GitHub - sandipanpaul21/ML-Notes-Daywise: Machine Learning Note Day Wise (THEORY)

Name		Name	Last commit message	Last commit date
Latest commit History 75 Commits
Day 01 ML (Introduction and History).pdf		Day 01 ML (Introduction and History).pdf
Day 02 Different Types of ML.pdf		Day 02 Different Types of ML.pdf
Day 03 Bias and Hypothesis Space.pdf		Day 03 Bias and Hypothesis Space.pdf
Day 04 Bias Variance Tradeoff.pdf		Day 04 Bias Variance Tradeoff.pdf
Day 05 Metrics in Classification (Confusion Matrix).pdf		Day 05 Metrics in Classification (Confusion Matrix).pdf
Day 06 Cross Validation & Errors.pdf		Day 06 Cross Validation & Errors.pdf
Day 07 Metrics in Regression.pdf		Day 07 Metrics in Regression.pdf
Day 08 Degree of Freedom.pdf		Day 08 Degree of Freedom.pdf
Day 09 Sum of Square & Adjusted R Square.pdf		Day 09 Sum of Square & Adjusted R Square.pdf
Day 10 Parametric & Non Parametric Test.pdf		Day 10 Parametric & Non Parametric Test.pdf
Day 11 Estimation of Function F.pdf		Day 11 Estimation of Function F.pdf
Day 12 (LR) Linear Regression Introduction.pdf		Day 12 (LR) Linear Regression Introduction.pdf
Day 13 Hypothesis Testing.pdf		Day 13 Hypothesis Testing.pdf
Day 14 P Values.pdf		Day 14 P Values.pdf
Day 15 Level of Significance (Alpha).pdf		Day 15 Level of Significance (Alpha).pdf
Day 16 Normal Distribution.pdf		Day 16 Normal Distribution.pdf
Day 17 Confidence Interval & Central Limit Theorem.pdf		Day 17 Confidence Interval & Central Limit Theorem.pdf
Day 18 Standard Error.pdf		Day 18 Standard Error.pdf
Day 19 Power Analysis.pdf		Day 19 Power Analysis.pdf
Day 20 Covariance, Pearson and Spearman Correlation.pdf		Day 20 Covariance, Pearson and Spearman Correlation.pdf
Day 21 Auto Correlation, Durbin Watson and Breusch Godfrey Test.pdf		Day 21 Auto Correlation, Durbin Watson and Breusch Godfrey Test.pdf
Day 22 Correlation and Causation.pdf		Day 22 Correlation and Causation.pdf
Day 23 Variation Inflation Factor (VIF).pdf		Day 23 Variation Inflation Factor (VIF).pdf
Day 24 Collinearity & Multi Collinearity.pdf		Day 24 Collinearity & Multi Collinearity.pdf
Day 25 Homoskedasticity & Heteroskedasticity.pdf		Day 25 Homoskedasticity & Heteroskedasticity.pdf
Day 26 AIC & BIC.pdf		Day 26 AIC & BIC.pdf
Day 27 Residual Plots.pdf		Day 27 Residual Plots.pdf
Day 28 Scaling, Normalisation & Standardisation.pdf		Day 28 Scaling, Normalisation & Standardisation.pdf
Day 29 R, R Square & Adjusted R Square.pdf		Day 29 R, R Square & Adjusted R Square.pdf
Day 30 Regression Assumptions.pdf		Day 30 Regression Assumptions.pdf
Day 31 Anova and Two Way Anova.pdf		Day 31 Anova and Two Way Anova.pdf
Day 32 T test.pdf		Day 32 T test.pdf
Day 33 Regression Output Explained.pdf		Day 33 Regression Output Explained.pdf
Day 34 Categorical variable & Interaction Terms.pdf		Day 34 Categorical variable & Interaction Terms.pdf
Day 35 OLS, Simple & Multiple Linear Regression.pdf		Day 35 OLS, Simple & Multiple Linear Regression.pdf
Day 36 AUC & ROC.pdf		Day 36 AUC & ROC.pdf
Day 37 Odds & Log(Odds) Ratio.pdf		Day 37 Odds & Log(Odds) Ratio.pdf
Day 38 Likelihood vs Probability.pdf		Day 38 Likelihood vs Probability.pdf
Day 39 Maximum Likelihood.pdf		Day 39 Maximum Likelihood.pdf
