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Diploma in Industrial Machine Learning Management

Rs.3,000.00 Rs.1,500.00

Machine learning is a process that needs inputs from many devices to feed data to it so that data can be collected. evaluated, and used to develop knowledge about how a production line produces the products and parts it does.

Description

Course Name: Diploma in Industrial Machine Learning Management

Course Id: DIMLM/Q1001.
Education Qualification:12th.
Course and Exam Fee: 3000.

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 receive Study Material and Online Examination link on your email id.

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

Step 8- After Online Examination within 7 days you will get Diploma Certificate soft copy(Scan Copy) and Hard Copy(Original With Seal and Sign) within 30 days.

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

Online Examination Detail:

Duration- 60 minutes.

No. of Questions- 30. (Multiple Choice Questions).

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.

Or You Can Fill below Enquiry form For Regular Course Training from our Training Centers located in India.

Student Course Enquiry Form

Syllabus

 

Diploma in Industrial Machine Learning Management
Name of PaperM. Marks Pass Marks
Introduction to Machine Learning10040
Machine Learning Techniques and Algorithms10040
Data Science Tool kit10040
Machine Learning and Artificial Intelligence10040
Reinforcement Learning10040
Manufacturing Planning and Control10040
Practical10040

 

Introduction to Machine Learning

Data Science, AI & ML, Use Cases in Business and Scope, Scientific Method, Modelling Concepts, CRISP-DM Method, Commands and Syntax, Packages and Libraries, Introduction to Data Types, Data Structures in R – Vectors, Matrices, Arrays, Lists, Factors, Data Frames,  Importing and Exporting Data, Control structures and Functions, Data exploration (histograms, bar chart, box plot, line graph, scatter plot), Measure of Central Tendency (Mean, Median and Mode), Relationship between attributes: Covariance, Correlation Coefficient, Chi Square.

Machine Learning Techniques and Algorithms

BASICS Learning Problems Perspectives and Issues Concept Learning Version Spaces and Candidate eliminations – Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic Space Search, NEURAL NETWORKS AND GENETIC ALGORITHMS: Neural Network Representation Problems Perceptions Multilayer Networks and Back Propagation Algorithms – Advanced Topics – Genetic Algorithms Hypothesis Space Search– Genetic Programming – Models of Evolutions and Learning.

Data Science Tool kit

Professional English and Soft Skills /Engineering Graphics and Computer Aided Design, Matrices and Calculus, Engineering Physics/Engineering Materials, Problem Solving Using C, Problem Solving Using C, Engineering Immersion Lab, Engineering Physics Lab/ Materials Chemistry Lab, Analytical Mathematics, Engineering Physics/ Engineering Materials, Professional English and Soft Skills /Engineering Graphics and Computer Aided Design, Introduction to Digital Systems / Engineering and Design, Sustainable Engineering Systems.

Machine Learning and Artificial Intelligence

Advanced Data Structures and Algorithms, Linear Algebra and Probability, Computational Methods for Optimisation, Foundation Core, Soft Core, Electives, Amrita Values Program/Career Competency, Dissertation, Advanced Data Structures and Algorithms, Linear Algebra and Probability, Computational Methods for Optimization, Advanced Computer Architecture, Advanced Computer Network, Algorithmic Graph Theory, Embedded Programming, Parallel and Distributed Data Management, Cryptography and Network Security, Wireless and Mobile Networks.

Reinforcement Learning

Reinforcement Learning, Elements of Reinforcement Learning, Limitations and Scope, An Extended Example: Tic-Tac-Toe, Summary, History of Reinforcement Learning, Bibliographical Remarks, An n-Armed Bandit Problem, Action-Value Methods, Incremental Implementation, Tracking a Non stationary Problem, Optimistic Initial Values, Upper-Confidence-Bound Action Selection, Gradient Bandits, Associative Search (Contextual Bandits), Finite Markov Decision Processes, Dynamic Programming.

Manufacturing Planning and Control

Introduction to Operations, Product planning, Process planning, Capacity and layout planning, Forecasting, Inventory management, Aggregate planning, Resource planning, Project management, Scheduling, Definition – Objectives of production Planning and Control – Functions of production planning and control – Elements of production control – Types of production – Organization of production planning and control department – Internal organization of department, Importance of forecasting –Types of forecasting.

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