Post Graduate Diploma in AI and Machine Learning

Description

Course Name: Post Graduate Diploma in AI and Machine Learning
Course Id: PGDAIML/Q1001.
Education Qualification: Graduate.

Duration: 370 Hrs. (Equivalent to One Year).

Total Credits: 18.

How You will Get Diploma Certificate:

Step 1- Select your Course for Certification.

Step 2- Click on Enroll Now.

Step 3- Proceed to Enroll Now.

Step 4- Fill Your Billing Details and Proceed to Pay.

Step 5- You Will be Redirected to Payment Gateway, Pay Course and Exam Fee by Following Options.

Card(Debit/Credit), Wallet, Paytm, Net banking, UPI and Google pay.

Step 6- After Payment You will get Study Material Login id and Password on your email id.

Step 7- After Completion of  Course Study give Online Examination.

Step 8- After Online Examination you will get Diploma Certificate soft copy(Scan Copy) and Hard Copy(Original With Seal and Sign).

Step 9- After Certification you will receive Prospect Job Opportunities as per your Interest Area.

Online Examination Detail:

• Duration- 120 minutes.
• No. of Questions- 60. (Multiple Choice Questions).
• 10 Questions from each module, each carry 10 marks.
• Maximum Marks- 600, Passing Marks- 40%.
• There is no negative marking in this module.

Benefits of Certification:

• Government Authorized Assessment Agency Certification.
• Certificate Valid for Lifetime.
• Lifetime Verification of Certificate.
• Free Job Assistance as per your Interest Area.

Syllabus

Introduction to AI and Machine Learning: Introduction, properties of search algorithms, constructing search trees, classification of machine learning, types of supervised machine learning algorithms, regression analysis in machine learning, types of regression, types of linear regression, clustering algorithms, applications of clustering, types of reinforcement learning.

Mathematical Foundations: Introduction, set rules % set combinations, relations, functions, properties of numeric functions, recurrence relation for discrete numeric functions, common recurrences from algorithms, method for solving recurrences, propositional logic, natural deduction method, variables and quantifiers, definition of regular expression.

Natural Language Processing (NLP): Technology for accessing info, information retrieval, information extraction, automatic summarization, speech technologies, human and machine intelligence, language communication technology, computational linguistics, statistical approaches, basic IR models.

Neural Networks and Deep Learning: The architecture of neural networks, implementing our network to classify digits, the cross- entropy cost function, weight initialization, many input variables, convolutional neural networks in practice, recent progress in image recognition.

Programming for AI: Introduction- AI for everyone, unlocking your future in AI, python Programming, data literacy-data collection to data analysis, machine learning algorithms, leveraging linguistics and computer science, AI ethics and values.

Supervised Learning: Introduction, the regression problem and linear regression, the classification problem and three parametric classifiers, non- parametric methods for regression and classification, ensemble methods, neural networks and deep learning.