Day 40 Logit Models.pdf		Day 40 Logit Models.pdf
Day 41 Logistic Regression.pdf		Day 41 Logistic Regression.pdf
Day 42 LR Output Explained.pdf		Day 42 LR Output Explained.pdf
Day 43 Bayes Theorem.pdf		Day 43 Bayes Theorem.pdf
Day 44 Naive Bayes .pdf		Day 44 Naive Bayes .pdf
Day 45 Decision Tree Regression.pdf		Day 45 Decision Tree Regression.pdf
Day 46 Classification Tree, Tree vs Linear Model.pdf		Day 46 Classification Tree, Tree vs Linear Model.pdf
Day 47 Entropy, Information Gain & Gini Index.pdf		Day 47 Entropy, Information Gain & Gini Index.pdf
Day 48 Decision Tree Classification.pdf		Day 48 Decision Tree Classification.pdf
Day 49 Random Forest and Ensemble Techniques.pdf		Day 49 Random Forest and Ensemble Techniques.pdf
Day 50 Voting Classifiers.pdf		Day 50 Voting Classifiers.pdf
Day 51 Euclidean & Manhattan Distance.pdf		Day 51 Euclidean & Manhattan Distance.pdf
Day 52 KNN Classification.pdf		Day 52 KNN Classification.pdf
Day 53 Curse of Dimensionality.pdf		Day 53 Curse of Dimensionality.pdf
Day 54 High Cardinality Attributes.pdf		Day 54 High Cardinality Attributes.pdf
Day 55 WOE & IV.pdf		Day 55 WOE & IV.pdf
Day 56 Standard Deviation & Variance.pdf		Day 56 Standard Deviation & Variance.pdf
Day 57 Statistical Test (Which Test to use When).pdf		Day 57 Statistical Test (Which Test to use When).pdf
Day 58 Sampling Techniques.pdf		Day 58 Sampling Techniques.pdf
Day 59 Chi Square Test.pdf		Day 59 Chi Square Test.pdf
Day 60 Interpolation & Extrapolation.pdf		Day 60 Interpolation & Extrapolation.pdf
Day 61 Z test & Z score.pdf		Day 61 Z test & Z score.pdf
Day 62 Unsupervised Learning.pdf		Day 62 Unsupervised Learning.pdf
Day 63 Clustering.pdf		Day 63 Clustering.pdf
Day 64 K Means Clustering.pdf		Day 64 K Means Clustering.pdf
Day 65 Elbow Method.pdf		Day 65 Elbow Method.pdf
Day 66 Hierarchical Clustering.pdf		Day 66 Hierarchical Clustering.pdf
Day 67 Dendogram.pdf		Day 67 Dendogram.pdf
Day 68 DBScan Clustering.pdf		Day 68 DBScan Clustering.pdf
Day 69 GMM Clustering.pdf		Day 69 GMM Clustering.pdf
Day 70 Cluster Adjustment .pdf		Day 70 Cluster Adjustment .pdf
Day 71 Silhouette Coefficient - Cluster Validation.pdf		Day 71 Silhouette Coefficient - Cluster Validation.pdf
Day 72 Disadvantage & Choosing Right Clustering .pdf		Day 72 Disadvantage & Choosing Right Clustering .pdf
Day 73 Clustering Assumptions.pdf		Day 73 Clustering Assumptions.pdf
Day 74 Regularization Techniques.pdf		Day 74 Regularization Techniques.pdf
Day 75 Dimensionality Reduction Overview.pdf		Day 75 Dimensionality Reduction Overview.pdf
Day 76 LDA.pdf		Day 76 LDA.pdf
Day 77 PCA.pdf		Day 77 PCA.pdf
Day 78 PCA Example.pdf		Day 78 PCA Example.pdf
Day 79 PCA Practical Tips.pdf		Day 79 PCA Practical Tips.pdf
Day 80 T-SNE.pdf		Day 80 T-SNE.pdf
Day 81 Factor Analysis.pdf		Day 81 Factor Analysis.pdf
Day 82 Dimensionality Reduction Assumptions.pdf		Day 82 Dimensionality Reduction Assumptions.pdf
Day 83 Dimensionality Reduction Advantage Disadvantages.pdf		Day 83 Dimensionality Reduction Advantage Disadvantages.pdf
Day 84 Effective way for Dimensionality Reduction.pdf		Day 84 Effective way for Dimensionality Reduction.pdf

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