Advance Certificate Course In Data Science | Learn With Talentserve

Data science is a field of study 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. Data science is a multi-disciplinary field that combines statistics, mathematics, computer science, and information science. It has emerged as a key area of focus for businesses and organizations looking to make better use of their data assets. There are many benefits to joining a data science course. This type, of course, can provide you with the skills and knowledge necessary to become a data scientist. 



Additionally, it can also help you to gain an understanding of the different aspects of data science so that you can make more informed decisions about your career. So, should you join a data science course? If you are interested in pursuing a career in data science or if you want to gain a better understanding of this field, then yes, you should definitely consider joining a course.


There are many benefits to taking a data science course. Firstly, it will give you a strong foundation in the area, which will be invaluable if you wish to pursue a career in data science or a related field. Secondly, the skills and knowledge you gain from the course will be invaluable in your current or future role, regardless of what industry you work in. And finally, taking a data science course is a great way to network with other professionals in the field, which can lead to future opportunities. So should you take a data science course? If you're interested in pursuing a career in data science or a related field, then absolutely. Even if you're not looking to change careers, the skills and knowledge you'll gain from the course will be beneficial in your current role. 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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