CIOL Presents Winter ML Bootcamp
CIOL Winter ML Bootcamp is an advanced technical workshop designed for CIOL members and external participants. This month long bootcamp focuses on practical machine learning (ML) applications, assignments, and research-oriented projects. Participants will gain hands-on experience, contribute to publishable research, and build a strong GitHub portfolio.
More details CIOL Winter ML Bootcamp | Registration (Closed)
Resources: Youtube Playlist | GitHub Repository
Event Details
- Event Name: CIOL Winter ML Bootcamp
- Prerequisite: Basic knowledge of Python, including input, variable handling, lists, dictionaries, functions, and file operations.
- Schedule:
- Date26 December, 2024 to 06 February, 2025
- Duration per Class: 1.5–2.5 hours
- Total Class Time: ~16–20 hours
- Assignment Time: ~20–30 hours
- Platform: Zoom + Recorded
Session Plan
- Exploratory Data Analysis (EDA): Data cleaning and visualization (Assignment: Perform EDA on a dataset)
- Tabular Data Model Training: Building and evaluating models (Assignment: Train and evaluate a model)
- Hyperparameter Tuning: Optimize and select the best model (Assignment: Tune models)
- Pre-trained Models for Text: NLP tasks using BERT and variants (Assignment: Text classification)
- Pre-trained Models for Images: Using CNNs and ViT (Assignment: Image classification)
- LLM Agents: Develop a AI Agent with CrewAI and Ollama in Colab (Assignment: Develop your own Agent)
- Explainable AI (XAI): Interpreting ML models (Assignment: Generate explanations for predictions)
- Research (Problem to Code): From research problems to actionable code
Instructors
Sessions Materials and Recordings
| Session Title | Instructor Name | Recording (Youtube URL) | Session Materials (GitHub URL) |
|---|---|---|---|
| Orientation | - | Youtube | - |
| Session 1: Exploratory Data Analysis (EDA) | Azmine Toushik Wasi | Youtube URL | Materials |
| Session 2: Tabular Data Model Training | Azmine Toushik Wasi | Youtube | Materials |
| Session 3: Hyperparameter Tuning and Model Selection | Azmine Toushik Wasi | Youtube | Materials |
| Session 3 - Bonus: Building Artificial Neural Networks | Azmine Toushik Wasi | Youtube | Materials |
| Session 4: Pre-trained Models for Text | Sheikh Ayatur Rahman | Youtube | Materials |
| Research and Writing: How To Write Shared Task Papers | Azmine Toushik Wasi | Youtube | - |
| Session 5: Pre-trained Models for Images | Azmine Toushik Wasi | Youtube | Materials |
| Session 6: LLM Agents | Azmine Toushik Wasi | Youtube | Materials |
| Session 7: Explainable AI (XAI) | MD Shafiqul Islam | Youtube | Materials | >
| Session 8: Research (Problem to Code) | MD Shafiqul Islam | Youtube | Materials |
Successful Participants
- LIVE Class Participants:
- Certificate of Excellence: Fairuz Nawar Fatema, Mahfuz Ahmed Anik, Minhaz Chowdhury
- Certificate of Appreciation: Abdullah Al Nahian Abir, Abdur Rahman, Arun Chandra Barmon, Fairuz Nawar Fatema, Ikramul Haque Iban, Mahfuz Ahmed Anik, Minhaz Chowdhury, Rahatun Nesa Priti, Rahul Dev Sharma, Tawfia Yeasmin, Tirtha Debnath
- Certificate of Research Participation: Abdullah Al Nahian Abir, Abdur Rahman, Anisha Ahmed, Arnab Laskar, Enjamamul Haque Eram, Fairuz Nawar Fatema, Ikramul Haque Iban, Khan Shariya Hasan Upoma, Mahfuz Ahmed Anik, Minhaz Chowdhury, Mst. Rafia Islam, Rahatun Nesa Priti, Rahul Dev Sharma, Sabrina Afroz Mitu, Taj Ahmad Turjo, Tirtha Debnath, Wahid Faisal
- Certificate of Participation: Abdullah Al Nahian Abir, Abdur Rahman, Anisha Ahmed, Arnab Laskar, Arun Chandra Barmon, Enjamamul Haque Eram, Fairuz Nawar Fatema, Ikramul Haque Iban, Khan Shariya Hasan Upoma, Mahfuz Ahmed Anik, Mahir Absar Khan, Minhaz Chowdhury, Mst. Rafia Islam, Rahatun Nesa Priti, Rahul Dev Sharma, Resma Jerin Tumu, Sabrina Afroz Mitu, Sahedul Mustaquim, Tawfia Yeasmin, Tirtha Debnath
- Recorded Class Participants:
- Certificate of Completion: Abier Farzana Hoque, Md Nadim Rahman, Sanatan Sushil, Sudipta Sarkar.
