Merge pull request #2109 from Kingston257/my-initial-contribution

Fix typo in README.md

README.md

@@ -94,7 +94,7 @@ The table below lists the recommendation algorithms currently available in the r
 | LightFM/Factorization Machine | Collaborative Filtering | Factorization Machine algorithm for both implicit and explicit feedbacks. It works in the CPU environment. | [Quick start](examples/02_model_collaborative_filtering/lightfm_deep_dive.ipynb) |
 | LightGBM/Gradient Boosting Tree<sup>*</sup> | Content-Based Filtering | Gradient Boosting Tree algorithm for fast training and low memory usage in content-based problems. It works in the CPU/GPU/PySpark environments. | [Quick start in CPU](examples/00_quick_start/lightgbm_tinycriteo.ipynb) / [Deep dive in PySpark](examples/02_model_content_based_filtering/mmlspark_lightgbm_criteo.ipynb) |
 | LightGCN | Collaborative Filtering | Deep learning algorithm which simplifies the design of GCN for predicting implicit feedback. It works in the CPU/GPU environment. | [Deep dive](examples/02_model_collaborative_filtering/lightgcn_deep_dive.ipynb) |
-| GeoIMC<sup>*</sup> | Collaborative Filtering | Matrix completion algorithm that has into account user and item features using Riemannian conjugate gradients optimization and following a geometric approach. It works in the CPU environment. | [Quick start](examples/00_quick_start/geoimc_movielens.ipynb) |
+| GeoIMC<sup>*</sup> | Collaborative Filtering | Matrix completion algorithm that takes into account user and item features using Riemannian conjugate gradient optimization and follows a geometric approach. It works in the CPU environment. | [Quick start](examples/00_quick_start/geoimc_movielens.ipynb) |
 | GRU | Collaborative Filtering | Sequential-based algorithm that aims to capture both long and short-term user preferences using recurrent neural networks. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/sequential_recsys_amazondataset.ipynb) |
 | Multinomial VAE | Collaborative Filtering | Generative model for predicting user/item interactions. It works in the CPU/GPU environment. | [Deep dive](examples/02_model_collaborative_filtering/multi_vae_deep_dive.ipynb) |
 | Neural Recommendation with Long- and Short-term User Representations (LSTUR)<sup>*</sup> | Content-Based Filtering | Neural recommendation algorithm for recommending news articles with long- and short-term user interest modeling. It works in the CPU/GPU environment. | [Quick start](examples/00_quick_start/lstur_MIND.ipynb) |