EARLY REGISTRATION

৳10,000 OFF Regular fee ৳50,000 · Early registration ৳40,000

STARTING LATE NOVEMBER 2026

THE PROGRAMME · LATE NOVEMBER 2026

24 classes. 6 months.
One complete research journey.

A research-first, hands-on program designed to take learners from research foundations and literature review to dataset development, AI/ML experimentation, proposed model development, rigorous evaluation, scientific writing, and publication-ready research.

Research Foundation AI & Machine Learning Computer Vision Transformers Hands-on Research Proposed Models Scientific Writing Publication

Months

Live Classes

Research Modules

Research Project

One live class per week. Sessions are generally 1.5 to 3 hours and may extend when research implementation, experiments, discussion, or hands-on work requires additional time.

EARLY ACCESS · OCTOBER–NOVEMBER 2026

Early Access · October–November 2026

The full programme starts in late November 2026 — but your research journey can start earlier. Complete your first installment before November 2026 to unlock Early Access.

UNLOCK WITH 1st Installment Available during October–November 2026

Expert Talks

Learn from researchers, academics and experienced professionals.

Research Guidelines

Start understanding research interests, papers and research pathways.

Higher Studies

Begin preparing for academic pathways and higher-study planning.

Research Q&A

Ask questions and get guidance before the full programme begins.

Basic Coding

Build the beginner-friendly technical foundation needed for AI/ML research.

AI/ML Orientation

Get familiar with the AI/ML research direction before the full bootcamp begins.

01Early RegistrationOctober 2026

02Early AccessOctober–November 2026

03Full BootcampLate November 2026

Early Access eligibility: Students who complete their first installment can access the Early Access activities during October–November 2026.

PROGRAM SNAPSHOT

Learn AI/ML through the complete research lifecycle

The program follows the actual workflow of AI/ML research, beginning with research foundations and domain selection and progressing through literature review, dataset development, modeling, experimentation, proposed model development, evaluation, scientific writing, publication, and research career development.

What students will learn

AI/ML research methodology and scientific thinking
AI/ML domain orientation including ML, DL, NLP, CV and segmentation
Literature search, critical paper reading and research gap analysis
Research ethics, AI ethics and academic integrity
Python for AI/ML research
Google Colab, Kaggle and research computing
Git, GitHub and reproducible research workflows
Data collection, processing and annotation
Machine learning and deep learning
Computer vision, classification, detection and image segmentation
Transformers, Hugging Face and foundation models
Proposed model development and implementation
Model evaluation, reliability and statistical analysis
LaTeX and scientific manuscript development
Publication, peer review and research career development

Hands-on research experience

Research problem selection
Literature matrix development
Research gap identification
Dataset development and auditing
Annotation and preprocessing
Python research notebooks
GPU based experiments
GitHub research repository development
Baseline model implementation
Proposed model implementation
Comparative and ablation experiments
Error and failure analysis
LaTeX manuscript development
Publication preparation

WHY AMIR ACADEMY

Built around research, not just lectures

Every major stage combines learning, practical work, assignments, implementation, feedback, and a concrete research deliverable.

Research First

Learn how to turn a research problem into a structured and testable AI/ML study.

Hands-on Work

Build datasets, implement models, run experiments and analyze real results.

Research Deliverables

Every class produces an assignment and a concrete research artifact or deliverable.

Proposed Models

Learn how to move from a research gap to a proposed architecture and implementation.

Rigorous Evaluation

Understand which metrics, validation strategies and analyses matter for different research problems.

Publication Focus

Develop a complete research manuscript and learn how to prepare it for publication.

RESEARCH METHODOLOGY

From research question to publication

Students progressively build a complete AI/ML research workflow, connecting research theory, technical implementation, experimentation, scientific writing and publication.

01 Problem

02 Literature

03 Research Gap

04 Dataset

05 Baseline

06 Proposed Model

07 Experiments

08 Evaluation

09 Writing

10 Publication

COMPLETE CURRICULUM

12 Research-Focused Modules · 24 Live Classes

The curriculum is structured around a complete research journey. Each class includes practical work, an assignment and a concrete research deliverable.

