Deep Learning and Al Engineering Bootcamp | Barcelona Code School -Ivanooo

Accrediations

Barcelona Code School

Durartion of the Course

9 Weeks

Course Fee

$4,917

Mode of Learning

Self-Paced

What is The Course All About?

The Deep Learning and Al Engineering Bootcamp provided by Barcelona Code School is an online Artificial Intelligence course meant for building one’s expertise in Artificial Intelligence.

Who is the Course Meant For?

Being an advanced-level course, the Deep Learning and Al Engineering Bootcamp is an ideal entry-level course for learners like:

Professionals who want to upskill themselves in their existing skillset.
Any individuals looking for a new career in Artificial Intelligence or who want to upskill themselves in the same.

Skills: What does this program cover?

Depth and Breadth of Topics
ⓘ
- Range of topics covered. - Number of topics, comprehensiveness (introductory to advanced)
- Theoretical vs. practical balance
- Alignment with industry standards and current trends
Not Applicable
Case Studies
ⓘ
- Number and variety of case studies
- Relevance of case studies to the course topic and target audience
Not Applicable
Tools
ⓘ
- Use of industrystandard tools and technologies
- Handson experience with tools
Not Applicable
Capstone Projects/Real World Application
ⓘ
- Presence of a project or practical application
- Complexity and scope of the project
Not Applicable

Skills: NaN /30

Faculties: Who are the experts behind the course?

Academic/Industry Exp.
ⓘ
- Educational background
- Work experience in relevant industries
Not Applicable
Teaching Experience
ⓘ
- Experience teaching similar courses or topics
- Teaching style and approach (engaging, clear, accessible)
Not Applicable
Industry Recognition
ⓘ
- Awards and recognition in the field
- Contributions to opensource projects, communities, or publications
Not Applicable

Faculties: 0 /20

Delivery Methods: How Is the program delivered?

Live Interaction
ⓘ
- Availability of live lectures, Q&A sessions, or webinars
- Provide direct interaction and realtime clarification of concepts
- Quality and effectiveness of live interactions
Not Applicable
Self-Paced
ⓘ
- Flexibility in scheduling and pacing
- Prerecorded videos covering lectures, tutorials, demonstrations, and supplemental content, allowing students to engage with course material flexibly.
- Quality and availability of selfpaced materials (videos, readings, exercises)
Not Applicable
Assessment
ⓘ
- Variety of assessment types (quizzes, assignments, projects)
- Alignment of assessments with learning objectives
- Quality and timeliness of feedback on assessments
Not Applicable
Community
ⓘ
- Presence and activity of online forums, discussion boards, or other community spaces
- Level of engagement and support from instructors and moderators
Not Applicable
Collaborative
ⓘ
- Opportunities for group projects, peer review, or other collaborative activities
Not Applicable

Delivery Methods: 0 /30

Career Assistance: How does the program support career growth?

Mentoring
ⓘ
- Availability of personalized guidance and support from mentors or career coaches
Not Applicable
Personalized Assistance
ⓘ
- Availability of career Assessments (personality, skills, interests) to help learners understand their strengths and potential career paths
Not Applicable
Alumni Network
ⓘ
- Active alumni network for networking
Not Applicable
Industry Partnerships
ⓘ
- Partnerships with companies
Not Applicable

Career Assistance: 0 /15

Student Satisfaction and Feedback: What Do Students Say About the Course?

Feedback & Improvement
ⓘ
- Quantity and quality of student reviews
- Responsiveness of course providers to feedback
- Evidence of continuous improvement in the course content and delivery
Not Applicable

Alumini Feedback: 0 /5

Benchmark Labels:

Strong (75-100%): This indicates the course demonstrates exceptional strengths in this area, exceeding expectations.

Moderate (50-74%): This signifies the course performs adequately in this area, covering essential aspects.

Limited (25-49%): This highlights areas where the course could significantly improve to provide a more comprehensive learning experience.

Very Limited (1-24%): This indicates that coverage in this area may be minimal, or even non-existent.

Not Applicable: This indicates that this criteria is not applicable for this course.