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datasets
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images
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Lec-6.01(Overview-of-ML).pdf
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Lec-6.02(Math-Behind-Simple-Linear-Regression).pdf
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Lec-6.03(Math-Behind-Multiple-Regression).pdf
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Lec-6.04(Linear-Regression-using-Gradient-Descent).pdf
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Lec-6.05(Overview-of-Scikit-Learn-Library).pdf
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Lec-6.06(Linear-Regression-using-Scikit-Learn).pdf
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Lec-6.07(Data-Preprocessing-Feature-Engineering-Handling-Outliers).pdf
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Lec-6.08(Data-Preprocessing-Missing-Values-Imputation).pdf
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Lec-6.09(Data-Preprocessing-Encoding-Categorical-Data).pdf
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Lec-6.10(Data-Preprocessing-Feature-Scaling).pdf
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Lec-6.11(Data-Preprocessing-Extracting-and-Combining-Information).pdf
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Lec-6.12(Polynomial Regression).pdf
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Lec-6.13(Regularization-Ridge-Lasso-ElasticNet).pdf
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Lec-6.14(Cross-Validation-Techniques).pdf
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Lec-6.15 (Hyperparameter Tuning using Grid and Random Search).pdf
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Lec-6.16(Building-Machine-Learning-Pipeline-A-Z).pdf
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Lec-6.20(Logistic Regression Part-I) - Jupyter Notebook.pdf
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Lec-6.21(Logistic Regression Part-II) - Jupyter Notebook.pdf
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Lec-6.22(Logistic Regression Part-III) - Jupyter Notebook.pdf
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Lec-6.23(Logistic Regression Part-IV) - Jupyter Notebook.pdf
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Lec-6.24(K-Nearest Neighbours).pdf
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Lec-6.25 (A Comprehensive Recap of Probability for Bayes Theorem).pdf
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Lec-6.26 (Bayes Theorem and Naive Bayes Classifiers).pdf
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Lec-6.27 (Naive Bayes Classifiers using Scikit Learn).pdf
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Lec-6.28(Support Vector Machines Part-I).pdf
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Lec-6.29(Support Vector Machines Part-II).pdf
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