Linear & Logistic regression, support vendor machines, Decision trees, Random forest, Gradient boosting, K-Nearest Neighbors.
Learn the intuition of various Machine learning models and implement them for different datasets with R & Python. Starting supervised learning models, to unsupervised learning and learn all the technique’s used by data scientist like misbalance dataset, feature engineering & correct model selection & tuning for better predictions.
Linear & Logistic regression, support vendor machines, Decision trees, Random forest, Gradient boosting, K-Nearest Neighbors.
K-means clustering, Hierarchical, Neural Networks, Sentiment Analytics, Naïve Bayes.
Treating missing values with MICE / Amelia, outlier values treatment with different techniques.
New bias creation, Dummy variables & one hot encoding, combining features, creating new variables.
ML models training, Prediction, tuning. Ensemble modelling – Bagging, Boosting, Stacking.
Functions & A hands-on intro classes with basic packages like pandas, numpy, scipy, matplotlib.
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