This tutorial demonstrates how to combine LLM embeddings, TF-IDF vectors, and metadata features into a single Scikit-learn pipeline for document retrieval and search. It covers generating embeddings with Sentence Transformers, calculating TF-IDF, handling metadata, and building a combined retrieval system.
This article compares the performance of LLM embeddings, TF-IDF, and Bag of Words for text vectorization and information retrieval tasks using scikit-learn. It provides a practical comparison with code examples and discusses the strengths and weaknesses of each approach.
This tutorial demonstrates how to perform document clustering using LLM embeddings with scikit-learn. It covers generating embeddings with Sentence Transformers, reducing dimensionality with PCA, and applying KMeans clustering to group similar documents.
A 12-week, 26-lesson curriculum all about Machine Learning, using primarily Scikit-learn and avoiding deep learning.
The `mlabonne/llm-course` GitHub page offers a comprehensive LLM education in three parts: **Fundamentals** (optional math/Python/NN basics), **LLM Scientist** (building LLMs – architecture, training, alignment, evaluation, optimization), and **LLM Engineer** (applying LLMs – deployment, RAG, agents, security). It’s a detailed syllabus with extensive resources for learning the entire LLM lifecycle, from theory to practical application.
The `mlabonne/llm-course` GitHub page offers a comprehensive LLM education in three parts: **Fundamentals** (optional math/Python/NN basics), **LLM Scientist** (building LLMs – architecture, training, alignment, evaluation, optimization), and **LLM Engineer** (applying LLMs – deployment, RAG, agents, security). It’s a detailed syllabus with extensive resources for learning the entire LLM lifecycle, from theory to practical application.
The author discusses a shift in approach to clustering mixed data, advocating for starting with the simpler Gower distance metric before resorting to more complex embedding techniques like UMAP. They introduce 'Gower Express', an optimized and accelerated implementation of Gower.
This article explores how different decision tree hyperparameters affect performance and visual structure, using scikit-learn's DecisionTreeRegressor and the California housing dataset. It examines the impact of max_depth, ccp_alpha, min_samples_split, min_samples_leaf, and max_leaf_nodes, and demonstrates the use of cross-validation and BayesSearchCV for optimal hyperparameter tuning.
This article explores the impact of hyperparameters on random forests, both in terms of performance and visual representation. It compares the performance of a default random forest with tuned decision trees and examines the effects of various hyperparameters like `n_estimators`, `max_depth`, and `ccp_alpha` using visualizations of individual trees, predictions, and errors.
The article discusses using Large Language Model (LLM) embeddings as features in traditional machine learning models built with scikit-learn. It covers the process of generating embeddings from text data using models like Sentence Transformers, and how these embeddings can be combined with existing features to improve model performance. It details practical steps including loading data, creating embeddings, and integrating them into a scikit-learn pipeline for tasks like classification.
The article showcases concise Python code snippets (one-liners) for common machine learning tasks like data splitting, standardization, model training (linear regression, logistic regression, decision tree, random forest), and prediction, leveraging libraries such as scikit-learn.
| **#** | **One-Liner** | **Description** | **Library** | **Use Case** |
|-----|-----------------------------------------------------|-------------------------------------------------------------------------------------|-------------------|-------------------------------------------------|
| 1 | `from sklearn.datasets import load_iris; X, y = load_iris(return_X_y=True)` | Loads the Iris dataset, a classic for classification. | scikit-learn | Loading a standard dataset. |
| 2 | `from sklearn.model_selection import train_test_split; X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)` | Splits the dataset into training and testing sets. | scikit-learn | Preparing data for model training & evaluation.|
| 3 | `from sklearn.linear_model import LogisticRegression; model = LogisticRegression(random_state=1)` | Creates a Logistic Regression model. | scikit-learn | Binary Classification. |
| 4 | `model.fit(X_train, y_train)` | Trains the Logistic Regression model. | scikit-learn | Model training. |
| 5 | `y_pred = model.predict(X_test)` | Predicts labels for the test dataset. | scikit-learn | Making predictions. |
| 6 | `from sklearn.metrics import accuracy_score; accuracy = accuracy_score(y_test, y_pred)` | Calculates the accuracy of the model. | scikit-learn | Evaluating model performance. |
| 7 | `import pandas as pd; df = pd.DataFrame(X, columns=iris.feature_names)` | Creates a Pandas DataFrame from the Iris dataset features. | Pandas | Data manipulation and analysis. |
| 8 | `df 'target' » = y` | Adds the target variable to the DataFrame. | Pandas | Combining features and labels. |
| 9 | `df.head()` | Displays the first few rows of the DataFrame. | Pandas | Inspecting the data. |
| 10 | `df.describe()` | Generates descriptive statistics of the DataFrame. | Pandas | Understanding data distribution. |
| **#** | **One-Liner** | **Description** | **Library** | **Use Case** |
|-----|-----------------------------------------------------|-------------------------------------------------------------------------------------|-------------------|-------------------------------------------------|
| 1 | `from sklearn.datasets import load_iris; X, y = load_iris(return_X_y=True)` | Loads the Iris dataset, a classic for classification. | scikit-learn | Loading a standard dataset. |
| 2 | `from sklearn.model_selection import train_test_split; X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)` | Splits the dataset into training and testing sets. | scikit-learn | Preparing data for model training & evaluation.|
| 3 | `from sklearn.linear_model import LogisticRegression; model = LogisticRegression(random_state=1)` | Creates a Logistic Regression model. | scikit-learn | Binary Classification. |
| 4 | `model.fit(X_train, y_train)` | Trains the Logistic Regression model. | scikit-learn | Model training. |
| 5 | `y_pred = model.predict(X_test)` | Predicts labels for the test dataset. | scikit-learn | Making predictions. |
| 6 | `from sklearn.metrics import accuracy_score; accuracy = accuracy_score(y_test, y_pred)` | Calculates the accuracy of the model. | scikit-learn | Evaluating model performance. |
| 7 | `import pandas as pd; df = pd.DataFrame(X, columns=iris.feature_names)` | Creates a Pandas DataFrame from the Iris dataset features. | Pandas | Data manipulation and analysis. |
| 8 | `df 'target' » = y` | Adds the target variable to the DataFrame. | Pandas | Combining features and labels. |
| 9 | `df.head()` | Displays the first few rows of the DataFrame. | Pandas | Inspecting the data. |
| 10 | `df.describe()` | Generates descriptive statistics of the DataFrame. | Pandas | Understanding data distribution. |
This example demonstrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN) using scikit-learn, showing how to generate synthetic clusters, compute DBSCAN clustering, and visualize the results, including core and non-core samples.
