Training: Mastering Dlib: Facial Recognition and Machine Learning in Python

Specialist course · Minimum level required

$3,150

  • 2 days
  • Onsite
  • 8 people max.
Ref: IAV149
8 people max.
$3,150
In-company rate: from $5,723 for the whole teamExpertise rate: $1,575 per day, reflecting the difficulty level
On-site and certification exam on quote
2 days
Onsite

Don't let this gap widen

Why this program matters

  • Without mastery of Dlib for facial recognition and machine learning in Python, your algorithms produce critical false positives that compromise application reliability.

  • A company loses an average of 45 000 € per failed project, with delays of 3 to 6 months due to inefficient implementations.

  • 70 % of biometric security incidents are linked to poor optimization of facial landmarks, exposing firms to GDPR fines of up to 4 % of revenue.

  • Every quarter without advanced expertise, your team misses AI contracts valued at more than 100 000 €, threatening your competitiveness and your career.

Allan BUSI, Learni trainer · AI expert
Allan BUSI

Learni trainer · AI expert

73%productivity gap
×3cost of inaction
Flag of the United StatesBuilt for US teams
Payment by wire transferEnhanced confidentialityPerfect for companies with 50 to 500 employeesScheduling to fit any time zone

Trusted by — schools, companies and industrial groups

41,091 programs · 25 countries

  • Aptar
  • ArcelorMittal
  • Equans
  • EDF
  • Ubisoft
  • Microsoft

Every program built with a dedicated engineer

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Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft and Microsoft.

Dlib training objectives

  • Understand the theoretical concepts behind Dlib and facial recognition
  • Efficiently install and configure Dlib in a Python environment
  • Use Dlib to detect and recognize faces in images and videos
  • Explore facial landmark extraction and their practical applications
  • Script complete facial recognition pipelines with database management
  • Optimize and evaluate the performance of Dlib-based models

Program

Dlib curriculum: 3 modules over 2 days

Module 1 : Introduction to Dlib, Installation and Getting Started

General presentation of Dlib, use cases, comparison with OpenCV. Installing Dlib on Windows/Linux/Mac, managing dependencies (Boost, cmake, etc.). Getting started with the Python API. Reading and displaying images, first script examples.

Module 2 : Face Detection and Facial Keypoint Extraction

Presentation of Dlib's face detection algorithms (HOG, CNN). Using the default face detector. Extraction and visualization of facial landmarks (keypoints), analysis of face geometry. Practical exercises on images and video.

Module 3 : Facial Recognition, Database Management and Optimizations

Creating facial recognition applications. Using Dlib's embedding (dlib.face_recognition_model_v1), generating and comparing face encodings. Structuring face databases. Performance optimization, real-time pipeline management, result evaluation, best practices and limitations. Presentation of concrete projects and supervised mini-project.

How Dlib skills are assessed

  • Practical case studies throughout the training
  • Final project implementing a facial recognition system
  • Personalized correction and feedback

How the Dlib course is taught

  • Illustrated theoretical presentations
  • Live coding demonstrations
  • Supervised hands-on exercises
  • Provision of datasets
  • Complete course materials and references provided

Dlib training: access and practical arrangements

The Training: Mastering Dlib: Facial Recognition and Machine Learning in Python program is delivered onsite or remote (blended-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.

The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.

Equipment required

For the smooth delivery of the Training: Mastering Dlib: Facial Recognition and Machine Learning in Python program, the following equipment is required:

  • Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
  • Training environments installed on workstations or accessible online
  • Course materials, hands-on exercises and complementary resources
  • Post-training access to materials and educational resources

For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.

* contact us for remote delivery feasibility** ratio varies depending on the program

Skills assessment methods

Assessment of skills acquired during the Training: Mastering Dlib: Facial Recognition and Machine Learning in Python program is performed through:

  • During training: case studies, hands-on labs and professional scenarios
  • End of training: self-assessment questionnaire and skills evaluation by the trainer
  • After training: completion certificate detailing acquired skills

Program accessibility

Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.

Enrollment terms and lead times

Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.

The Learni story

Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.

Our method

How Learni guarantees the quality of your Dlib training

Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.

Step 1

Rigorous trainer selection

Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.

  • Triple validation: technical, pedagogical, sectoral.
  • Minimum rating 4.8/5 over the last 12 sessions.
Step 2

You meet the trainer beforehand

30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.

  • Live briefing on goals and team context.
  • Veto right — we swap the trainer for free if needed.
Step 3

Content tailored to your context

No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.

  • Hands-on cases drawn from your stack and projects.
  • Program co-written then validated by your team.
Step 4

Continuous quality follow-up

Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.

  • NPS, knowledge quizzes and skills self-assessment.
  • Satisfaction guarantee: fully satisfied or free rework.

A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.

A child walking to school with a backpack
Our social commitment

A school kit donated to a child for every training booked

To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.

  • Backpack, notebooks and essential supplies
  • Distributed through our partner charities
  • Included, at no extra cost to you
Enroll and offer a kit
FAQ

Dlib: frequently asked questions

What can participants do after Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training?+
They can understand the theoretical concepts behind Dlib and facial recognition, efficiently install and configure Dlib in a Python environment and use Dlib to detect and recognize faces in images and videos. Those are the stated objectives of the Training: Mastering Dlib: Facial Recognition and Machine Learning in Python program at Advanced level, checked by the end-of-session assessment.
What does the Training: Mastering Dlib: Facial Recognition and Machine Learning in Python program cover, day by day?+
Day 1: Introduction to Dlib, Installation and Getting Started. Day 2: Face Detection and Facial Keypoint Extraction. Day 3: Facial Recognition, Database Management and Optimizations. The Training: Mastering Dlib: Facial Recognition and Machine Learning in Python program runs over 2 days, delivered on site.
How is Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training assessed?+
Assessment relies on practical case studies throughout the training, final project implementing a facial recognition system and personalized correction and feedback. Learni issues a completion certificate together with the individual assessment report at the end of the Training: Mastering Dlib: Facial Recognition and Machine Learning in Python program, which an L&D team can file as evidence.
How much does Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training cost per participant?+
Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training costs $3,150 per participant. For an in-company team, the flat rate starts at $5,723 for the whole group, up to 8 people. The detailed quote, sent within one business day, adjusts the amount to the format and how far the syllabus is tailored to your team.
How long does Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training take?+
2 days, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.
Who pays for Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training?+
Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
What do participants need to know before Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training?+
Basic Python programming skills and fundamental machine learning concepts.
Is a certificate issued after Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.
Does Learni provide the equipment for Training: Mastering Dlib: Facial Recognition and Machine Learning in Python training?+
No. A computer and a stable internet connection are required for each participant. Learni provides the learning platform, the trainer and all course materials.
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