Ivanooo

Certification

Upgrad

Durartion of the Course

16 Weeks

Course Fee

$6180

Mode of Learning

Self-Paced

What is The Course All About?

The Fundamentals of Deep Learning of Neural Networks course provided by Upgrad 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 Fundamentals of Deep Learning of Neural Networks course 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
Grade
0%Very Limited

No specific reasons provided.

Case Studies/Real World Events

  • Number and variety of case studies
  • Relevance of case studies to the course topic and target audience
Grade
0%Very Limited

No specific reasons provided.

Tools

  • Use of industrystandard tools and technologies
  • Handson experience with tools
Grade
0%Very Limited

No specific reasons provided.

Hands-on Projects and Applications

  • Presence of a project or practical application
  • Complexity and scope of the project
Grade
0%Very Limited

No specific reasons provided.

Faculties: Who are the experts behind the course?

Academic/Industry Exp.

  • Educational background
  • Work experience in relevant industries
Grade
0%Very Limited

No specific reasons provided.

Faculty Involvement

  • Evaluates the active role and contribution of all individuals—faculty, teaching assistants, mentors, and support staff—who directly support, guide, or enhance the learning experience throughout the course.
Grade
0%Very Limited

No specific reasons provided.

Industry Recognition

  • Awards and recognition in the field
  • Contributions to opensource projects, communities, or publications
Grade
0%Very Limited

No specific reasons provided.

Delivery Methods: How Is the program delivered?

Active Interaction

  • Availability of live lectures, Q&A sessions, or webinars
  • Provide direct interaction and realtime clarification of concepts
  • Quality and effectiveness of live interactions
Grade
0%Very Limited

No specific reasons provided.

Self-paced/Flexible Resources

  • 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)
Grade
0%Very Limited

No specific reasons provided.

Assessment

  • Variety of assessment types (quizzes, assignments, projects)
  • Alignment of assessments with learning objectives
  • Quality and timeliness of feedback on assessments
Grade
0%Very Limited

No specific reasons provided.

Community

  • Presence and activity of online forums, discussion boards, or other community spaces
  • Level of engagement and support from instructors and moderators
Grade
0%Very Limited

No specific reasons provided.

Collaborative

  • Opportunities for group projects, peer review, or other collaborative activities
Grade
0%Very Limited

No specific reasons provided.

Career Assistance: How does the program support career growth?

Mentoring

  • Availability of personalized guidance and support from mentors or career coaches
Grade
0%Very Limited

No specific reasons provided.

Personalized Assistance

  • Availability of career Assessments (personality, skills, interests) to help learners understand their strengths and potential career paths
Grade
0%Very Limited

No specific reasons provided.

Alumni Network

  • Active alumni network for networking
Grade
0%Very Limited

No specific reasons provided.

Industry Partnerships

  • Partnerships with companies
Grade
0%Very Limited

No specific reasons provided.

Deep-Dive/Skin in the Game

  • Aligns with how humans build skills—through real-world action, not passive theory
  • Encourages simultaneous learning and doing, with no delay between concept and practice
  • Prioritizes real-time feedback in immersive, high-engagement settings.
  • Measures how much learners are made to commit, apply, and perform—putting real skin in the game.
Grade
0%Very Limited

No specific reasons provided.

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 (0-24%): This indicates that coverage in this area may be minimal, or even non-existent.

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