Best Data Science Course Learn With Talentserve

Data science is a field of study that combines mathematics, statistics, computer science, and other disciplines to extract knowledge and insights from data. It has become popular recently as businesses have increasingly relied on data to make decisions. A Data Science course can provide you with the skills and knowledge necessary to pursue a career in this growing field. In a Data Science course, you will learn how to clean and process data, how to build models to extract insights from data, and how to communicate your findings to others. 



You will also gain experience working with real-world data sets. A Data Science course can benefit you in many ways. It can help you develop crucial skills that are in high demand by employers. It can also give you the opportunity to learn from experienced practitioners and professors. And, if you choose to pursue a career in data science, it can provide a solid foundation for your future studies. By the end of the course, you will be able to effectively analyze and interpret data, build predictive models, and communicate your findings to decision-makers. 


The Data Science course is an ideal choice for those who want to pursue a career in data science or who want to gain the skills necessary to work with data in their current field. The course is also a good choice for those who are looking to improve their data analysis skills. For more details, visit our site:https://www.talentserve.org/course-datascience  


 COURSE CONTENT


  • Introduction to Python

  • Basic Steps

  • NUMPY

  • Data Visualization

  • Pandas

  • Exceptions and Errors

  • Introduction to Artificial Intelligence and Machine Learning

  • Data Wrangling and Manipulation

  • Supervised Learning

  • Supervised Learning-Classification

  • Unsupervised learning

  • Machine Learning Pipeline Building

  • Decision Tree Analysis and Ensemble Learning

  • AI and Deep learning introduction

  • Artificial Neural Network

  • Deep Neural Network & Tools

  • Deep Neural Net optimization, tuning, interpretability

  • Convolutional Neural Net

  • Recurrent Neural Networks

  • Overfit and underfit

  • Transfer Learning

  • Working with Generative Adversarial Networks

  • Pytorch

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