Supervised Learning
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.
Bored of PPT lectures in classroom?? Yeah.. We too.. Come fall in love with our simplified ways of teaching with complete hands-on sessions & real time datasets.
Scared of programming?? Coding, syntax errors, logical errors haunts you?? Fear not. Utilize our free One on One mentorship support with complete guidance.
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