cloudsandai

Structured AI & Machine Learning Programs

The 16-week AI/ML Foundations and AI Engineering programs are paid, instructor-led options for learners seeking a structured path. Explore their curricula, workshops and limited-seat cohorts; tutorials and interactive resources remain free.

Structured Programs

Two Clear Learning Paths

Start with AI/ML Foundations or move directly into AI Engineering with the required Python background.

Starts from zero
PAID · 16 WEEKS · BEGINNERS

AI/ML Foundations

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Build a strong foundation in Python, mathematics, probability, statistics, machine learning and introductory deep learning through code, experimentation and a guided capstone project.

Python • Math • ML • Capstone
16 weeks · Beginner-focused path
01
Python Programming and Scientific ComputingPython fundamentals, data structures and numerical computing with real examples and datasets.
02
Mathematics, Probability and StatisticsVectors, gradients, optimization, uncertainty and the ideas needed to understand ML models.
03
Core Machine Learning and Model EvaluationUnderstand supervised learning, training workflows, evaluation and model limitations.
04
Introduction to Deep LearningNeural networks, activation functions and the intuition behind learning in deep models.
05
Guided Capstone ProjectApply your learning to a real dataset, explain the model and discuss its strengths and limitations.
Learning highlights
Build predictive models
Analyze real datasets
Train your first neural network
One guided capstone project
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PAID · 16 WEEKS · PYTHON PROFICIENCY

AI Engineering

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Learn to integrate modern AI models, APIs, tools and cloud infrastructure to build useful AI applications across three guided and assessed projects.

LLMs • RAG • APIs • Agents • AWS
16 weeks · Python proficiency required
01
LLM FundamentalsTokens, context windows, prompting, structured outputs and model evaluation.
02
Models and Open-Source EcosystemsHugging Face, Ollama, local inference and selecting between hosted and local models.
03
APIs and Application DevelopmentHTTP, JSON, Python backend development and FastAPI-based application design.
04
Retrieval-Augmented GenerationDocument ingestion, embeddings, retrieval and grounded answers with citations.
05
Agentic AITool calling, state management, bounded execution and human approval flows.
06
AWS and DeploymentDeploy a lightweight AI application with logging, cost and latency considerations.
Learning highlights
Knowledge-Grounded AI
Multimodal AI
Agentic AI
Guided and assessed project work
Book a ConsultationView Curriculum

Learning Roadmap

The Learning Path

Choose a beginner path into machine learning foundations or build practical AI applications with Python.

Foundations Program16 weeks

AI/ML Foundations

Beginners starting from zero

Build a strong foundation in Python, mathematics, statistics, machine learning and introductory deep learning.

Practical outcome: Build predictive models, analyze real datasets and train your first neural network.

Project: Complete one guided, assessed ML capstone.

Explore Foundations
AI Engineering Program16 weeks

AI Engineering

Learners with Python proficiency

Build AI applications using LLMs, APIs, multimodal models, retrieval-augmented generation and agentic workflows.

Practical outcome: Build applications that work with documents and media, integrate AI models, and use tools to execute controlled workflows.

Projects: Complete three guided, assessed end-to-end projects, one from each category: Knowledge-Grounded AI, Multimodal AI and Agentic AI.

Explore AI Engineering

Comparison

Compare the Two 16-Week Programs

FeatureAI/ML FoundationsBeginnersAI EngineeringApplication-first
Duration16 weeks16 weeks
AudienceBeginners from zeroPython-ready learners
PrerequisitesNonePython proficiency + consultation
Learning focusAI/ML fundamentalsAI application building
Capstone / projects1 capstone project3 end-to-end projects
Core technologiesPython · math · MLLLMs · APIs · RAG · agents · AWS

AI Engineering is for Python-ready learners who want to build practical AI applications.

Curriculum

The Curriculum

Two focused 16-week courses cover the fundamentals of AI and machine learning, then move into practical AI application development.

Module 01
01

Mathematical Foundations for Machine Learning

A

Vectors and Matrices

  • Vectors and vector operations
  • Matrices
  • Matrix multiplication
  • Dot products
B

Distance and Similarity

  • Norms and distances
  • Inner products and similarity
C

Derivatives and Gradients

  • Derivatives
  • Partial differentiation
  • Gradients
  • Chain rule
D

Optimization

  • Loss functions
  • Gradient descent
  • Stochastic gradient descent
  • Learning rate
Module 02
02

Introduction to Statistical Methods

A

Descriptive Statistics

  • Mean and median
  • Variance and standard deviation
B

Probability and Bayes Rule

  • Basic probability
  • Conditional probability
  • Independent events
  • Bayes rule
C

