Data Science & Machine Learning: Naive Bayes in Python

Review the idea of classification in supervised learning and how Naive Bayes fits as a classifier. Learn data as geometry with inputs X and targets Y in a matrix form.

Explore supervised classification, visualize separation of colored data, and compare Naive Bayes variants Gaussian, Bernoulli, and Multinomial under independence assumptions.

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Explore a strategic approach to Python Naive Bayes projects by emphasizing exploratory data analysis, misclassified samples, confusion matrix, F1 score, AUC, feature selection, and tuning.

Flatten 28×28 grayscale images into 784-feature vectors to train a naive Bayes model on MNIST digits, and compare Gaussian versus Bernoulli approaches with 0-1 scaling.

Learn how to apply Naive Bayes to text by converting documents into bag-of-words vectors, using tokenization, counting word occurrences, and building a vocab-based input matrix for classification.

Implement Gaussian naive Bayes in Python by building a fit and predict workflow with priors, means, variances, and smoothing, then test accuracy on a dataset after normalizing inputs.

Explore Bernoulli naive Bayes theory, deriving priors from class proportions, formulating the Bernoulli likelihood with log, and producing a linear classifier by w_k^T x + b_k for arg max predictions.

Explore multinomial naive Bayes, deriving P(X|Y) with a multinomial distribution on count features and using log likelihood plus prior to classify via arg max of w_k^T x + b_k.

Clarify the appendix and FAQ, showing they are optional, supplementary material with frequently asked questions and a Q&A mechanism.

Learn to install data science and machine learning libraries on Windows with Anaconda, including NumPy, SciPy, Matplotlib, Pandas, TensorFlow, Keras, PyTorch, CNTK, and Open Air Gym.

Learn proven strategies to succeed in this challenging data science course by leveraging the Q&A forum, meeting prerequisites, and implementing theory in code to solidify understanding.