The Machine Learning Simplified: A Gentle Introduction to Supervised Learning
by Andrew Wolf
- Length: 199 pages
- Edition: 1
- Language: English
- Publication Date: 2022-05-22
- ISBN-10: B0B216KMM4
- Sales Rank: #42141 (See Top 100 Books)
The underlying goal of “Machine Learning Simplified” is to develop strong intuition into inner workings of ML. We use simple intuitive examples to explain complex concepts, algorithms or methods, as well as democratize all mathematics “behind the scenes”.
After reading this book, you will understand everything that comes into the scope of supervised ML. You will be able to not only understand nitty-gritty details of mathematics, but also explain to anyone how things work on a high level.
I FUNDAMENTALS OF SUPERVISED LEARNING Introduction Machine Learning Supervised Learning Unsupervised Learning Machine Learning Pipeline Data Science ML Operations Artificial Intelligence Information Processing Types of AI Overview of this Book Overview of Supervised Learning ML Pipeline: Example Problem Representation Learning a Prediction Function How Good is our Prediction Function? Controlling Model Complexity ML Pipeline: General Form Data Extraction Data Preparation Model Building Model Deployment Model Learning Linear Regression Linear Models Goodness-of-Fit Gradient Descent Algorithm Gradient Descent with More Parameters Gradient Descent in Other ML Models Getting Stuck in a Local Minimum Overshooting Global Minimum Non-differentiable Cost Functions Basis Expansion and Regularization Basis Expansion Polynomial Basis Expansion Comparison of Model Weights Regularization Ridge Regression Choosing Regularization Strength Lasso Regression Comparison between L1 and L2 Regularization Model Selection Bias-Variance Decomposition Mathematical Definition Diagnosing Bias and Variance Error Sources Validation Methods Hold-out Validation Cross Validation Unrepresentative Data Feature Selection Introduction Filter Methods Univariate Selection Multivariate Selection Search Methods Embedded Methods Comparison Data Preparation Data Cleaning Dirty Data Outliers Feature Transformation Feature Encoding Feature Scaling Feature Engineering Feature Binning Ratio Features Handling Class Label Imbalance Oversampling Synthetic Minority Oversampling Technique (SMOTE) Appendix Unsupervised Learning Appendix Non-differentiable Cost Functions Discontinuous Functions Continuous Non-differentiable Functions
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