Best Course In Data Science Learn, Solve, And Grow With TalentServe

 Data Science is a multidisciplinary field that involves the use of statistical, mathematical, and computational techniques to extract insights and knowledge from data. It is the practice of applying scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. A Data Science course typically covers a range of topics related to working with data, including:



Statistics: Data Science relies heavily on statistics to analyze data and make informed decisions. Programming: Students will learn programming languages such as Python and R, which are widely used in Data Science. Machine Learning: Machine learning is a subset of artificial intelligence that teaches computers to learn from data and improve their performance over time. Data Visualization: Students will learn to use data visualization tools to represent data in a way that is easy to understand and interpret.

Big Data Technologies: In the modern era, data sets are getting bigger and bigger. So, it is necessary to learn how to work with Big Data technologies such as Hadoop and Spark.


Data Cleaning: Cleaning and preprocessing data is an essential part of data science. So, students will learn to clean, preprocess and transform data. Business Intelligence: Business intelligence tools are used to provide businesses with insights into their operations, and how they can improve their performance. Overall, Data Science is an interdisciplinary field, and a data science course should cover a range of topics related to working with data. 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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