BxD Primer Series: Ending and New Beginnings

I am grateful for our 75 editions long journey and ready for next steps.

Hey there 👋

Welcome to BxD Primer Series where we covered machine learning and AI models in ‘75-posts-75-topics’ format.

Today marks the 76th edition of primer series and with that we are calling it a close. Reading all of these editions will set your feet solid in ML/AI models, both theoretically and practically.

Revisiting our journey from the day we introduced BxD and Primer series:

Seven Regression Models:

• Linear Regression

• Logistic Regression

• Polynomial Regression

• Ridge Regression and L2 Regularization

• Lasso Regression and L1 Regularization

• Elastic Net Regression; Combining L1 and L2 Regularization

• Decision Trees for Regression

Five Classification Models:

• Decision Trees for Classification

• Conditional Inference Decision Trees

• Support Vector Machines

• Naive Bayes Models for Classification

• K-Nearest Neighbors

Seven Clustering Models:

• K-Means Clustering

• DBSCAN Clustering

• Agglomerative Clustering

• Spectral Clustering

• Affinity Propagation

• Mean-Shift Clustering

• Fuzzy C-Means Clustering

Three Pattern Search Models:

• ECLAT Pattern Search

• Apriori Pattern Search

• FP-Growth Pattern Search

Four Dimensionality Reduction Models:

• Principal Component Analysis (PCA)

• Latent Semantic Analysis (LSA)

• Linear Discriminant Analysis (LDA)

• t-SNE for Dimensionality Reduction

Five Recommendation Models:

• Collaborative Filtering

• Content-based Filtering

• Knowledge-based Recommendation Models

• Matrix Factorization

• Hybrid Recommendation Systems

Six Time Series Models:

• ARIMA Time Series

• Exponential Smoothing

• SARIMA Time Series

• Vector Autoregression

• Prophet Time Series

• Hidden Markov Time Series

Five Ensemble Models:

• Stacking

• Bagging

• Boosting

• Ensemble of Experts

• Bayesian Model Averaging

Six Reinforcement Learning Models:

• SARSA Reinforcement Learning

• Asynchronous Advantage Actor-Critic

• Q-Learning

• Deep Q-Network (DQN)

• Genetic Algorithm Reinforcement Learning

• Multi-Agent Reinforcement Learning

Twenty Three Neural Network Architectures/Models:

• Introduction to Neural Networks and Perceptron

• Feed Forward Network

• Radial Basis Network

• Recurrent Network

• Long Short-Term Memory (LSTM) Network

• Gated Recurrent Unit (GRU) Network

• Autoencoder Network

• Variational Autoencoder Network

• Markov Chain Network

• Hopfield Network

• Boltzmann Machine Network

• Restricted Boltzmann Machine Network

• Deep Belief Network

• Convolutional Network

• Deconvolutional Network

• Convolutional Inverse Graphic Network

• Generative Adversarial Network

• Liquid State Machine (LSM) Network

• Extreme Learning Machine Network

• Echo State Network

• Deep Residual Network

• Capsule Network

• Kohonen Network

Attention Mechanism

Stable Diffusion Models

Transformer Models

Transfer Learning Techniques

What’s Next?

Next, you can expect some more formats.

Your continued support and feedback is the only thing we need to continue delivering.

So stay around and bring more friends…

And remember to,

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Enjoy life in full, every single day!

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