Update README.md

Author: chhayac

README.md

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 | Notebook Description| Link | Notes |
 | -------------------| -----|--------|
 | Iris Flower Classification | [Iris_flower_classification.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Iris_flower_classification.ipynb) | Build a neural network model using Keras & Tensorflow. Evaluated the model using scikit learn's k-fold cross validation. | 
-| Recognizing CIFAR-10 images (Part I   - Simple model) | [Recognizing-CIFAR-10-images-Simple-Model.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Simple-Model.ipynb) | Built a simple Convolutional Neural Network(CNN) model to classify CIFAR-10 image dataset with Keras deep learning library achieving classification accuracy of 67.1%. |
-| Recognizing CIFAR-10 images (Part II  - Improved model) | [Recognizing-CIFAR-10-images-Simple-Model.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Improved-Model.ipynb) | Built an improved CNN model by adding more layers with Keras deep learning library achieving classification accuracy of 78.65%. |
-| Recognizing CIFAR-10 images (Part III - Data Augmentation) | [Recognizing-CIFAR-10-images-Improved-Model-Data-Augmentation.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Improved-Model-Data-Augmentation.ipynb) | Built an improved CNN model by data augmentation with Keras deep learning library achieving classification accuracy of 80.73%. |
-| Traffic Sign Recognition using Deep Learning | [Traffic-Sign-Recognition.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Traffic-Sign-Recognition.ipynb) | Built a deep learning model to detect traffic signs using the German Traffic Sign Recognition Benchmark(GTSRB) dataset achieving an accuracy of 98.4%. |
-| Movie Recommendation Engine | [Movie_Recommendation_Engine.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Movie_Recommendation_Engine.ipynb) | Built a movie recommendation engine using k-nearest neighbour algorithm implemented from scratch. |
-| Linear Regression | [Linear_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Linear_Regression.ipynb) | Built a simple linear regression model to predict profit of food truck based on population and profit of different cities. |
-| Multivariate Linear Regression | [Multivariate_Linear_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Multivariate_Linear_Regression.ipynb) | Built a simple multivariate linear regression model to predict the price of a house based on the size of the house in square feet and number of bedrooms in the house. |
+| Recognizing CIFAR-10 images (Part I   - Simple model) | [Recognizing-CIFAR-10-images-Simple-Model.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Simple-Model.ipynb) | Build a simple Convolutional Neural Network(CNN) model to classify CIFAR-10 image dataset with Keras deep learning library achieving classification accuracy of 67.1%. |
+| Recognizing CIFAR-10 images (Part II  - Improved model) | [Recognizing-CIFAR-10-images-Simple-Model.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Improved-Model.ipynb) | Build an improved CNN model by adding more layers with Keras deep learning library achieving classification accuracy of 78.65%. |
+| Recognizing CIFAR-10 images (Part III - Data Augmentation) | [Recognizing-CIFAR-10-images-Improved-Model-Data-Augmentation.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Improved-Model-Data-Augmentation.ipynb) | Build an improved CNN model by data augmentation with Keras deep learning library achieving classification accuracy of 80.73%. |
+| Traffic Sign Recognition using Deep Learning | [Traffic-Sign-Recognition.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Traffic-Sign-Recognition.ipynb) | Build a deep learning model to detect traffic signs using the German Traffic Sign Recognition Benchmark(GTSRB) dataset achieving an accuracy of 98.4%. |
+| Movie Recommendation Engine | [Movie_Recommendation_Engine.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Movie_Recommendation_Engine.ipynb) | Build a movie recommendation engine using k-nearest neighbour algorithm implemented from scratch. |
+| Linear Regression | [Linear_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Linear_Regression.ipynb) | Build a simple linear regression model to predict profit of food truck based on population and profit of different cities. |
+| Multivariate Linear Regression | [Multivariate_Linear_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Multivariate_Linear_Regression.ipynb) | Build a simple multivariate linear regression model to predict the price of a house based on the size of the house in square feet and number of bedrooms in the house. |
 | Sentiment Analysis of Movie Reviews| [Sentiment_Analysis.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Sentiment_Analysis.ipynb)| Experiment to analyze sentiment according to their movie reviews. |
 | Wine quality prediction | [Predicting_wine_quality.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Predicting_wine_quality.ipynb)| Experiment to predict wine quality with feature selection (In progress). |
 | Unsupervised Learning | [unsupervised_learning-Part_1.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/unsupervised_learning-Part_1.ipynb)| Hands-on with Unsupervised learning. |
 | Autoencoders using Fashion MNIST| [Autoencoder_Fashion_MNIST.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Autoencoder_Fashion_MNIST.ipynb)| Building an autoencoder as a classifier using Fashion MNIST dataset. |
-| Logistic Regression| [Logistic_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Logistic_Regression.ipynb)| Built a logistic regression model from scratch |
+| Logistic Regression| [Logistic_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Logistic_Regression.ipynb)| Build a logistic regression model from scratch |
 | Fuzzy string matching| [fuzzy_string_matching.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/fuzzy_string_matching.ipynb)| To study how to compare strings and determine how similar they are in Python. |
-| Spam email classification| [spam_email_classification.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/spam_email_classification.ipynb)| Built a spam detection classification model using an email dataset.
+| Spam email classification| [spam_email_classification.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/spam_email_classification.ipynb)| Build a spam detection classification model using an email dataset.
 | Customer churn prediction| [customer_churn_prediction.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/customer_churn_prediction.ipynb)| To predict if customers churn i.e. unsubscribed or cancelled their service.|