Program Structure

M01 · 1 class · AI/ML Research Foundations & Domain Orientation
M02 · 2 classes · Literature Review, Research Gap & Ethics
M03 · 3 classes · Python, Colab, Kaggle & GitHub
M04 · 2 classes · Data Collection, Processing & Annotation
M05 · 3 classes · Machine Learning, Deep Learning & Optimization
M06 · 2 classes · Transformers, Hugging Face & Foundation Models

Research Project to Publication

M07 · 3 classes · Hands-on AI/ML Research Project
M08 · 2 classes · Proposed Model Development
M09 · 2 classes · Model Evaluation & Reliability
M10 · 2 classes · LaTeX, Scientific Writing & Manuscript Development
M11 · 1 class · Publication, Submission & Peer Review
M12 · 1 class · Research Career & Academic Profile

01 AI/ML Research Foundations & Domain Orientation 1 Class · Build the research mindset and understand major AI/ML research domains.

Class 01 · AI/ML Research Landscape & Research Foundations

AI/ML research fundamentals

Research vs software development

Research lifecycle

Scientific thinking

Research problems and questions

Research objectives

Hypothesis and contribution

Machine Learning

Deep Learning

Computer Vision

Image Classification

Object Detection

Image Segmentation

Natural Language Processing

Generative AI

Foundation Models

Reinforcement Learning

Graph Machine Learning

AI for Healthcare and Science

Explainable AI

Trustworthy AI

Selecting a research direction

Assignment · AI/ML Research Domain Exploration

Select a potential AI/ML research domain.
Explore relevant research problems.
Identify a preliminary research direction.

Deliverable · AI/ML Research Direction Brief

Selected research domain
Potential research problem
Initial research direction

02 Literature Review, Research Gap & Ethics 2 Classes · Learn how to investigate existing research and formulate a credible research direction.

Class 02 · Literature Search & Critical Paper Reading

Google Scholar

Academic databases

Search strategies

Keyword development

Paper structure

Three-pass paper reading

Critical paper analysis

Dataset comparison

Method comparison

Result comparison

Literature synthesis

Literature matrix

Citation management

Assignment · Literature Investigation

Find relevant peer-reviewed papers.
Compare methods, datasets and results.
Extract limitations from the selected papers.

Deliverable · Literature Matrix

Structured literature matrix
Paper comparison
Preliminary research observations

Class 03 · Research Gap, Novelty & Research Ethics

Research limitations

Research gap identification

Novelty

Research questions

Research ethics

AI ethics

Bias and fairness

Privacy

Academic integrity

Plagiarism

Authorship

Publication ethics

Responsible AI

Assignment · Research Gap & Ethics Analysis

Identify a research gap from the literature.
Explain the potential novelty.
Analyze ethical considerations.

Deliverable · Research Gap Statement + Ethics Checklist

Research gap statement
Preliminary novelty direction
Research ethics checklist

03 Python, Google Colab, Kaggle & GitHub 3 Classes · Build the technical environment required for reproducible AI/ML research.

Class 04 · Python for AI/ML Research

Python fundamentals

NumPy

Pandas

Matplotlib

Dataset loading

Data manipulation

Basic EDA

Research notebooks

Assignment · Dataset Exploration Notebook

Load a research dataset.
Inspect and summarize the data.
Create basic visualizations.

Deliverable · Reproducible Python EDA Notebook

Clean notebook
EDA results
Visualizations and observations

Class 05 · Google Colab & Kaggle Research Computing

Google Colab

Kaggle

Kaggle datasets

Kaggle notebooks

CPU

GPU

GPU memory

Runtime configuration

Package installation

Research experiments

Efficient data loading

Assignment · GPU Research Experiment

Configure a research environment.
Run an AI/ML experiment.
Document the runtime configuration.

Deliverable · Reproducible Colab/Kaggle Experiment

Working notebook
Environment configuration
Experiment results

Class 06 · Git & GitHub for Research

Git fundamentals

Repositories

Commits

Branches

GitHub

README

Repository structure

Version control

Code organization

Reproducibility

Research collaboration

Assignment · Research Repository Setup

Create a structured research repository.
Commit research code and notebooks.
Document the project.

