Advanced Certificate course in Data Science

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining. A data science course can cover a broad range of topics, including mathematics, statistics, computer science, machine learning, artificial intelligence (AI), and programming. The aim of a data science course is to provide students with the skills and knowledge they need to pursue a career in data science or a related field. There are many benefits of taking a data science course. 

Firstly, it can help you develop an understanding of the fundamentals of data science. Secondly, a data science course can provide you with the skills and knowledge you need to pursue a career in data science or a related field. Thirdly, taking a data science course can help you stay up-to-date with the latest advancements in the field of data science. Finally, a data science course can help you network with other professionals in the field of data science.

A data science course can provide you with the skills and knowledge necessary to pursue a career in this growing field. Data science is a rapidly growing field with many job opportunities available for those with the correct skill set. A data science course can help you to develop these essential skills. The benefits of a data science course include; The opportunity to learn about a rapidly growing field -Develop essential skills for a career in data science -Learn from experienced instructors -Gain hands-on experience with real-world data sets. For more details, visit our site: https://www.talentserve.org/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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