feat: version change + api documentation updated

src/lib.rs

@@ -10,16 +10,11 @@
 //!
 //! Welcome to SmartCore, the most advanced machine learning library in Rust!
 //!
-//! In SmartCore you will find implementation of these ML algorithms:
-//! * __Regression__: Linear Regression (OLS), Decision Tree Regressor, Random Forest Regressor, K Nearest Neighbors
-//! * __Classification__: Logistic Regressor, Decision Tree Classifier, Random Forest Classifier, Supervised Nearest Neighbors (KNN)
-//! * __Clustering__: K-Means
-//! * __Matrix Decomposition__: PCA, LU, QR, SVD, EVD
-//! * __Distance Metrics__: Euclidian, Minkowski, Manhattan, Hamming, Mahalanobis
-//! * __Evaluation Metrics__: Accuracy, AUC, Recall, Precision, F1, Mean Absolute Error, Mean Squared Error, R2
+//! SmartCore features various classification, regression and clustering algorithms including support vector machines, random forests, k-means and DBSCAN,
+//! as well as tools for model selection and model evaluation.
 //!
-//! Most of algorithms implemented in SmartCore operate on n-dimentional arrays. While you can use Rust vectors with all functions defined in this library
-//! we do recommend to go with one of the popular linear algebra libraries available in Rust. At this moment we support these packages:
+//! SmartCore is well integrated with a with wide variaty of libraries that provide support for large, multi-dimensional arrays and matrices. At this moment,
+//! all Smartcore's algorithms work with ordinary Rust vectors, as well as matrices and vectors defined in these packages:
 //! * [ndarray](https://docs.rs/ndarray)
 //! * [nalgebra](https://docs.rs/nalgebra/)
 //!
@@ -28,21 +23,21 @@
 //! To start using SmartCore simply add the following to your Cargo.toml file:
 //! ```ignore
 //! [dependencies]
-//! smartcore = "0.1.0"
+//! smartcore = "0.2.0"
 //! ```
 //!
-//! All ML algorithms in SmartCore are grouped into these generic categories:
+//! All machine learning algorithms in SmartCore are grouped into these broad categories:
 //! * [Clustering](cluster/index.html), unsupervised clustering of unlabeled data.
 //! * [Martix Decomposition](decomposition/index.html), various methods for matrix decomposition.
 //! * [Linear Models](linear/index.html), regression and classification methods where output is assumed to have linear relation to explanatory variables
 //! * [Ensemble Models](ensemble/index.html), variety of regression and classification ensemble models
 //! * [Tree-based Models](tree/index.html), classification and regression trees
 //! * [Nearest Neighbors](neighbors/index.html), K Nearest Neighbors for classification and regression
+//! * [Naive Bayes](naive_bayes/index.html), statistical classification technique based on Bayes Theorem
+//! * [SVM](svm/index.html), support vector machines
 //!
-//! Each category is assigned to a separate module.
 //!
-//! For example, KNN classifier is defined in [smartcore::neighbors::knn_classifier](neighbors/knn_classifier/index.html). To train and run it using standard Rust vectors you will
-//! run this code:
+//! For example, you can use this code to fit a [K Nearest Neighbors classifier](neighbors/knn_classifier/index.html) to a dataset that is defined as standard Rust vector:
 //!
 //! ```
 //! // DenseMatrix defenition