Description
Certification Name: Certificate in AI & ML Professional
Course Id: CAIMLS/Q0001.
Eligibility: Graduation or Equivalent.
Objective: The Certified AI & ML Professional course is designed to equip participants with the knowledge and skills to design, develop, and implement artificial intelligence and machine learning solutions. The course covers foundational AI concepts, supervised and unsupervised learning, deep learning, neural networks, natural language processing, computer vision, and reinforcement learning.
Duration: Three Month.
How to Enroll and Get Certified in Your Chosen Course:
Step 1: Choose the course you wish to get certified in.
Step 2: Click on the “Enroll Now” button.
Step 3: Proceed with the enrollment process.
Step 4: Enter your billing details and continue to course fee payment.
Step 5: You will be redirected to the payment gateway. Pay the course and exam fee using one of the following methods:
Debit/Credit Card, Wallet, Paytm, Net Banking, UPI, or Google Pay.
Step 6: After successful payment, you will receive your study material login ID and password via email within 48 hours of fee payment.
Step 7: Once you complete the course, take the online examination.
Step 8: Upon passing the examination, you will receive:
• A soft copy (scanned) of your certificate via email within 7 days of examination.
• A hard copy (original with official seal and signature) sent to your address within 45 day of declaration of result.
Step 9: After certification, you will be offered job opportunities aligned with your area of interest.
Online Examination Detail:
Duration- 60 minutes.
No. of Questions- 30. (Multiple Choice Questions).
Maximum Marks- 100, Passing Marks- 40%.
There is no negative marking in this module.
| Marking System: | ||||||
| S.No. | No. of Questions | Marks Each Question | Total Marks | |||
| 1 | 10 | 5 | 50 | |||
| 2 | 5 | 4 | 20 | |||
| 3 | 5 | 3 | 15 | |||
| 4 | 5 | 2 | 10 | |||
| 5 | 5 | 1 | 5 | |||
| 30 | 100 | |||||
| How Students will be Graded: | ||||||
| S.No. | Marks | Grade | ||||
| 1 | 91-100 | O (Outstanding) | ||||
| 2 | 81-90 | A+ (Excellent) | ||||
| 3 | 71-80 | A (Very Good) | ||||
| 4 | 61-70 | B (Good) | ||||
| 5 | 51-60 | C (Average) | ||||
| 6 | 40-50 | P (Pass) | ||||
| 7 | 0-40 | F (Fail) | ||||
Key Benefits of Certification- Earning a professional certification not only validates your skills but also enhances your employability. Here are the major benefits you gain:
Practical, Job-Ready Skills – Our certifications are designed to equip you with real-world, hands-on skills that match current industry demands — helping you become employment-ready from day one.
Lifetime Validity – Your certification is valid for a lifetime — no renewals or expirations. It serves as a permanent proof of your skills and training.
Lifetime Certificate Verification – Employers and institutions can verify your certification anytime through a secure and reliable verification system — adding credibility to your qualifications.
Industry-Aligned Certification –All certifications are developed in consultation with industry experts to ensure that what you learn is current, relevant, and aligned with market needs.
Preferred by Employers – Candidates from ISO-certified institutes are often prioritized by recruiters due to their exposure to standardized, high-quality training.
Free Job Assistance Based on Your Career Interests – Receive personalized job assistance and career guidance in your preferred domain, helping you land the right role faster.
Assessment Modules:
Module 1: Fundamentals of Artificial Intelligence: Introduction to AI, History and evolution of AI, AI problem-solving techniques, Intelligent agents and environments, AI ethics and societal impact, AI applications across industries
Module 2: Machine Learning Concepts: Supervised learning, Unsupervised learning, Reinforcement learning, Feature selection and engineering, Model evaluation and validation, Overfitting, underfitting, and regularization
Module 3: Data Preparation and Management: Data collection and acquisition, Data cleaning and preprocessing, Handling missing data and outliers, Data transformation and normalization, Feature scaling and encoding, Data splitting and sampling techniques
Module 4: Machine Learning Algorithms: Linear and logistic regression, Decision trees and random forests, Support vector machines (SVM), Neural networks and deep learning, Clustering algorithms (K-means, hierarchical), Ensemble learning techniques
Module 5: Model Deployment and Optimization: Model tuning and hyperparameter optimization, Cross-validation techniques, Model deployment strategies, Monitoring and maintaining models, Model interpretability and explainability, Tools for model deployment (e.g., Docker, cloud platforms)
Module 6: Advanced AI and Emerging Trends: Natural language processing (NLP), Computer vision techniques, Reinforcement learning applications, Generative AI and large language models, AI in robotics and IoT, Ethical AI, bias detection, and fairness
