Our Courses
Learn vectors, matrices, transformations: the mathematical language of AI. Understand what happens inside neural networks geometrically. Learn just enough to start building. Derivatives, gradients, optimization: understand how models learn. Backpropagation from scratch. Focus on computational efficiency and resource-constrained optimization. Extends calculus to multiple variables: partial derivatives, gradients, Jacobians, constrained optimization. Understand optimization of high-dimensional loss surfaces, and parameter tuning. Build the physical intuition via mechanics, electricity, magnetism & atomic physics, needed to reason about sensors, hardware, energy, and real-world systems that AI ultimately interacts with. Understand distributions, hypothesis testing, Bayesian reasoning, rigorous uncertainty quantification, risk modeling and confidence intervals for high-stakes decisions. Understand mathematical modeling of robotic motion and perception. Connect physics (mechanics, control systems, sensors, actuators, signal processing and feedback loops) directly to robots. Understand Brownian motion, Ito's lemma, Black-Scholes, option pricing. Stochastic differential equations for modeling financial systems. Focus on practical inference techniques for complex real-world datasets via computational Bayesian methods including MCMC, probabilistic modeling, and uncertainty estimation. The course covers modeling applications from physics, engineering, biology, business. Understand higher dimensional analysis, scaling, analytic and computational tools. Learn probabilistic models like Markov chains, Poisson processes, and Brownian motion for modeling randomness in finance, engineering, and natural systems. Statistical modeling using linear and generalized regression, hypothesis testing, and experimental design to extract insights and make predictions. Cover advanced modeling techniques including queuing theory, financial derivatives, and interpreting mathematical solutions for real-world decision making. Learn modern data-driven methods like regression, classification, kernel methods, and SVMs, with mathematical behind-the-scenes, to extract patterns and make predictions from large datasets.Linear Algebra for Machine Learning
Calculus for Machine Learning
Multivariate Calculus for ML
General Physics
Probability & Statisical Inference
Physics of Robots
Stochastic Calculus & Financial Mathematics
Bayesian Computational Statistics
Mathematical Modeling I
Introduction to Stochastic Processes
Regression
Mathematical Modelling II
Statistical Learning
Learn to break down problems algorithmically and implement solutions in Python. Focus on writing clean, maintainable code for data processing and simple ML workflows. Emphasis on shipping working code quickly. Build full-stack web apps that can serve ML models using modern frameworks. Next.js/React, FastAPI, Vercel/Railway deployment. Ship to production in hours, not weeks. Understand the common concepts of arrays, trees, graphs, hash tables, time and space complexity. Write efficient code that works on limited hardware. Optimize for memory and CPU constraints. Understand databases, SQL, data cleaning, ETL pipelines, working with APIs. Handle messy, multilingual, sparse data. Build pipelines that work with intermittent connectivity and limited infrastructure. Hands-on ML: regression, classification, decision trees, random forests. Use scikit-learn to build predictive models. Deploy models quickly using pre-trained solutions and transfer learning. Design highly available, fault-tolerant systems. Load balancing, caching, distributed databases, disaster recovery. Build for 99.99% uptime. Circuit breakers, graceful degradation. Neural networks, CNNs, RNNs, training dynamics. Build models from scratch in PyTorch. Focus on reproducibility, versioning, and production-grade training pipelines. AWS/GCP/Azure for enterprise. Containerization (Docker/Kubernetes), CI/CD, infrastructure as code, security compliance, multi-region deployment. SOC2, ISO27001 considerations. Reinforces modern web fundamentals: responsive design, performance optimization, accessibility, security basics, and maintainable front-end architecture. Foundations of symbolic AI: search algorithms, planning, constraint satisfaction, knowledge representation, expert systems, probabilistic reasoning. Complements modern ML by understanding when rules outperform neural networks. How CPUs/GPUs/TPUs/NPUs actually work. Memory hierarchies, parallel processing, ARM vs x86. Edge AI chips, model optimization for specific hardware. Power consumption analysis. CNNs, object