About This Program
Become job-ready in just 1 month with intensive AI & ML training using Python. Gain hands-on experience with projects in computer vision, NLP, and deployment while preparing for interviews with expert mentorship.
Course Overview
This 1-month intensive program equips participants with strong AI & ML skills using Python, covering machine learning, deep learning, NLP, computer vision, and deployment. Students build 3 end-to-end projects, gain portfolio-ready experience, and receive mentorship and interview preparation support.
What You Will Learn
Hands-on Projects
Apply your learning through real-world projects that build your portfolio
Unique method to make you professionally rich
Progress through our comprehensive program which makes qualified professional to earn their awards
Bronze Award
Begin your journey with foundational skills and basic certification.
Course Curriculum
Technologies You Will Learn
Master the most in-demand tools and technologies for data analytics
Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras)
SQL & Databases (SQLite/PostgreSQL)
Data Visualization (Matplotlib, Seaborn)
Deep Learning (CNNs, RNNs, Transformers)
NLP (NLTK, spaCy, BERT, GPT basics)
Computer Vision (OpenCV, CNNs)
Cloud Platforms (AWS/GCP, Docker, Flask/FastAPI)
Git & GitHub (version control, collaboration)
What Our Students Say
Hear from our students who have transformed their careers
Why Choose Edjamon?
Discover the difference between Edjamon and traditional platforms
| Features & Criteria | EdJAMON | Others |
|---|---|---|
Duration | 1 Month (intensive, project-focused, job-ready) | 6–12 Months (longer, less focused) |
Learning Format | Live Classes + 1:1 Mentor Support + Real Projects | Mostly self-paced or generic recorded videos |
Python & SQL | Hands-on coding from Day 1 (Python, Pandas, SQL, NumPy, Scikit-learn) | Limited practice, more theory-heavy |
Statistics & Machine Learning | Covers supervised & unsupervised learning, ML algorithms, evaluation, hyperparameter tuning | Covers basics, often lacks real-world application depth |
Deep Learning & AI | Neural Networks, CNNs, RNNs, Transformers, NLP, Computer Vision, Generative AI basics | Often limited to introductory DL, no advanced NLP/Transformer coverage |
Visualization Tools | Matplotlib, Seaborn + integrated visual reporting in projects | Focus on one tool only (Excel OR Tableau) |
Capstone Projects | 3 End-to-end projects (EDA → ML/DL → Deployment) | Case studies or mini projects only |
Cloud & Deployment | Flask / FastAPI, Docker basics, AWS/GCP deployment, model monitoring | Rarely included or only optional |
Version Control & Tools | Git/GitHub, Jupyter, VS Code/PyCharm, Linux basics | Limited or skipped entirely |
Mentorship & Career | Weekly code reviews, 1:1 mentoring, mock interviews, resume prep | Very limited or none |
Job Roles After Program
Explore exciting career opportunities
Machine Learning Engineer
AI Engineer
Data Scientist
NLP Engineer
Computer Vision Engineer
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Resources & References
Access curated external resources to deepen your understanding
LLMs & Generative AI
Official guide for GPT models and API usage
Pre-trained models, datasets, and community resources
Open-source LLaMA models and implementation guides
Claude AI research papers and documentation
RAG & Framework Docs
Complete guide to building LLM applications
Data indexing framework for LLM applications
Step-by-step RAG implementation
Efficient open-source LLM models
Vector Databases
Cloud-native vector database for AI applications
Facebook's efficient similarity search library
Open-source ML-first vector database
Open-source embeddings database
Best Practices & Learning
OpenAI's official prompt engineering best practices
Academic foundation for RAG architectures
Real-world RAG implementation patterns
Security and ethical considerations for AI apps
Deployment & DevOps
Containerization and deployment guide
CI/CD automation and workflow automation
Build data apps with simple Python scripts
Cloud deployment and infrastructure setup
Python & Development
Complete Python language reference
Modern Python web framework for APIs
Numerical computing with Python
Data manipulation and analysis library
Frequently Asked Questions
Find answers to common questions