cloudsandai
About cloudsandai
A foundations-first, instructor-led learning initiative focused on mathematics, statistics, programming, scientific computing, machine learning and deep learning.
Learning Philosophy
How You Learn
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
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.