PBC-ML · Specialty
Machine Learning — Specialty
Build and deploy models that work in production. From notebooks to MLOps.
Duration
100h self-paced
Exam
65 Qs · 150min
Pass mark
75%
What you'll learn
Supervised and unsupervised methods, feature engineering, model evaluation, deep learning fundamentals, deployment, monitoring, and drift detection. Original applied project required: deploy a working model with monitoring on a chosen problem.
Course modules
- 01~750min
ML foundations — what ML actually is, when it fits, when it doesn't
Machine learning is widely misunderstood — both overhyped and underestimated. This module covers what ML actually is, the major paradigms (supervised, unsupervised, reinforcement learning), when ML is the right tool vs when traditional code is better, and the senior ML engineer's mindset.
- 02~750min
The data layer — data quality, feature engineering, leakage, splits
Most ML projects succeed or fail based on data quality, not model choice. This module covers data validation, feature engineering, train/validation/test splits done correctly, data leakage (the silent killer), label quality, and the unsexy reality of professional ML work.
- 03~750min
Classical ML — regression, trees, ensembles, when they beat deep learning
Classical ML still dominates production. This module covers linear models (regression, logistic regression), decision trees, ensemble methods (Random Forests, Gradient Boosting), when classical methods beat deep learning, and the toolkit senior ML engineers reach for first.
- 04~750min
Deep learning — neural networks, training, transformers
Deep learning powers modern AI. This module covers neural network fundamentals, training (loss functions, backpropagation, optimizers), regularization (dropout, batch norm), modern architectures (CNNs, RNNs, transformers), and the deep learning toolkit senior engineers know.
- 05~750min
ML in production — MLOps, serving, monitoring, retraining
Most ML models never reach production. This module covers MLOps fundamentals: model serving (batch vs online), monitoring (data drift, model drift, performance), retraining pipelines, model versioning, A/B testing, and the production discipline that separates senior ML engineers.
- 06~750min
LLMs and generative AI — fine-tuning, RAG, agents, prompt engineering
LLMs reshaped AI in 2023-2026. This module covers LLM fundamentals, prompt engineering, retrieval-augmented generation (RAG), fine-tuning, agents and tool use, evaluation, and the production patterns for LLM-powered applications.
- 07~750min
Evaluation — metrics, A/B testing, bias, fairness, responsible AI
Models that look good in evaluation often fail in production. This module covers evaluation rigor: choosing right metrics, A/B testing methodology, bias and fairness, calibration, responsible AI practices, and the discipline that prevents shipping broken models.
- 08~750min
Career paths and continuous learning for ML engineers
ML offers diverse career paths beyond 'ML Engineer.' This module covers specializations, career progression, building a portfolio, getting hired at top AI labs, the PhD-or-not question, staying current in a fast-moving field, and the long-term ML career.
What you walk away with
Not just a certificate. A career-grade credential built to open doors.
A credential employers verify in one click
Unique cert ID, QR code, and tamper-evident signature. No more wondering if your certificate is taken seriously.
A portfolio piece, not just a paper
Your capstone project lives on your public PBV portfolio. Recruiters click and see actual work — that beats a cert badge every time.
Lifetime access, no expiry
Once you earn it, it's yours. No renewals, no subscriptions, no surprises.
A profile that stands out
Verified PBV credentials show up on your PBridge freelancer profile, putting you ahead of unverified candidates in client searches.
Why PBC works
- ✓USD pricing, transparent and global. Pay in USD, accepted globally.
- ✓Built for the global context. Case studies and capstone briefs use real-world business scenarios.
- ✓Reviewed capstone project. A human reviews your final project.
- ✓Verifiable digital certificate. QR code and cryptographic hash for one-scan verification.
- ✓PBridge job board access. Featured in front of employers hiring on PBridge.
Machine Learning certification FAQs
How much does the Machine Learning certification cost?+
The PBridge Certified Machine Learning certification costs $249 for the exam-only path and $399 for the full path which includes the capstone project review. Both prices are in US Dollars. Payment via secure international checkout (Visa, Mastercard, Amex).
Is the PBridge Certified Machine Learning certification recognized internationally?+
Yes. PBridge Certified certifications are issued with verifiable digital credentials including a unique cert ID, QR code, and hash signature. Hiring managers anywhere in the world can verify any cert at https://www.pbridgeco.com/verify/{cert-id}.
How long does it take to complete the Machine Learning certification?+
The Machine Learning certification takes approximately 100 hours of self-paced study. The exam itself is 150 minutes with 65 questions. Most candidates complete the full path including the capstone project in 4-8 weeks studying part-time alongside work.
What makes PBridge Certified the right fit for learners and employers globally?+
PBridge Certified is built for the global market — USD pricing, capstone scenarios drawn from real-world business cases, international hiring manager recognition, and direct integration with the PBridge job board so certified holders get featured in front of employers actively hiring.
Can I get a job after the Machine Learning certification?+
PBridge Certified cert holders get priority access to the PBridge job board where global and remote employers post machine learning roles. Pair the certification with the capstone project as a portfolio piece — most certified holders see callbacks within 30 days.