سرفصل های کتاب اول:
Algorithm 1: Gradient Descent
Algorithm 2: Linear Regression
Algorithm 3: Logistic Regression
Algorithm 4: Linear Discriminant Analysis
Algorithm 5: Classification and Regression Trees
Algorithm 6: Naive Bayes
Algorithm 7: K-Nearest Neighbors
Algorithm 8: Learning Vector Quantization
Algorithm 9: Support Vector Machines
Algorithm 10: Bagged Decision Trees and Random Forest
Algorithm 11: Boosting and AdaBoost
Tutorial 1: Simple Linear Regression using Statistics
Tutorial 2: Simple Linear Regression with Gradient Descent
Tutorial 3: Logistic Regression with Gradient Descent
Tutorial 4: Linear Discriminant Analysis using Statistics
Tutorial 5: Classification and Regression Trees with Gini
Tutorial 6: Naive Bayes for Categorical Data
Tutorial 7: Gaussian Naive Bayes for Real-Valued Data
Tutorial 8: K-Nearest Neighbors for Classification
Tutorial 9: Learning Vector Quantization for Classification
Tutorial 10: Support Vector Machines with Gradient Descent
Tutorial 11: Bagged Classification and Regression Trees
Tutorial 12: AdaBoost for Classification
سرفصل های کتاب دوم:
Data Preparation
Linear Algorithms
Nonlinear Algorithms
Ensemble Algorithms
سرفصل های کتاب سوم:
Statistics Methods for Machine Learning
Linear Algebra for Machine Learning
Probability for Machine Learning
Optimization for Machine Learning
Master Machine Learning Algorithms
ML Algorithms From Scratch
Machine Learning Mastery With Weka
Machine Learning Mastery With R
Machine Learning Mastery With Python
Data Preparation for Machine Learning
Imbalanced Classification With Python
Time Series Forecasting With Python
Deep Learning With Python
Deep Learning for CV
Deep Learning for NLP
Deep Learning for Time Series Forecasting
Generative Adversarial Networks with Python
Better Deep Learning
LSTM Networks With Python
XGBoost With Python
Ensemble Learning Algorithms With Python
Calculus for Machine Learning
Python for Machine Learning
Building Transformer Models with Attention
Deep Learning with PyTorch
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