Deliverable · Structured GitHub Research Repository

Organized repository
README documentation
Version-controlled research code

04 Data Collection, Processing & Annotation 2 Classes · Develop reliable datasets suitable for AI/ML research.

Class 07 · Research Data Collection & Dataset Development

Research data sources

Data collection

Dataset design

Data formats

Data cleaning

Missing values

Duplicate records

Preprocessing

Feature preparation

Assignment · Dataset Development

Select or collect research data.
Perform initial cleaning.
Prepare the dataset for modeling.

Deliverable · Initial Research Dataset Package

Dataset profile
Cleaned data
Preprocessing workflow

Class 08 · Annotation, Quality & Data Leakage

Data annotation

Annotation guidelines

Label quality

Quality control

Class imbalance

Train/validation/test split

Entity-level splitting

Data leakage

Dataset auditing

Dataset documentation

Assignment · Dataset Audit

Audit a research dataset.
Identify imbalance and leakage risks.
Design an annotation workflow.

Deliverable · Data Audit Report

Dataset quality findings
Leakage analysis
Annotation and preprocessing plan

05 Machine Learning, Deep Learning & Optimization 3 Classes · Build strong modeling foundations and learn how to improve generalization.

Class 09 · Machine Learning Foundations

Supervised learning

Unsupervised learning

Classification

Regression

Feature engineering

Baseline models

Cross-validation

Hyperparameter tuning

Ensemble methods

Assignment · Baseline Modeling

Implement multiple baseline models.
Use an appropriate validation strategy.
Compare model performance.

Deliverable · Baseline Benchmark Report

Model comparison table
Validation results
Baseline selection rationale

Class 10 · Deep Learning & Computer Vision for Research

Neural networks

Backpropagation

Loss functions

Optimizers

CNNs

Transfer learning

Computer Vision pipelines

Image classification

Object detection

Image segmentation

Semantic segmentation

Instance segmentation

Attention mechanisms

PyTorch

Assignment · Deep Learning / CV Experiment

Implement a deep learning pipeline.
Experiment with a computer vision task.
Analyze initial model performance.

Deliverable · Working DL/CV Pipeline + Initial Results

Working pipeline
Initial results
Experiment observations

Class 11 · Model Optimization & Generalization

Overfitting

Underfitting

Bias and variance

Learning curves

Regularization

Dropout

Weight decay

Early stopping

Data augmentation

Hyperparameter optimization

Generalization

Computational efficiency

Assignment · Model Optimization Study

Analyze training and validation behavior.
Identify overfitting or underfitting.
Apply appropriate optimization strategies.

Deliverable · Optimization Report

Diagnosis evidence
Optimization experiments
Before and after comparison

06 Transformers, Hugging Face & Foundation Models 2 Classes · Explore modern pretrained, transformer and foundation model workflows.

Class 12 · Transformers & Hugging Face

Transformer architecture

Self-attention

Pretrained models

Transfer learning

Hugging Face

Model Hub

Dataset Hub

Tokenizers

Inference

Assignment · Transformer Experiment

Select an appropriate pretrained model.
Run inference.
Analyze the research application.

Deliverable · Transformer Experiment Report

Model selection rationale
Experiment results
Research application analysis

Class 13 · Foundation Models & Research Applications

Large Language Models

Vision foundation models

Multimodal models

Fine-tuning

Parameter-efficient fine-tuning

Foundation model selection

Research applications

Assignment · Foundation Model Research Study

Select a suitable foundation model.
Evaluate its suitability for a research problem.
Analyze possible adaptation strategies.

Deliverable · Foundation Model Evaluation Report

Model selection rationale
Research application analysis
Evaluation findings

07 Hands-on AI/ML Research Project 3 Classes · Apply the complete research workflow to one hands-on project.

Class 14 · Research Project Setup

Final research problem

Research questions

Objectives

Literature review

Research gap

Dataset selection

Research methodology

Experimental design

Assignment · Research Project Proposal

Define the research problem.
Formulate research questions and objectives.
Define the proposed experimental direction.

Deliverable · Research Project Proposal

Problem statement
Research gap
Research methodology outline

Class 15 · Research Pipeline Implementation

Dataset preparation

Preprocessing

Baseline implementation

Model training

Experiment tracking

Reproducibility

Assignment · Research Pipeline Implementation

Implement the selected research pipeline.
Prepare the data and baseline.
Run the initial experiment.

