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Machine Learning Algorithms PDF in Hindi

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  • इस Machine Learning Algorithms PDF in Hindi में आपको अपने Syllabus के सभी Topics मिलेंगे।
  • इसमें आपको पिछले साल के Question Paper भी मिलेंगे।
  • आप हमें कभी भी WhatsApp में मैसेज या Call कर सकते हैं। हमारा नंबर हैं:- 9899927549
Category:
  • इस Machine Learning Algorithms PDF in Hindi में आपको अपने Syllabus के सभी Topics मिलेंगे।
  • इसमें आपको पिछले साल के Question Paper मिलेंगे। जिससे आपको अपने exam की तैयारी करने में बहुत मदद मिलेगी।
  • आप हमें कभी भी WhatsApp में मैसेज या Call कर सकते हैं। हमारा नंबर हैं:- 9899927549

Syllabus

1. Foundations for Machine learning

ML Techniques overview, Validation Techniques (Cross-Validations), Feature Reduction/Dimensionality reduction, Principal components analysis (Eigen values, Eigen vectors, Orthogonality).

2. Regression Techniques

Regression basics: Relationship between attributes using Covariance and Correlation, Relationship between multiple variables: Regression (Linear, Multivariate) in prediction, Residual Analysis, Identifying significant features, feature reduction using AIC, multi-collinearity, Non-normality and Heteroscedasticity, Hypothesis testing of Regression Model, Confidence intervals of Slope, Rsquare and goodness of fit, Influential Observations – Leverage.

3. Multiple Linear Regression and Non-Linear Regression 

Polynomial Regression, Regularization methods, Lasso, Ridge and Elastic nets, Categorical Variables in Regression, Logit function and interpretation, Types of error measures (ROCR), Logistic Regression in classification.

4. Clustering 

Distance measures, Different clustering methods (Distance, Density, and Hierarchical), Iterative distance-based clustering, Dealing with continuous, categorical values in K-Means, Constructing a hierarchical cluster, K-Medoids, k-Mode and density-based clustering, Measures of quality of clustering.

5. Classification Techniques 

Naive Bayes Classifiers: Model Assumptions, Probability estimation, Required data processing, Mestimates, Feature selection: Mutual information, Classifier. K-Nearest Neighbors: Computational geometry, Voronoi Diagrams, Delaunay Triangulations, KNearest Neighbor algorithm, Wilson editing and triangulations, Aspects to consider while designing K-Nearest Neighbor.

Support Vector Machines: Linear learning machines and Kernel space, Making Kernels and working in feature space, SVM for classification and regression problems.

Advanced Machine Learning topics: Neural Network algorithms, Deep Learning algorithms,
Natural Language processing algorithms.

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