Data Science Coding | Keras and Tensorflow with Grid Search Cross Validation | IRIS Data | WACAMLDS

Keras and Tensorflow with Grid Search Cross Validation | IRIS Data

Keras and TensorFlow are two powerful libraries that are used for building and training machine learning models. Keras is a high-level neural networks API, written in Python, that runs on top of TensorFlow. It is designed to make it easy to build and train neural networks.

Grid Search Cross Validation is a method used to find the best set of parameters for a machine learning model. It works by testing different combinations of parameters and evaluating their performance. The best combination of parameters is then chosen for the final model.

In this example, we will be using the IRIS dataset, which contains information about different variations of the IRIS flower, such as the setosa, virginica, and versicolor. We will use Keras and TensorFlow to build a neural network model and use Grid Search Cross Validation to find the best set of parameters for the model.

First, we will preprocess the IRIS data and split it into training and testing sets. Next, we will use Keras to define the neural network architecture and TensorFlow to train the model. We will then use Grid Search Cross Validation to test different combinations of parameters, such as the number of hidden layers and the number of neurons in each layer, and evaluate their performance.

After the model is trained and the best set of parameters is found, we can use it to classify new IRIS flowers based on their characteristics.

In summary, The Keras and Tensorflow with Grid Search Cross Validation using IRIS Data is a powerful combination of libraries and techniques that can be used to classify the IRIS flowers. Keras and Tensorflow are used to build and train a neural network and Grid Search Cross Validation is used to fine-tune the best set of parameters for the model.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Keras and Tensorflow with Grid Search Cross Validation.


Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!


ML Classification in Python | Data Science Tutorials | Tensorflow | Keras | IRIS | Deep Learning

Data Science Coding | SKLEARN XGBoost Classifier with Grid Search Cross Validation | WACAMLDS

SKLEARN Gradient Boosting Classifier with Grid Search Cross Validation