ARTIFICIAL INTELLIGENCE
₹4130.00 ₹7500.00 45% OFF
Course Overview
Duration: 2 Months
Format: Recorded Training & Hands-On Projects
Month 1: Training with Industry Mentors
- Objective: Gain foundational knowledge and insights from experienced professionals.
- Format: A combination of live sessions and recorded content, providing flexibility for participants.
Key Components:
- Recorded Content: Access to pre-recorded sessions for flexible learning at your own pace.
- Topics Covered: Current industry trends, best practices, technical skills, and soft skills development.
Month 2: Hands-On Project Experience
- Objective: Apply learned concepts through practical experience.
Key Components:
- Live Research and Development Projects: Participants will engage in both minor and major projects, working on real-world challenges.
- Collaboration: Work in teams to foster collaboration, problem-solving, and innovation.
- Mentorship: Continuous support and guidance from industry mentors throughout the project phase.
Certification
Upon successful completion of the program, participants will receive:
- Course Completion Certificate: Recognizing the completion of the training and project components.
- Collaborated Internship Completion Certificate: Validating the hands-on experience gained during the program.
Schedule of Classes
Course Curriculum
1 Subject
ARTIFICIAL INTELLIGENCE
23 Learning Materials
Introduction AI and Python Essentials
Introduction AI and Python Essentials Part-1
Introduction AI and Python Essentials Part-2
Introduction AI and Python Essentials Part-3
Introduction AI and Python Essentials Part-4
Introduction AI and Python Essentials Part-5
Introduction AI and Python Essentials Part-6
Module Notes
Problem Solving Agents And Search Alorithms
Problem Solving Agents And Search Alorithms Part-1
Problem Solving Agents And Search Alorithms Part-2
Problem Solving Agents And Search Alorithms Part-3
Problem Solving Agents And Search Alorithms Part-4
Problem Solving Agents And Search Alorithms Part-5
Module-2 Notes
Rule Based Systems & Traditional Machhine Learning
Rule Based Systems and Traditional Machine Learning Part-1
Rule Based Systems and Traditional Machine Learning Part-2
Rule Based Systems and Traditional Machine Learning Part-3
Rule Based Systems and Traditional Machine Learning Part-4
Rule Based Systems and Traditional Machine Learning Part-5
Rule Based Systems and Traditional Machine Learning Part-6
Module-3 Notes
Module-3 Resource Code
Unsupervised Learning And Probabilistic Reasonong
Unsupervised Learning And Probabilistic Reasonong Part-1
Unsupervised Learning And Probabilistic Reasonong Part-2
Course Instructor
SmartED Innovations
39 Courses • 12574 Students
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