detection, segmentation, tracking. Deploy models on edge devices (Raspberry Pi, Jetson Nano, mobile phones). Real-time inference, camera interfaces, video processing pipelines. Internet architecture, networking protocols, wireless communication, latency, bandwidth optimization, IoT connectivity, and distributed system communication. Principles of data modeling across relational, document, graph, and time-series systems. Schema design, normalization, query optimization, and data lifecycle management. Time series forecasting, reinforcement learning for trading, portfolio optimization, risk modeling, high-frequency trading signals. Backtesting frameworks. DNA/RNA sequencing analysis, protein structure prediction, genomic data processing. BLAST, alignment algorithms, variant calling. Deep learning for radiology, pathology, dermatology. Segmentation, classification, detection. Working with DICOM, handling class imbalance, clinical validation. FHIR standards, HL7, EHR integration, de-identification, federated learning, differential privacy. FDA/CE/NMPA approval processes, clinical trial design, HIPAA/GDPR compliance, medical device classification. Protein folding (AlphaFold), drug discovery, materials science, theorem proving, mathematical reasoning. ML for differential equations, scientific simulation. CRISPR, gene editing for crops, molecular breeding, genomic selection. Managing field trial data. Regulatory approval for GMOs. Collaborative filtering, content-based filtering, hybrid approaches. Cold start problem, explore-exploit, diversity-accuracy tradeoffs. Algorithms behind TikTok, YouTube, Spotify. Transformer architecture deep dive, distributed training, mixture of experts, RLHF, constitutional AI. Working at scale: ZeRO, pipeline parallelism, activation checkpointing. Cognitive biases, habit formation, attention economics, persuasive technology. Ethics of addictive design. Designing for behavior change. Learn to detect communities, influence, centrality, and cascades. Understand how small design choices reshape entire networks and behavior of millions. Market basket analysis, nearest neighbor, decision trees, clustering, implementing different techniques of these algorithms. Discussion of the impact of computer technology on present and future society. Historical development of the computer. Social issues raised by cybernetics. Foundations of computer vision including image formation, feature extraction, object detection, motion estimation, and deep learning-based visual understanding. Learn how digital maps, GPS, and location-based services are built using computer vision, sensing, and visualization. Covers mapping technologies, spatial data processing, and real-world geospatial applications. Study how organizations use data without compromising privacy. Covers cryptography, secure computation, anonymization, and privacy-preserving technologies across sectors like social networks, finance, and cloud systems. Explore algorithmic game theory, Nash equilibria, market dynamics, and network decision-making. Applications include economics, internet systems, and strategic interactions in complex networks. Foundations of secure digital communication: encryption, signatures, key exchange, authentication, and privacy protocols. Emphasis on mathematical principles and modern security applications. Study systems where correctness depends on both accuracy and timing. Covers distributed computing, embedded systems, and real-time applications like smart vehicles and automated infrastructure. Design scalable distributed systems using modern cloud platforms. Covers resource management, big-data frameworks, distributed infrastructure, and real-world cloud deployments. Explore human-AI collaboration, explainable AI, bias analysis, interactive learning systems, and transparency in machine learning decision-making. Analyze social networks using graph theory, text analytics, and AI. Covers influence, communities, sentiment analysis, and applications in politics, marketing, and public health. Deep dive into advanced AI topics: optimization, probabilistic reasoning, reinforcement learning, deep learning, decision-making under uncertainty, and AI ethics and safety. Algorithms for sensing, perception, navigation, and decision-making in robots. Covers sensor fusion, localization, motion planning, and multi-agent coordination. Learn graphical models like Bayesian networks, HMMs, and CRFs for reasoning under uncertainty across domains like vision, NLP, and healthcare. Study how machines process human language: parsing, semantics, text generation, information retrieval, and conversational AI applications.Introduction to Computational Thinking
