Online/ e-LMS
Mentor Based
Beginners
5 weeks
About
TensorFlow – Use in AI is a comprehensive course tailored for M.Tech, M.Sc, and MCA students, as well as professionals in the fields of IT, BFSI, consulting, and fintech. The course covers foundational concepts to advanced applications of TensorFlow in AI, emphasizing hands-on learning and real-world problem-solving.
Aim
This course aims to provide a deep understanding of TensorFlow, equipping participants with the skills to build, train, and deploy sophisticated machine learning models using TensorFlow.
Program Objectives
- Comprehensive TensorFlow Mastery: In-depth knowledge of TensorFlow and its capabilities in AI development.
- Practical Application Skills: Hands-on experience in building, training, and deploying AI models with TensorFlow.
- Innovation in AI: Skills to innovate and solve complex problems using TensorFlow in various industry contexts.
Program Structure
- Introduction to TensorFlow:
- Understanding TensorFlow’s role in AI, setting up the environment, and basic programming.
- Basic Concepts in TensorFlow:
- Exploring core components like tensors and data types, and building simple neural networks.
- Advanced TensorFlow:
- Advanced model architectures like CNNs and RNNs, debugging, and optimization techniques.
- TensorFlow in Production:
- Strategies for deploying TensorFlow models in cloud and edge environments, monitoring, and maintenance.
- Real-World Applications:
- Exploring TensorFlow’s applications in various industries like healthcare and retail, and innovative uses in fields like art and music.
- Project and Implementation:
- Designing and implementing a TensorFlow project tailored to specific industry needs, from conception to deployment.
Participant’s Eligibility
- Students and professionals in M.Tech, M.Sc, and MCA, especially those in IT, BFSI, consulting, and fintech sectors.
- Anyone interested in advancing their knowledge and skills in AI with TensorFlow.
Program Outcomes
- Advanced TensorFlow Skills: Ability to utilize TensorFlow for advanced AI model development and deployment.
- Strategic Implementation: Skills to strategically deploy TensorFlow models to solve real-world problems.
- Leadership in AI: Enhanced capabilities to lead AI projects and initiatives using TensorFlow.
Fee Structure
Standard Fee: INR 7,998 USD 118
Discounted Fee: INR 3,999 USD 59
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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Key Takeaways
Program Assessment
Certification to this program will be based on the evaluation of following assignment (s)/ examinations:
| Exam | Weightage |
|---|---|
| Mid Term Assignments | 50 % |
| Project Report Submission (Includes Mandatory Paper Publication) | 50 % |
To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Placement Assistance
- Industry Partnerships: Collaboration with leading companies using TensorFlow, providing insights and potential career opportunities.
- Career Development: Workshops and guidance on career paths in AI development using TensorFlow.
- Networking Events: Opportunities to engage with industry experts and fellow AI professionals.
Future Career Prospects
- AI Engineer: Specializing in building and deploying AI models using TensorFlow.
- Data Scientist: Leveraging TensorFlow for complex data analysis and machine learning solutions.
- AI Product Manager: Overseeing AI product development and implementation strategies using TensorFlow.
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