foundational business knowledge for aspiring tech entrepreneurs, emphasizing business models, market analysis, and startup strategies.

Develops problem-solving skills using computational methods, including algorithmic thinking, abstraction, and decomposition.

Studies the principles of computer networking, including protocols, architectures, and networked application development.

Explores the structure and function of computer systems, including processors, memory, input/output, and assembly language.

Introduces techniques for extracting patterns and knowledge from large datasets using statistical and machine learning methods.

Covers fundamental data structures such as arrays, lists, stacks, queues, and trees, with applications in algorithm development.

Examines the principles of database design, implementation, and management, including SQL, normalization, and transaction processing.

Introduces discrete mathematics topics essential for computer science, including logic, sets, relations, functions, and combinatorics.

Covers the fundamentals of statics and dynamics, including force analysis, equilibrium, and the behavior of structures and materials.

Introduces statistical methods for engineers, including probability, hypothesis testing, regression, and quality control.

Provides an overview of security principles, threats, and countermeasures in computer systems and networks.

Discusses ethical, legal, and professional issues in computing, including intellectual property, privacy, and workplace conduct.

Focuses on software engineering principles, including design patterns, architecture, and the software development lifecycle.

Introduces essential tools for software development, such as version control, debugging, and build automation.

Covers fundamental concepts and principles of operating systems, including process management, memory management, file systems, and concurrency.

Continues foundational business knowledge for aspiring tech entrepreneurs, focusing on advanced strategies, funding, and growth.

Introduces basic concepts of probability, including random variables, probability distributions, and statistical inference.

Explores techniques for machine learning and pattern recognition, including supervised and unsupervised learning, neural networks, and deep learning.

First part of a series focusing on foundational engineering concepts and principles for holistic development.

Second part of the Renaissance Engineer series, emphasizing engineering design methodologies and principles.

Covers fundamental principles of chemistry and their application to materials science, including properties and behavior of materials.

Explores advanced calculus concepts, including multivariate functions, vector fields, and their applications in engineering and science.

Covers principles of electricity, magnetism, and optics relevant to engineering applications.

Introduces fundamental principles of microeconomics, including supply and demand, market structures, and consumer behavior.

Explores fundamental principles of macroeconomics, including national income, inflation, unemployment, and government policy.

Provides an overview of current research areas and emerging trends in computer science.

First part of an introductory series to computer science, covering basic programming concepts and problem-solving.

Second part of an introductory series to computer science, building upon basic programming and introducing more complex data structures and algorithms.

Studies the rates and mechanisms of chemical reactions, including factors influencing reaction speed and energy changes.

Introduces fundamental concepts of differential and integral calculus with applications in various fields.

Continues the study of integral calculus, including techniques of integration, sequences, and series, with practical applications.

Covers concepts of linear algebra, including vectors, matrices, systems of linear equations, and eigenvalues, with applications in science and engineering.

Focuses on building retrieval-augmented generation (RAG) systems using LangChain, covering document loaders, embeddings, vector stores, retrievers, and conversational interfaces for querying custom datasets.

Explores how to enhance LLM capabilities with function calling, external tools, and autonomous agents, enabling advanced workflows such as reasoning, planning, and tool-augmented AI behavior.

Covers the core concepts of building LLM-powered applications with LangChain, including prompt templates, chains, memory, and integrations for creating reliable and scalable AI systems.

How to adapt large language models (LLMs) to your specific needs by training them on your own data

Covers the design and implementation of systems where multiple AI agents collaborate, communicate, and solve complex tasks, preparing you for advanced applications of AI agents in real-world scenarios.

Teaches best practices for working with large language models like ChatGPT, including prompt design, understanding model capabilities, and building robust applications that leverage LLMs effectively for various tasks.

Provides a comprehensive introduction to supervised machine learning, focusing on regression and classification techniques, regularization, and the application of large language models and AI to real-world data problems.

An intensive program focused on deep learning fundamentals, neural networks, and practical AI skills using PyTorch, designed to accelerate careers in machine learning and artificial intelligence.