Rapid Web Development for AI Products
Data Structures & Algorithm Design
Data Engineering for Emerging Markets
Practical Machine Learning
System Design for Mission-Critical Applications
Practical Deep Learning
Enterprise Cloud Architecture & DevOps
Fundamentals of Web Development
Classical Artificial Intelligence
Computer Architecture & Edge Computing
Computer Vision & Embedded AI
Introduction to Data Networks and the Internet
Data Modeling and Applications
Quantitative Trading
Computational Biology & Genomics
Medical Imaging & Diagnostic AI
Health Data Privacy, Device Regulation & Clinical Trials
AI for Scientific Discovery
Agricultural Biotechnology & Data
Recommender Systems & Personalization
Foundation Models & Large-Scale Training
Behavioral Psychology & Persuasive Design
Social Network Analysis
Data Mining
Computers and Society
Computer Vision
Geospatial Vision and Visualization
Data Privacy and Security
Game Theory: Algorithms and Applications
Cryptography
Distributed Real-Time Systems
Cloud Computing
Interactive and Transparent Machine Learning
Online Social Network Analysis
Advanced Artificial Intelligence
Computational Robotics
Probabilistic Graphical Models
Natural Language Processing
Understand how technology products find customers. Learn positioning, customer discovery, digital marketing funnels, performance ads (Meta/Google), and early-stage sales tactics for AI products. Focus on validating demand quickly rather than theoretical marketing plans. Introduces economic thinking for builders: demand, incentives, pricing, market structure, and unit economics. Focus on interpreting markets and evaluating opportunities. Introduces macroeconomic forces shaping technology markets: inflation, interest rates, trade flows, demographics, policy, and technological diffusion. Focus on how macro trends affect startup strategy, capital access, and product-market timing in emerging economies. Examines ethical, philosophical, and societal implications of AI: bias, fairness, privacy, autonomy, and human-AI collaboration. Case studies from real deployments, especially in developing economies. Focus on responsible innovation rather than abstract theory. Unit economics, cohort analysis, CAC/LTV, burn rate, runway planning, pricing strategy, financial modeling, KPI dashboards, data-driven growth, and investor readiness. Term sheets, dilution, cap tables, valuation methods, and pitch construction. Practical project management for technical teams: Agile, Scrum, milestone planning, stakeholder communication, risk management, compliance documentation, and cross-functional coordination for enterprise deployments. Work with Shenzhen manufacturers. PCB design, sensor integration, enclosure design, rapid prototyping. MOQs, unit economics, supply chain, quality control, supply chain risk, tariffs, international shipping. Factory visits. Foundations of nutrition, sleep science, stress management, and cognitive performance. Focus on sustaining energy, mental clarity, and resilience for high-intensity technical careers and startup environments. Product-led growth, viral loops, network effects, growth hacking, A/B testing, retention optimization. Case studies: Instagram, Snapchat, Discord. Explore how humans and machines learn across psychology, neuroscience, education, and AI. Covers major learning theories, motivation, cognition, technology, and how intelligence and disability affect learning processes. An interdisciplinary look at how the mind works, combining psychology, neuroscience, computer science, linguistics, and behavioral economics to understand perception, reasoning, and intelligence.Sales & Marketing
Economics
Global Macroeconomics
Artificial Intelligence, Philosophy and Ethics
Business Metrics Analysis & Start-up Fundraising
Project Management for Information Technology and Management
Hardware Prototyping & Manufacturing at Scale
Nutrition & Wellness
Consumer Growth & Viral Mechanics
Learning Theory
Cognitive Science
MiFID II, GDPR, AML/KYC requirements, regulatory reporting. Building compliant fintech systems. RegTech tools and frameworks. Payment processing, banking partnerships, licensing requirements, fraud prevention. Unit economics of fintech. Case studies: Stripe, Wise, Revolut. Learn to identify, measure and mitigate financial, operational, and technological risks, with practical strategies founders use to protect businesses against uncertainty while preserving growth opportunities.Financial Regulation & Compliance Tech
Building & Scaling FinTech Products
Hedging & Risk Management