MST 011 Real Analysis, Calculus and Geometry | IN-DEPTH STUDY GUID for IGNOU 
MST 011 Real Analysis, Calculus and Geometry | IN-DEPTH STUDY GUIDE for IGNOU 
MST 012 Probability and Probability Distributions | IN-DEPTH STUDY GUIDE for IGNOU 
MST 013 Survey Sampling and Design of Experiments-I | IN-DEPTH STUDY GUIDE for IGNOU 
MST 014 Statistical Quality Control and Time Series Analysis | IN-DEPTH STUDY GUIDE for IGNOU 
MST 015 Introduction to R Software | IN-DEPTH STUDY GUIDE for IGNOU 
MST 016 Statistical Inference| IN-DEPTH STUDY GUIDE for IGNOU 
MST 017 Applied Regression Analysis | IN-DEPTH STUDY GUIDE for IGNOU 
MST 018 Multivariate Analysis | IN-DEPTH STUDY GUIDE for IGNOU 
MST 019 Epidemiology and Clinical Trials | IN-DEPTH STUDY GUIDE for IGNOU 
MST 020 Survey Sampling and Design of Experiments-II| IN-DEPTH STUDY GUIDE for IGNOU 
MST 021 Classical and Bayesian Inference | IN-DEPTH STUDY GUIDE for IGNOU 
MST 022 Linear Algebra and Multivariate Calculus | IN-DEPTH STUDY GUIDE for IGNOU 
MST 023 Research Methodology | IN-DEPTH STUDY GUIDE for IGNOU 
MST 024 Data Analysis with Python | IN-DEPTH STUDY GUIDE for IGNOU 
MST 025 Categorical and Survival Analysis | IN-DEPTH STUDY GUIDE for IGNOU 
MSTE 011 Operations Research | IN-DEPTH STUDY GUIDE for IGNOU 
MSTE 012 Stochastic Processes | IN-DEPTH STUDY GUIDE for IGNOU 
MST 026 Introduction to Machine Learning | Quick...
Rs.149
| Product Name | Cart |
|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| Category | : MASTER‘S DEGREE PROGRAMMES |
| Sub Category | : Master of Science (Applied Statistics) (MSCAST) |
| Products Code | : MSCAST-MST-S4-5.51 |
| HSN Code | : 490110 |
| Language | : English |
| Author | : BMAP EDUSERVICES PVT LTD |
| Publisher | : BMAP EDUSERVICES PVT LTD |
| University | : IGNOU (Indira Gandhi National Open University) |
| Pages | : 300 |
| Weight | : 199 GM |
| Dimensions | : 21.0 x 29.7 cm (A4 Size Pages) |
MST 026 Introduction to Machine Learning is designed to introduce students to the fundamental concepts and techniques of machine learning. This course covers various algorithms and methodologies used in supervised and unsupervised learning, providing a solid foundation for further studies and applications in machine learning and data science.
Benefits:
DISCLAIMER
The IGNOU solved assignments and guess papers provided on this platform are for reference purposes only and should not be used to engage in educational dishonesty. These materials serve as learning and study tools and are not intended for submission as original work. Users are responsible for using these materials ethically and in accordance with their educational institution's guidelines. We do not assume liability for any misuse or consequences resulting from the use of these materials. By accessing and utilizing these resources, users agree to this disclaimer.
Chat with us
