cloudsandai
About cloudsandai
A foundations-first, instructor-led learning initiative focused on mathematics, statistics, programming, scientific computing, machine learning and deep learning.
Learning Philosophy
Theory and computation, learned together.
Machine learning is easier to understand when the mathematics, statistics, algorithms, and implementation are connected.
Mathematical, statistical and machine learning concepts develop alongside programming, algorithms and scientific computing. Learn the concept, implement it, experiment with it, and understand the relationship between theory and computation.
Academic Foundations
Computational Foundations
Theory ↔ Computation ↔ Implementation
Deep Neural Networks follows as the advanced Course 2 extension.
About
About the Instructor
instructor.profile
Deven Dande & team of 3+ AI Engineers
Deven Dande and his team bring a foundations-first approach to AI/ML, backed by AWS certifications and hands-on experience delivering AI/ML projects and POCs for clients across India, the UK, USA, and government sectors. For the past three years, the team has also conducted AI/ML internships and training programs for polytechnic and engineering students across Nagpur, focusing on practical, industry-oriented learning with 200+ happy and knowledgable students.
Audience
Who Is This For?
Polytechnic & Engineering Students
For students looking for structured coverage of mathematical foundations, statistics, programming and machine learning.
Recent Graduates
For recent graduates who want to strengthen their foundations and connect theory with implementation.
Data Analysts & Data Scientists
For data professionals who want to deepen their understanding of the mathematics and mechanics behind machine learning.
ML Engineers Who Need to Understand the Basics
For ML engineers who know how to build systems but want to understand why the methods work.
Curious Learners, Irrespective of Background
For curious learners who want to understand machine learning from the foundations up, regardless of their background.
Start Learning
Build the Foundations.
Then Go Deeper.
Learn theory and computation together, then continue into deep neural networks with Course 2.