Deliverable · Working Research Pipeline

Research code
Prepared dataset pipeline
Initial experiment output

Class 16 · Research Experiments

Comparative experiments

Baseline comparison

Experiment management

Ablation planning

Error analysis

Failure analysis

Reproducibility

Assignment · Experimental Study

Run comparative experiments.
Record experiment configurations.
Analyze errors and failures.

Deliverable · Research Experiment Package

Experiment logs
Comparative results
Error analysis

08 Proposed Model Development 2 Classes · Move from research gap and novelty to a proposed methodology.

Class 17 · From Research Gap to Proposed Model

Research novelty

Gap to methodology

Architecture design

Model components

Feature representation

Feature fusion

Attention mechanisms

Custom components

Architecture diagrams

Methodological contribution

Assignment · Proposed Model Design

Design a model addressing the research gap.
Explain each proposed component.
Define the expected contribution.

Deliverable · Proposed Architecture + Methodology

Architecture diagram
Methodology description
Novelty statement

Class 18 · Proposed Model Implementation

Architecture implementation

Component integration

Training strategy

Baseline vs proposed model

Configuration

Debugging

Reproducibility

Assignment · Proposed Model Implementation

Implement the proposed architecture.
Integrate all model components.
Prepare the model for experiments.

Deliverable · Working Proposed Model

Model implementation
Training configuration
Initial proposed model results

09 Model Evaluation & Reliability 2 Classes · Learn how to evaluate models rigorously and analyze reliability.

Class 19 · Evaluation Metrics & Experimental Analysis

Accuracy

Precision

Recall

F1-score

ROC-AUC

PR-AUC

MCC

Confusion matrix

MAE

MSE

RMSE

R²

Assignment · Evaluation Strategy

Select appropriate metrics.
Evaluate a trained model.
Explain metric relevance.

Deliverable · Evaluation Framework + Results Table

Metric selection rationale
Evaluation table
Experimental analysis

Class 20 · Reliability, Validation & Error Analysis

Cross-validation

Internal validation

External validation

Statistical significance

Confidence intervals

Calibration

Error analysis

Failure analysis

Robustness

Generalization

Ablation analysis

Assignment · Model Reliability Study

Apply an appropriate validation strategy.
Analyze errors and failure cases.
Assess model reliability.

Deliverable · Model Evaluation & Reliability Report

Validation results
Error analysis
Reliability findings

10 LaTeX, Scientific Writing & Manuscript Development 2 Classes · Build and write the research manuscript using LaTeX.

Class 21 · LaTeX & Research Manuscript Formatting

Overleaf

LaTeX fundamentals

Document structure

Sections and subsections

Equations

Tables

Figures

Captions

Labels

Cross-referencing

BibTeX

Bibliography

Citation styles

IEEE templates

Elsevier templates

Two-column formatting

Compilation

Troubleshooting

Assignment · LaTeX Research Paper Setup

Create a complete Overleaf project.
Implement sections, figures and tables.
Configure citations and references.

Deliverable · Structured Overleaf Research Paper

Working LaTeX project
Formatted manuscript
Bibliography and references

Class 22 · Scientific Writing & Complete Manuscript Development

Research paper structure

Title

Abstract

Keywords

Introduction

Literature review

Research gap

Methodology

Proposed method

Experimental setup

Results

Discussion

Limitations

Future work

Conclusion

Scientific writing

Evidence-based reporting

Tables and figures

Assignment · Complete Research Manuscript

Write the major sections of the research paper.
Integrate research findings and experimental results.
Prepare figures and tables.

Deliverable · Complete Research Manuscript Draft

Complete manuscript structure
Research figures and tables
Written research sections

Students write their own research paper. This module does not include AI-assisted paper writing.

11 Publication, Submission & Peer Review 1 Class · Learn how to prepare and submit research responsibly.

Class 23 · From Manuscript to Publication

Journal selection

Conference selection

Scope matching

Q1, Q2, Q3 and Q4

Indexing

Predatory journals

Submission workflow

Cover letter

Peer review

Reviewer comments

Revision

Response to reviewers

Resubmission

Assignment · Publication Strategy

Identify suitable publication venues.
Compare scope and suitability.
Prepare a submission strategy.

