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Machine Learning 101 Class Bootcamp Course NYC
Python Scikit-learn Library
Supervised vs Unsupervised Learning
Regression vs Classification models
Categorical vs Continuous feature spaces
Modeling Fundamentals: Test-train split, Cross validation(CV), Bias–variance tradeoff, Precision and Recall, Ensemble models
Interpreting Results of Regression and Classification Models (Hands On)
Parameters and Hyper Parameters
SVM, K-Nearest Neighbor, Neural Networks
Understanding and Interpreting results of Regression and Logistic Regression using Google Spreadsheets and Python
Calculating R-Square, MSE, Logit manually in excel for enhanced understanding (Multiple Regression)
Understanding features of Popular Datasets: Titanic, Iris (Scikit) and Housing Prices
Running Logistic Regression on Titanic Data Set
Running Regression, Logistic Regression, SVM and Random Forest on Iris Dataset