Machine Learning Certification Training - Self-Paced E-Learning Course
Learn ML at your own pace with HD video lectures, 500+ practice questions, downloadable cheat sheets and mentor support, fully aligned with Industry-aligned, capstone-based.
What's Inside the ML E-Learning Program
Self-Paced HD Video Library
On-demand access to full curriculum recordings, organised module by module.
Practice Tests with Explanations
Hundreds of exam-style questions with detailed rationales for every answer.
Downloadable Study Resources
PDF formula sheets, mind maps, glossaries and process charts to revise offline.
Mobile + Desktop Access
Learn on iOS, Android, tablet or laptop. Pick up exactly where you left off.
Mentor Support on Demand
Email and chat support from certified instructors for doubt resolution.
Certificate of Completion
Earn a verifiable Learners Ink certificate to attach to your application.
ML Self-Paced Curriculum
Modular content mapped to Industry-aligned, capstone-based. Watch on any device, revisit any chapter, anytime.
Module 1: Classical ML Foundations
- Linear, Ridge, Lasso, ElasticNet
- Polynomial features and splines
- Quantile and robust regression
- Diagnostics and assumptions
- Logistic Regression
- Decision Trees
Module 2: Unsupervised, Recommender and Time Series ML
- K-Means, DBSCAN, HDBSCAN
- Gaussian Mixture Models
- Hierarchical clustering
- Cluster evaluation
- PCA, Kernel PCA
- t-SNE, UMAP
Module 3: Deep Learning with PyTorch
- Forward and backprop
- Optimisers and schedulers
- Regularisation, dropout, batch norm
- Loss design
- ConvNets and pooling
- ResNet and EfficientNet
Module 4: Applied NLP, GenAI and Vision
- Tokenisation and embeddings
- Text classification
- Named entity recognition
- Semantic search
- LLM APIs and prompt engineering
- RAG with vector DBs
Module 5: MLOps, Responsible AI and Deployment
- FastAPI inference services
- Docker packaging
- GPU vs CPU serving
- Batch vs real-time
- GitHub Actions for ML
- DVC for data versioning
Module 6: ML Engineer Career Launch
- End-to-end system design framework
- Trade-offs and constraints
- Latency vs accuracy
- Production case studies
- ML coding interviews
- Math and statistics rounds
Who This Program Is For
- Aspiring ML Engineer
- Data Scientist
- Software Engineer
- Data Engineer
- Backend Engineer
- AI Researcher
What You'll Achieve
- Classical ML Mastery
- Pipelines and Reproducibility
- Deep Learning with PyTorch
- Applied GenAI for ML
- MLOps
- Advanced Evaluation
- Hyperparameter Optimisation
- Recommender and Ranking
Frequently Asked Questions
Start Your ML Journey Today
Instant access, 12 months to complete, mentor support throughout. Enrol and start learning in under 5 minutes.