Random Variables and Distributions

  • Random variables
  • Expectation
  • Variance
  • Covariance
  • Bernoulli distribution
  • Gaussian distribution
Module 03
03

Machine Learning

A

Introduction

  • Introduction to Machine Learning
  • Types / taxonomy of Machine Learning
  • Design a learning system
  • Challenges in Machine Learning
B

Machine Learning Workflow

  • Role of data
  • Data preprocessing
  • Data wrangling
  • Data skewness removal / sampling
  • Model training
  • Model testing
  • Performance metrics
C

Linear Models for Regression

  • Direct solution method
  • Gradient descent
  • Batch gradient descent
  • Stochastic gradient descent
  • Mini-batch gradient descent
  • Linear basis function models
  • Bias-variance decomposition
D

Linear Models for Classification

  • Discriminant functions
  • Decision theory
  • Probabilistic discriminative classifiers
  • Logistic regression
  • Log-loss function
  • Gradient descent
  • Multi-class classification
E

Decision Trees

  • Information theory
  • Entropy
  • Entropy-based decision tree construction
  • Avoiding overfitting
  • Minimum Description Length
  • Continuous-valued attributes
  • Missing attributes
F

Instance-Based Learning

  • k-Nearest Neighbor
  • Locally Weighted Regression
  • Radial Basis Functions
G

Support Vector Machines

  • Linearly separable data
  • Non-linearly separable data
  • Kernel Trick
  • Mercer kernels
  • Applications to structured data
  • Applications to unstructured data
H

Bayesian Learning

  • MLE hypothesis
  • MAP hypothesis
  • Bayes rule
  • Optimal Bayes classifier
  • Naïve Bayes classifier
  • Probabilistic generative classifiers
  • Bayesian interpretation of linear regression
I

Ensemble Learning

  • Combining classifiers
  • Bagging
  • Random Forest
  • Boosting
  • AdaBoost
  • Gradient Boosting
  • XGBoost
J

Unsupervised Learning

  • K-Means clustering
  • K-Means variants
  • Mixture models for probabilistic clustering
  • Expectation Maximization review
  • Applications
K

ML Model Evaluation

  • Comparing Machine Learning models
  • Bias
  • Fairness
  • Interpretability
Module 04
04

Introduction to Deep Learning

A

Neural Network Fundamentals

  • Neurons, layers and weights
  • Forward propagation
  • Activation and loss functions
  • Gradient descent and backpropagation (introduction)
  • Overfitting and regularization
  • Common applications of deep learning

Learning Methodology

Integrated Programming & Implementation

Programming runs alongside the academic curriculum rather than being treated as a separate subject. Every major concept is connected to computation, coding, experimentation, visualization, and algorithm implementation.

Learn the concept → Code it → Experiment with it → Understand it

Theory ↔ Computation ↔ Implementation, throughout the curriculum

01Mathematics
02Code & Compute
03Experiment
04Machine Learning

This is a recurring learning cycle, not a sequence completed before coding begins.

PythonNumPyPandasMatplotlibScientific ComputingAlgorithm ImplementationData AnalysisML ImplementationProblem SolvingExperiments and Visualization

Explore integrated programming topics

A

Python Foundations

  • Python syntax
  • Variables and data types
  • Conditionals
  • Loops
  • Functions
  • Modules
  • Basic debugging
B

Programming Fundamentals

  • Problem decomposition
  • Functions
  • Recursion
  • Computational thinking
  • Code organization
  • Basic complexity concepts
C

Data Structures

  • Lists
  • Tuples
  • Dictionaries
  • Sets
  • Stacks
  • Queues
  • Practical use of data structures
D

Algorithms

  • Searching
  • Sorting
  • Recursion
  • Algorithmic thinking
  • Big-O / computational complexity
  • Implementation-oriented problem solving
E

NumPy & Numerical Computing

  • Arrays
  • Vectorization
  • Broadcasting
  • Matrix operations
  • Numerical computation
F

Scientific Computing

  • Numerical differentiation
  • Numerical integration
  • Simulation
  • Numerical optimization
  • Computational experiments
G

Data Handling & Visualization

  • Pandas
  • Data cleaning
  • Exploratory data analysis
  • Matplotlib
  • Visualization
H

Machine Learning Programming

  • Translating mathematical equations into code
  • Implementing ML algorithms
  • Training workflows
  • Evaluation
  • Computational experiments

AI/ML Foundations

Build from zero to practical ML understanding

Learn Python, mathematics, statistics, machine learning and introductory deep learning, then build predictive models, analyze real datasets and train your first neural network.

