[Fully Explained] 100 Superb Machine Learning Modelsofficial website

In conjunction with two other big-time algorithm gurus, we are proud to present 100 Superb Machine Learning Algorithm Models! This booklet has already surpassed 200,000 readers across the web, attracting even more machine learning algorithm enthusiasts.

Six columns with rich content

The booklet covers six main columns:

1. Supervised learning
2. unsupervised learning
3. Enhanced learning
4. deep learning
5. Regularization and optimization techniques
6. Other algorithms and techniques

Each column provides a detailed analysis of the algorithmic model, including case introduction, algorithm principle, dataset, calculation steps, Python code examples and code detail analysis to help you further on your learning path.

How to Buy & Benefits

Originally $199, now $29.90 permanent buyout. For every 500 people, the price will increase by $10. After purchase, scan the QR code in the top article to add WeChat (note [ML] and attach a screenshot of the little newsboy), you can get the machine learning and interview materials, and join the learning group to communicate and discuss with other learners.

Why this booklet?

Many beginners are often confused and clueless when it comes to learning machine learning due to the lack of complete examples. To help you overcome this challenge, we have compiled a list of 100 of the most common algorithmic models that are designed to provide you with tangible help on your learning journey.

List of casebook contents

Supervised learning

• Linear Regression (LR)
• Polynomial Regression (Polynomial Regression)
• Ridge Regression
• Lasso Regression (Lasso Regression)
• Elastic Net Regression (ENR)
• Logistic Regression
• Decision Tree Regression (DTR)
• Random Forest Regression (RFR)
• Support Vector Machines (SVM)
• nonlinear support vector machine (math.)
• multiclass support vector machine (SVM)
• kernel function support vector machine
• sparse support vector machine (SSVM)
• kernel Bayesian support vector machine
• Unbalanced category support vector machine
• K-Nearest Neighbors (KNN)
• AdaBoost
• Gradient Boosting Trees (GBT)
• XGBoost (extreme gradient boosting)
• LightGBM (Lightweight Gradient Lifter)
• CatBoost
• Bayesian Ridge regression
• Bayesian Lasso regression

unsupervised learning

• K-Means Clustering (K-Means Clustering)
• Hierarchical Clustering (HCC)
• Density-Based Clustering (DBC)
• Spectral Clustering (Spectral Clustering)
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
• EM Clustering (Expectation-Maximization Clustering)
• Fuzzy Clustering (Fuzzy Clustering)
• Principal Component Analysis (PCA, Principal Component Analysis)
• Linear Discriminant Analysis (LDA, Linear Discriminant Analysis)
• t-SNE (t-Distributed Stochastic Neighbor Embedding)
• Independent Component Analysis (ICA, Independent Component Analysis)
• Autoencoder
• Association rule learning
• Apriori algorithm
• Eclat's algorithm
• FP-growth algorithm

Enhanced learning

• Q-learning
• Deep Q Network (DQN)
• Policy Gradients
• Actor-Critic method
• Deep Deterministic Strategy Gradient (DDPG)
• Advantageous actors-critics (A2C)
• Advantage Actor-Critic (A3C)
• Trust Region Strategy Optimization (TRPO)
• Proximal Policy Optimization (PPO)
• Self-game learning (e.g. AlphaGo)

deep learning

• Feedforward Neural Networks (FNNs)
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM) Networks
• Gated Recurrent Unit (GRU)
• Self-attention model (Transformer)
• Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAEs)
• Deep Belief Networks (DBNs)
• Deep Boltzmann Machine (DBM)
• Residual Network (ResNet)
• Inception Network
• U-Net
• YOLO (real-time object detection)
• Mask R-CNN (instance segmentation)
• Siamese network (for similarity learning)
• Triplet networks (for similarity learning)
• multitasking learning network
• Migration learning and fine-tuning models
• Neural Style Conversion
• CycleGAN (Cycle Generative Adversarial Network)

Regularization and optimization techniques

• L1 Regularization (Lasso regularization)
• L2 Regularization (Ridge Regularization)
• Elastic Net Regularization (Elastic Net Regularization)
• Dropout regularization
• Batch Normalization
• Gradient Clipping
• Early Stopping
• Hyperparameter Tuning (e.g. grid search, random search)
• Model Integration Technology
• Bagging (Bootstrap Aggregating)
• Boosting
• Stacking
• Voting
• Deep Learning Integration
• data enhancement

Other algorithms and techniques

• ID3 (Iterative Dichotomiser 3)
• C4.5
• CART (Classification and Regression Trees)
• Random Forest
• Multi-output Trees (MOTTs)
• Feature Selection
• nuclear method downscaling
• Gaussian Processes (Gaussian Processes)
• Bayesian Optimization (Bayesian Optimization)
• Variational Bayesian Methods (VBM)
• Bayesian Deep Learning (BDL)
• Naive Bayes.
• Bayesian Networks.
• Natural Language Processing (NLP) specific algorithms (e.g. BERT, GPT)
• Graph Neural Networks (GNN)

summarize

100 Superb Machine Learning Algorithm Models is more than just a booklet, it is an indispensable helper in your machine learning process. It provides you with detailed case studies and practical guidance to help you quickly master all kinds of machine learning algorithm models. Whether you are a beginner or an advanced learner, this booklet can meet your needs. Join us and start your machine learning journey!