Deliverable · Publication Package

Target journal/conference shortlist
Submission checklist
Publication roadmap

12 Research Career & Academic Profile 1 Class · Build a sustainable research profile and long-term academic roadmap.

Class 24 · Building a Research Career

Research portfolio

Academic CV

Research statement

Research interests

ORCID

Google Scholar

ResearchGate

GitHub research profile

Research collaboration

Research internships

Graduate research opportunities

PhD preparation

Publication record

Long-term research roadmap

Assignment · Personal Research Career Plan

Define your research interests.
Develop an academic profile strategy.
Plan your next research milestones.

Deliverable · Research Career Portfolio + Future Research Roadmap

Research portfolio
Academic profile plan
Long-term research roadmap

HANDS-ON CAPSTONE

One complete AI/ML research project

Students progressively build one research project throughout the program, connecting research foundations, technical implementation, experimentation, proposed methodology, evaluation and manuscript development.

01 · Research Problem

Define a focused problem, research questions, objectives, hypothesis and expected contribution.

02 · Literature

Review previous work, compare methods and identify the research gap.

03 · Dataset

Collect or select data, perform preprocessing, annotation, quality control and leakage analysis.

04 · Baselines

Establish credible baseline models and define an appropriate experimental protocol.

05 · Proposed Model

Design and implement a proposed methodology addressing the identified research gap.

06 · Evaluation

Conduct comparative experiments, ablations, validation, statistical analysis and failure analysis.

07 · Writing

Develop the methodology, results, discussion and other sections of the scientific manuscript.

08 · Publication

Prepare the research package and identify suitable publication venues.

EVALUATION MATTERS

Learn when each metric matters

Model evaluation is not about reporting as many metrics as possible. Students learn how the research problem, class distribution, decision objective and experimental design determine the appropriate evaluation strategy.

Balanced Classification

Accuracy, Precision, Recall, F1 and confusion matrix can provide complementary evidence.

Imbalanced Classification

PR-AUC, Recall, F1, MCC and class-specific analysis may become more informative than accuracy alone.

Ranking & Discrimination

ROC-AUC and PR-AUC help assess ranking and discrimination across thresholds.

Regression

MAE, MSE, RMSE and R² provide different views of prediction error and explained variance.

Reliability

Calibration, confidence intervals and statistical analysis help assess reliability beyond a single score.

Generalization

Cross-validation, external validation, error analysis and failure analysis help assess robustness.

WHAT YOU TAKE WITH YOU

Build a research profile, not just a course certificate

The bootcamp is built around the complete research lifecycle — from research foundations and implementation to evaluation, scientific writing and publication preparation.

01+ Research Paper Target

RESEARCH OUTPUT

Research Paper

Students who properly complete the research work and deliverables are guided toward at least one research paper through the full research-to-publication workflow.

AMIR LAB

Research Internship Certificate

Every student who successfully completes the AMIR Academy bootcamp receives an AMIR Lab Research Internship Certificate.

ACADEMIC PROFILE

Letter of Recommendation

Build documented research experience that can support an LOR based on the student's work, contribution and performance.

HIGHER STUDIES

SOP Support

Get guidance on presenting your research experience, academic goals and higher-study direction in your Statement of Purpose.

AMIR LAB · PUBLICATION SUPPORT

APC / Publication Fee Support

Selected high-quality research projects may have an opportunity to receive APC or publication-fee support from AMIR Lab, subject to research quality, target venue, publication suitability and AMIR Lab approval.

SELECTED PROJECTS

AMIR LAB · PERFORMANCE-BASED PATHWAY

Perform well. Go further with AMIR Lab.

Students who demonstrate strong research performance, technical ability, consistency and quality of work during the bootcamp may be considered for an AMIR Lab Research Assistantship (RA).

01BootcampLearn & build

02ResearchDeliver quality work

03PerformanceStand out through work

04AMIR Lab RAPerformance-based progression

RESEARCH TRACK RECORD

40+ 2025 Batch Alumni

A research-oriented learning environment

More than 40 alumni came through the 2025 batch. Many alumni went on to publish research papers after completing their research journey, reflecting the practical and research-focused direction of the program.

PROGRAM INVESTMENT

Regular Fee · ৳50,000

৳40,000

EARLY REGISTRATION · SAVE ৳10,000

Program starts in late November 2026. Early registration price is currently ৳40,000.

