Mathematics · Statistics · Machine Learning · Deep Learning
Learn mathematics, statistics, machine learning, programming, algorithms and scientific computing together through a structured, instructor-led curriculum.
Advanced AI & Cloud tracks coming soon
Agentic AI · AWS Bedrock & SageMaker · AI Deployment on Cloud
Learning Philosophy
Learn the concept. Code it. Experiment with it. Understand it.
Programming is not taught as a separate subject. It is used throughout the curriculum to turn mathematical and statistical concepts into working implementations and experiments.
Mathematics
Python, NumPy, computation and visualization
Statistics
Python, data analysis and experiments
Machine Learning
Algorithm implementation, experimentation and evaluation
Theory ↔ Computation ↔ Implementation
Learning Roadmap
Mathematics, statistics and machine learning are learned through an ongoing cycle of theory, computation and implementation. Course 2 extends this foundation into advanced deep learning.
Foundations → Machine Learning → Deep Learning → Advanced AI & Cloud
Mathematical Foundations
Mathematics required to understand how machine learning works.
Statistical Methods
Probability, distributions, estimation, testing and related concepts used in ML.
Machine Learning
Core algorithms, intuition, implementation, evaluation and experimentation.
Theory ↔ Computation ↔ Implementation
Each concept moves through code, computation and experiments as it is learned.
Advanced programs focused on building and deploying modern AI systems.
Why This Approach
Understand the mathematical structures behind machine learning rather than treating algorithms as black boxes.
Build familiarity with probability, distributions, estimation, hypothesis testing and statistical modelling.
Study classical machine learning methods including regression, classification, trees, SVMs, Bayesian learning, ensembles and unsupervised learning.
Develop programming, algorithms and scientific computing alongside academic concepts, then connect them through implementation.
Course 2 builds on the previous foundation and moves into DNNs, CNNs, RNNs, attention, transformers and additional deep learning techniques.
Contact
Have questions about the curriculum or which learning path is right for you? Get in touch with Deven.
FAQ
cloudsandai connects mathematical foundations, statistics, programming, scientific computing and machine learning in one structured curriculum, helping learners understand both how AI and ML methods work and why they work.
cloudsandai takes a foundations-first, instructor-led approach. Concepts are studied alongside computation and implementation so learners can build a connected understanding of mathematics, statistics, programming, machine learning and deep learning.
The courses are designed for polytechnic and engineering students, recent graduates, data analysts and data scientists, ML engineers who want to understand the fundamentals, and curious learners irrespective of background.
It means building mathematical and statistical understanding while using programming throughout to implement concepts, test ideas and experiment before relying on advanced models. The curriculum connects theory, computation and implementation.
Course 1 covers three core areas: Mathematical Foundations for Machine Learning, Introduction to Statistical Methods, and Machine Learning. Programming and scientific computing are integrated throughout the curriculum through 8 programming sessions. It includes 120 hours and 60 lectures.
Course 2 includes everything in Course 1, plus Deep Neural Networks and 6 additional deep-learning implementation sessions, for 14 Programming Sessions total. It includes 180 hours and 90 lectures.
The curriculum covers mathematics, statistical methods and machine learning, with Python, NumPy, Pandas, Matplotlib, scientific computing and algorithm implementation integrated throughout. Advanced topics include neural networks, CNNs, sequence models, attention and transformers.
Choose Course 1 for the three core academic areas with integrated programming and implementation. Choose Course 2 if you want everything in Course 1 plus Deep Neural Networks and additional deep-learning implementation sessions.
Contact Deven using the enquiry form, email, phone or LinkedIn. You can ask questions about the curriculum and which learning path is right for you before enrolling.
Start Learning
Learn theory and computation together, then continue into deep neural networks with Course 2.