- Certificate of Participation: Mohammed Asfaqul Alam Chowdhury, Nixon Deb Antu, Rifat Faruk Zitu, Samira Farzana, Shahoriar Muttaki Utshaw, Yak Safu
Bootcamp Resources
Bootcamp Report
CIOL Winter ML Bootcamp: A Journey into Advanced Machine Learning
CIOL's Winter ML Bootcamp marked a transformative experience for nearly 70 participants, offering a month-long immersion into the practical world of machine learning from December 26, 2024 to February 6, 2025. Designed for both CIOL members and external enthusiasts, the bootcamp delivered a rigorous blend of theory, hands-on assignments, and research-focused projects that left a significant impact on all involved.
The curriculum was meticulously structured to balance academic insights with real-world application. Participants engaged in sessions on Exploratory Data Analysis, model training and hyperparameter tuning, as well as the use of pre-trained models for text and image classification. Advanced topics, including the development of AI agents using CrewAI and Ollama in Colab and techniques in Explainable AI, provided participants with cutting-edge skills. Each module was accompanied by practical assignments that reinforced learning and encouraged the creation of robust GitHub portfolios.
The bootcamp also featured a dedicated three-week research and publication segment. This component was designed to help participants translate complex research problems into actionable code, with intensive mentorship that boosted coding proficiency and publication skills. An impressive 87.5% of research participants felt that the segment enhanced their publication capabilities, further emphasizing the program’s comprehensive nature.
Feedback from the bootcamp reflected its high quality across multiple dimensions. The overall experience received an average rating of 4.62 out of 5, with 62% of respondents awarding a perfect score. The hands-on coding experience was equally commendable, earning an average of 4.62/5, with 66.7% of participants rating it 5/5. The overall learning experience garnered an average rating of 4.43/5.
Instructors Azmine Toushik Wasi, MD Shafikul Islam, and Sheikh Ayatur Rahman were central to the bootcamp's success. Their deep expertise was recognized with an average rating of 4.76/5, and 81% of participants gave them a perfect score. Moreover, 90.4% of participants appreciated the clarity with which complex concepts were explained, while the approachability and responsiveness during Q&A sessions were rated at an average of 4.76/5, with 81% providing top marks.
The relevance and structure of the topics were also highly valued, achieving an average rating of 4.67/5 with 66.7% of respondents awarding a perfect score. Learning materials, including slides and interactive notebooks, were found to be helpful and easy to follow by 95.2% of participants. Notably, the likelihood to recommend the bootcamp was exceptionally high, with an average rating of 4.9/5 and 91% of respondents giving a 5/5.
Beyond the technical skills and knowledge, the bootcamp celebrated excellence among its participants by recognizing star performers and awarding certificates of appreciation to 17 individuals. The overall impact of the bootcamp is evident not only in the high ratings but also in the enthusiasm for future events, with 95.2% of participants expressing interest in upcoming workshops, including the anticipated CIOL Summer ML Bootcamp.
In summary, the CIOL Winter ML Bootcamp delivered a comprehensive learning journey that combined advanced technical content, hands-on practice, and meaningful research mentorship. With impressive feedback statistics underscoring the high quality of instruction and content, the bootcamp stands as a testament to CIOL's commitment to fostering advanced machine learning expertise and nurturing the next generation of tech professionals.