Professional Certificate in AI and Machine Learning
Master AI and machine learning with production-ready projects and mentor support.
- Train and evaluate supervised and unsupervised models
- Implement deep learning models with TensorFlow and PyTorch
- Apply NLP and computer vision techniques to real datasets
- Fine-tune LLMs and design safe prompting workflows
- Deploy models with containers and basic MLOps pipelines
- Present business impact narratives for AI initiatives
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Course Overview
A comprehensive program covering Python, deep learning, NLP, computer vision, and generative AI. Work on 12+ hands-on projects—from data pipelines to model deployment—and earn a professional certificate designed for ML engineer and AI engineer career paths.
Key Features
100% Money Back Guarantee- 12+ hands-on projects across classification, NLP, CV, and GenAI
- TensorFlow and PyTorch labs with GPU-backed notebooks
- MLOps modules covering Docker, CI/CD, and model monitoring
- Capstone with deployable model and portfolio documentation
- Career coaching for ML engineer and data science interviews
Skills Covered
- Python for Data Science
- NumPy, Pandas, and Scikit-learn
- TensorFlow and Keras
- PyTorch Fundamentals
- Deep Learning and Neural Networks
- Natural Language Processing
- Computer Vision
- Generative AI and LLMs
- Prompt Engineering
- Model Evaluation and Tuning
- MLOps and Model Deployment
- Feature Engineering
- Experiment Tracking
- Responsible AI Practices
Benefits
AI and ML professionals are among the fastest-growing technical roles, with strong demand for production deployment skills—not just model training.
Source: Glassdoor
Source: Indeed
Training Options
Online Bootcamp
- Live classes with industry-certified trainers
- Flexible weekend and weekday batches
- Peer learning groups and mentor office hours
- Mock exams, capstone projects, and job-ready templates
Batch starting from:
Corporate Training
- Tailored curriculum aligned to your delivery methodology
- Dedicated account manager and progress reporting
- On-site, virtual, or blended delivery options
- Volume pricing and flexible billing for enterprises
Course Curriculum
Eligibility
This intermediate program assumes comfort with programming and basic statistics. Structured refreshers are included for Python and linear algebra essentials.
Recommended background
- — 6+ months of Python or similar programming experience
- — Familiarity with basic statistics and linear algebra
- — Comfort working in Jupyter or notebook environments
Also suitable for
- — Software engineers transitioning into ML engineering
- — Analysts expanding from BI into predictive modeling
- — STEM graduates targeting AI engineer roles
Pre-requisites
You should be comfortable writing Python functions and working with data structures. Prior exposure to SQL or analytics helps but is not mandatory. Expect 10–12 hours per week for live sessions, projects, and capstone work over six months.
Course Content
Exam & Certification
This program awards a Hanux Professional Certificate based on project rubrics and capstone review rather than a single vendor exam.
