5 Modules
4.8 Rating
30 Months
Course Overview
Here’s a timeline-based roadmap for 10 AI/ML courses, showing how long each stage typically takes before you’re job-ready. This assumes a serious learner investing ~10–12 hours per week.
Course Curriculum
- 01•Data Science & Machine Learning Bootcamp (3 months) - Build Python, statistics, ML basics.
- 02•Applied AI for Business Analytics (3 months) - Learn dashboards, predictive analytics, business insights.
- 03•Outcome: Ready for entry-level analyst/ML intern roles.
- 01•Deep Learning Specialization (3 months) - Neural networks, CNNs, RNNs.
- 02•Natural Language Processing (NLP) with Transformers (2 months) - Chatbots, LLM fine-tuning.
- 03•Computer Vision & Image Recognition (1 month) - Object detection, image classification.
- 04•Outcome: Competitive for AI Engineer / Data Scientist roles.
- 01•AI in Finance & Risk Modelling (2 months) - Fraud detection, credit scoring.
- 02•AI for Healthcare & Diagnostics (2 months) - Predictive diagnostics, medical imaging.
- 03•Outcome: Target domain-specific AI jobs (fintech, health-tech).
- 01•Generative AI & Prompt Engineering (2 months) - Text, image, multimodal generation.
- 02•MLOps & AI Deployment (4 months) - Model lifecycle, CI/CD, cloud deployment.
- 03•Outcome: Ready for production-level AI roles (MLOps Engineer, Applied Scientist).
- 01•Ethical AI & Responsible Innovation (6 months, part-time) - Bias detection, fairness, compliance.
- 02•Outcome: Positioned for senior roles (AI Lead, Responsible AI Specialist).
Certification & Job Support
- Certificate of Achievement
- 100% Placement Assistance