Data Analytics II: Statistics and Machine Learning in Python with Examples and Applications

by Daniel Rösch

Data Analytics, Statistics and Machine Learning in Python with many Examples and Real World Applications for Financial Data Analytics, Text and Image Processing, Image Recognition and more Series 1 Fundamentals of Data - Exploratory Data Analytics - Equality and Performance Measurement - Stochastic and Probability Foundations - Statistical Learning - Parameter Estimation - Statistical Tests - Bayesian Statistics - Regression - Regression Diagnostics - Analysis of Variance (ANOVA) - Generalized Linear ModelsSeries 2 Machine Learning Principles – Workflow – Feature Engineering - Learning, Validation and Prediction – Under- and Overfit – Train-Test-Split - Crossvalidation – Hyperparameter tuning - Supervised Machine Learning for Regression and Classification– K-Nearest Neighbors – Decision Trees – Bootstrapping – Bagging – Random Forests - Boosting – Neural Networks – Unsupervised Learning – Clustering – Principal Components Analysis - Fallacies

250 pages, Paperback

Published October 30, 2020