Advance Certificate course in Data Science

In today's world, data is everything. It's used to make decisions large and small, from what we eat for dinner to which countries go to war. And the field of data science is only getting bigger and more important. So what is data science? Simply put, it's the study of data. This can encompass anything from collecting and organizing data to analyzing and making predictions based on that data. Data science is a relatively new field, but it draws on many existing disciplines, including statistics, computer science, and mathematics. As such, there is no one set path to becoming a data scientist. 

However, taking a data science course can give you the skills and knowledge you need to start a successful career in this exciting field. In a data science course, you'll learn how to collect and analyze data using various tools and techniques. You'll also develop your critical thinking and problem-solving skills, which will come in handy when working with complex datasets. And of course, you'll learn how to use statistical methods to draw conclusions from your data. So if you're interested in a career in data science, be sure to check out our data science course. It's the perfect way to get started in this dynamic and rapidly-growing field.

So, if you're looking for a way to boost your career or simply want to acquire some useful skills, a Data Science course is a great option. So if you're thinking of pursuing a career in data science, then join our course for more details, you can check 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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