৳10,000+ First Payment

03 Installments

06 Months

Program highlights

6-month research-focused program
24 live classes
12 research-focused modules
1 student-friendly live class every week
Hands-on AI/ML research project
Assignment and deliverable for every class
Python, Colab, Kaggle and GitHub
Machine Learning, Deep Learning and Computer Vision
Image classification, detection and segmentation
Transformers and foundation models
Proposed model development
Rigorous model evaluation
LaTeX and scientific manuscript development
Publication and peer review preparation
AMIR Lab Research Internship Certificate upon successful bootcamp completion
Research paper development and publication preparation
LOR and SOP support
Potential APC / publication-fee support for selected research projects
Performance-based AMIR Lab RA pathway

Research. Build. Evaluate. Publish.

Start building the research skills, technical foundation and publication workflow needed for serious AI/ML research — and take your research profile further with AMIR Lab.

12 MODULES · 24 CLASSES · 06 MONTHS · LATE NOVEMBER 2026

Book your free consultation ↗

24 classes. 6 months.One complete research journey.

Early Access · October–November 2026

Expert Talks

Research Guidelines

Higher Studies

Research Q&A

Basic Coding

AI/ML Orientation

Learn AI/ML through the complete research lifecycle

What students will learn

Hands-on research experience

Built around research, not just lectures

Research First

Hands-on Work

Research Deliverables

Proposed Models

Rigorous Evaluation

Publication Focus

From research question to publication

12 Research-Focused Modules · 24 Live Classes

Program Structure

Research Project to Publication

Class 01 · AI/ML Research Landscape & Research Foundations

Assignment · AI/ML Research Domain Exploration

Deliverable · AI/ML Research Direction Brief

Class 02 · Literature Search & Critical Paper Reading

Assignment · Literature Investigation

Deliverable · Literature Matrix

Class 03 · Research Gap, Novelty & Research Ethics

Assignment · Research Gap & Ethics Analysis

Deliverable · Research Gap Statement + Ethics Checklist

Class 04 · Python for AI/ML Research

Assignment · Dataset Exploration Notebook

Deliverable · Reproducible Python EDA Notebook

Class 05 · Google Colab & Kaggle Research Computing

Assignment · GPU Research Experiment

Deliverable · Reproducible Colab/Kaggle Experiment

Class 06 · Git & GitHub for Research

Assignment · Research Repository Setup

Deliverable · Structured GitHub Research Repository

Class 07 · Research Data Collection & Dataset Development

Assignment · Dataset Development

Deliverable · Initial Research Dataset Package

Class 08 · Annotation, Quality & Data Leakage

Assignment · Dataset Audit

Deliverable · Data Audit Report

Class 09 · Machine Learning Foundations

Assignment · Baseline Modeling

Deliverable · Baseline Benchmark Report

Class 10 · Deep Learning & Computer Vision for Research

Assignment · Deep Learning / CV Experiment

Deliverable · Working DL/CV Pipeline + Initial Results

Class 11 · Model Optimization & Generalization

Assignment · Model Optimization Study

Deliverable · Optimization Report

Class 12 · Transformers & Hugging Face

Assignment · Transformer Experiment

Deliverable · Transformer Experiment Report

Class 13 · Foundation Models & Research Applications

Assignment · Foundation Model Research Study

Deliverable · Foundation Model Evaluation Report

Class 14 · Research Project Setup

Assignment · Research Project Proposal

Deliverable · Research Project Proposal

Class 15 · Research Pipeline Implementation

Assignment · Research Pipeline Implementation

Deliverable · Working Research Pipeline

Class 16 · Research Experiments

Assignment · Experimental Study

Deliverable · Research Experiment Package

Class 17 · From Research Gap to Proposed Model

Assignment · Proposed Model Design

Deliverable · Proposed Architecture + Methodology

Class 18 · Proposed Model Implementation

Assignment · Proposed Model Implementation

Deliverable · Working Proposed Model

Class 19 · Evaluation Metrics & Experimental Analysis

Assignment · Evaluation Strategy

Deliverable · Evaluation Framework + Results Table

Class 20 · Reliability, Validation & Error Analysis

24 classes. 6 months.
One complete research journey.

AI & Machine Learning Research Bootcamp