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Hyperparameter Tuning of Decision Tree Classifier Using GridSearchCV

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The models can have many hyperparameters and finding the best combination of the parameter using grid search methods. Grid search is a technique for tuning hyperparameter that may facilitate build a model and evaluate a model for every combination of algorithms parameters per grid. We might use 10 fold cross-validation to search the best value for that tuning hyperparameter. These values are called hyperparameters. To get the simplest set of hyperparameters we will use the Grid Search method.


Telecom Churn Prediction using Machine Learning, Python, and GridDB

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Customer churn is a key business concept that determines the number of customers that stop doing business with a specific company. The churn rate is then defined as the rate by which a company loses customers in a given time frame. For example, a churn rate of 15%/year means that a company loses 15% of its total customer base every year. Customer churn takes special importance in the telecommunication sector, given the increasing competition and appearance of new telecommunication companies. For this reason, the telecom industry expects high churn rates every year.


Building a Text Preprocessing Microservice with FastAPI

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Each inherited class overrides the regex and replace_token with its values representing a system's functionality. The input validation is very straightforward, we just need to create a class describing what the data sent by a client should look like. The following code shows this implementation. The StringTransformation class enumerates all the accepted values for the variable steps in the PreprocessingRequest class, each one representing one functionality. The PreprocessingRequest is the main class, it describes how the data sent should look like.


How To Become A Master In Machine Learning With Python?

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Machine learning training is something long-term holds. We need more choices for personalization, great proposals, and brilliantly look highlights. The address presently emerges: which is the finest programming language for machine learning? Python training is the arrangement for this. Machine learning online course is best learned in Python online training.


Python and Data Science from Scratch With RealLife Exercises

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Welcome to my "Python and Data Science from Scratch With Real Life Exercises" course. OAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you're interested in machine learning, data mining, or data analysis, Udemy has a course for you. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. Essentially, data science is the key to getting ahead in a competitive global climate. Python instructors on OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels. Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability.


8 Python Frameworks For Data Science

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Create better design patterns and avoid duplicate or insecure code with Data Science Frameworks. The swiftly changing global marketplace requires companies to take a more sophisticated approach to market dominance. Innovate companies now use data science to attract new clients, recommend products, increase sales, and improve customer satisfaction, ultimately helping them gain a competitive advantage. Data Science is simply the study of data. It leverages domain expertise from mathematics, statistics, and programming to extract, analyze, visualize, and manage data to find unseen patterns, create insights and make powerful data-driven decisions.


GitHub - unitaryai/detoxify: Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at contact@unitary.ai.

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Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at contact@unitary.ai. - GitHub - unitaryai/detoxify: Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at contact@unitary.ai.


GitHub - stellargraph/stellargraph: StellarGraph - Machine Learning on Graphs

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StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can contain people as nodes and friendships between them as links, with data like a person's age and the date a friendship was established. StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy.


Private Artificial Intelligence: Machine Learning on Encrypted Data

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By Kristin Lauter Artificial intelligence (AI) refers to the science of utilizing data to formulate mathematical models that predict outcomes with high assurance. Such predictions can be used to make decisions automatically or give recommendations with high confidence. Training mathematical models to make predictions based on data is called machine learning (ML) in the computer science community. Tremendous progress in ML over the last two decades has led to impressive advances in computer...


Top 10 open-source Responsible AI toolkits

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According to Accenture's 2022 Tech Vision research, only 35% of global consumers trust how organisations implement AI. And 77% think organisations must be held accountable for their misuse of AI. "Responsible AI practice is starting to go mainstream. In fact, Big Tech has large in-house teams and divisions under their Responsible AI practice," said Nikhil Kurhe, co-founder and CEO, of Finarkein Analytics. Responsible AI toolkits can make AI applications and systems fair, robust, and transparent. We have made a list of toolkits and resources to help implement Responsible AI.