Python
Coding
Implementation
Problem Solving
ML-oriented Programming

Live instructor-led classes

Class recordings uploaded to the LMS after each class

AI Engineering

Build production-minded AI applications

Complete one instructor-approved application in each of three categories: Knowledge-Grounded AI, Multimodal AI and Agentic AI. All three projects are guided, assessed and demonstrated.

3Guided,
assessed projects

Live instructor-led classes

Class recordings uploaded to the LMS after each class

1Knowledge-Grounded AI · Document Intelligence Assistant
2Multimodal AI · Speech and Translation Assistant
3Agentic AI · Booking Workflow Agent

AI Engineering

Practical AI Applications

Move beyond fundamentals to build LLM-powered apps, document-grounded workflows, agentic systems and cloud-deployed solutions.

16-week application-first course
01

LLM Fundamentals

Tokens, context windows, prompting, structured outputs and model evaluation.

02

Models and Open-Source Ecosystems

Hugging Face, Ollama, local inference and selecting between hosted and local models.

03

APIs and Application Development

HTTP, JSON, Python backend development and FastAPI-based application design.

04

Retrieval-Augmented Generation

Document ingestion, embeddings, retrieval and grounded answers with citations.

05

Agentic AI

Tool calling, state management, bounded execution and human approval flows.

06

AWS and Deployment

Deploy a lightweight AI application with logging, cost and latency considerations.

01

LLMs

02

RAG

03

FastAPI

04

Agentic AI

05

Tool Calling

06

Embeddings

07

Grounded Answers

08

Model Evaluation

09

AWS Deployment

10

Application Security

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Course Selection

Which Track Is Right For You?

AI/ML FoundationsCall to know more

A beginner-friendly start

Learn Python, mathematics, statistics and machine learning fundamentals through guided practice, hands-on coding and a beginner-level capstone project.

Beginners from zero
16 weeks
Python and scientific computing
Capstone project
Theory ↔ Computation ↔ Implementation
Call to know more
AI EngineeringBook a consultation

Build practical AI systems

Learn to integrate LLMs, APIs, open-source models, RAG and agentic workflows into production-minded AI applications, with AWS deployment as part of the learning path.

16 weeks
Python proficiency and consultation with Deven before admission
LLMs, RAG and API integration
Three end-to-end AI engineering projects
AWS deployment and application workflow design
Book a consultation

FAQ

Frequently Asked Questions

What courses does cloudsandai offer?

cloudsandai offers AI/ML Foundations for beginners starting from zero and AI Engineering for learners with Python proficiency who want to build AI applications.

What is the difference between AI/ML Foundations and AI Engineering?

AI/ML Foundations builds Python, mathematics, statistics and machine learning understanding, including introductory deep learning. AI Engineering focuses on building applications with LLMs, APIs, RAG, multimodal models and agentic workflows.

Can beginners join AI/ML Foundations without prior programming experience?

Yes. AI/ML Foundations is designed for beginners starting from zero and introduces Python as part of the course.

What are the prerequisites for AI Engineering?

Learners should have Python programming proficiency and discuss readiness and learning goals with Deven during a consultation.

Can I join AI Engineering without completing AI/ML Foundations?

Yes. Completing AI/ML Foundations is not required. Direct entry to AI Engineering is possible for learners with Python proficiency after a consultation with Deven.

How long are the courses?

Both AI/ML Foundations and AI Engineering run for 16 weeks and combine live instructor-led classes with recordings uploaded to the LMS after class.

Are the courses live or self-paced?

The courses use live instructor-led classes. Class recordings are uploaded to the LMS after each class for review.

Are class recordings available after live sessions?

Yes. Recordings are uploaded to the LMS after class.

What practical projects will I build during AI/ML Foundations?

You will independently complete one guided, assessed ML capstone. Example directions include a predictive model, dataset exploration with a classification model, or a simple image-classification neural network; these are possible directions, not separate required projects.

What projects will I complete in AI Engineering?

Every student completes three guided, assessed end-to-end projects: one instructor-approved application in each category—Knowledge-Grounded AI, Multimodal AI and Agentic AI.

How are the projects guided and assessed?

Projects use structured milestones and instructor guidance. Students demonstrate working functionality, explain implementation decisions and receive review of code and outputs.

Do the courses guarantee placement or employment?

No. The courses focus on learning and practical project development; employment or placement outcomes are not guaranteed.

How can I enquire about course fees and admission?

Book a consultation with Deven using the existing consultation links on the courses page to discuss fees, course fit and admission. Course prices are not published on the website.

How do I book a consultation with Deven?

Use a Book a Consultation link on the courses page to open the existing Calendly consultation and choose an available time.

Free Learning Resources

Learn the Concepts.
Build Something Practical.

Start with free tutorials and interactive resources, then explore an optional structured program if you want a guided learning path.

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