BerkeleyAIML

Coursework and notes for the Berkeley AI/ML Certification program.

Modules

Introduction
Fundamentals (distributions, correlations, probability)
Intro Data Analysis (CRISP-DM, groupby/agg/filter)
Fundamentals Data Analysis (plotting, merge/clean)
Practical Application I
Clustering & Component Analysis (SVD, PCA, KMeans, DBSCAN)
Linear & Nonlinear Regression (loss functions, multilinear)
Feature Engineering (polynomial features, hyperparameter tuning, ohe/ordinal encoding)
Model Selection & Regularization (SFS, L1/2, cv grid search)
Time Series & Forecasting (decomposition, ARMA, convolution)
Practical Application II
Classification & KNN (confusion matrix, roc curve)
Logistic Regression (sigmoid, cross-entropy)
Decision Trees (entropy, bagging)
Gradient Descent Optimization (convexity, double descent/implicit regularization)
Support Vector Machines (kernel trick)
Practical Application III
Natural Language Processing (stem/lemmatization, TDF-IDF, naive Bayes)
Recommendation Systems (content/collaborative filtering, Funk SVD)
Capstone I
Deep NNs (Keras)
Deep NNs (CNN, LSTM) 23 Capstone II

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README.md		README.md

xboluna/BerkeleyAIML