MST 011 Real Analysis, Calculus and Geometry | Quick Readable Notes For IGNOU Exam 
MST 012 Probability and Probability Distributions | Quick Readable Notes For IGNOU Exam 
MST 013 Survey Sampling and Design of Experiments-I | Quick Readable Notes For IGNOU Exam 
MST 014 Statistical Quality Control and Time Series Analysis | Quick Readable Notes For IGNOU Exam 
MST 015 Introduction to R Software | Quick Readable Notes For IGNOU Exam 
MST 016 Statistical Inference | Quick Readable Notes For IGNOU Exam 
MST 017 Applied Regression Analysis | Quick Readable Notes For IGNOU Exam 
MST 018 Multivariate Analysis | Quick Readable Notes For IGNOU Exam 
MST 019 Epidemiology and Clinical Trials | Quick Readable Notes For IGNOU Exam 
MST 020 Survey Sampling and Design of Experiments-II | Quick Readable Notes For IGNOU Exam 
MST 021 Classical and Bayesian Inference | Quick Readable Notes For IGNOU Exam 
MST 022 Linear Algebra and Multivariate Calculus | Quick Readable Notes For IGNOU Exam 
MST 023 Research Methodology | Quick Readable Notes For IGNOU Exam 
MST 024 Data Analysis with Python | Quick Readable Notes For IGNOU Exam 
MST 025 Categorical and Survival Analysis| Quick Readable Notes For IGNOU Exam 
MSTE 011 Operations Research | Quick Readable Notes For IGNOU Exam 
MSTE 012 Stochastic Processes | Quick Readable Notes For IGNOU Exam MST 026 Introduction to Machine Learning | Quick...
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| 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 | : 100-130 |
| Weight | : 157gms |
| Dimensions | : 21.0 x 29.7 cm (A4 Size Pages) |
MST 026 Introduction to Machine Learning introduces foundational principles and techniques used in machine learning. Topics include supervised learning (regression and classification), unsupervised learning (clustering), decision trees, support vector machines, neural networks, and model evaluation metrics.
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