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Complete course of Data Science and machine learning with Python

 

Complete course of Data Science and machine learning with Python

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Complete course of Data Science and machine learning with Python From beginner to expert in Data Science techniques with Python: machine learning, neural networks, text mining


What you'll learn

  • Review of the basics of Python and its data structures
  • Programming environments for Data Science
  • Importing datasets into Python
  • Graph creation and dataset exploration
  • Manipulation and management of datasets
  • Preprocessing and cleaning of data for analysis
  • Machine learning theory and algorithms, supervised and unsupervised methods
  • Machine learning theory and algorithms
  • Model evaluation and validation
  • Text cleaning and analysis
  • Methods for Sentiment Analysis

Requirements

  • Basic knowledge of Python

Description

  • This course on Data Science with Python was created to be a complete path on how data analysis has evolved in recent years starting from classical algebra and statistics. The goal is to accompany a student who has some basic Python on a journey through the various souls of Data Science.
  • We will start with a review of the basics of Python, starting from downloading and installing, setting up the work environment, going through structures, creating functions, using operators and some important functions.
  • We will then move on to see how to manipulate and manage a dataset, extract cases or variables, generate random datasets, calculate basic statistical measures, create graphs with the Matplotlib and Seaborn packages.
  • In the following sections we begin to enter the heart of Data Science with Python, starting with preprocessing: we see how to clean up and normalize a dataset, and how to manage missing data.
  • The next section allows us to start setting up machine learning models with Python: we will see all the most common algorithms, both supervised and unsupervised, such as regression, simple, multiple and logistic, the k-nearest neighbors, the Support Vector Machines , Naive Bayes, decision trees and clustering.
  • We will then move on to the most common ensemble methods, such as Random Forest, Bagging and Boosting, and to the analysis of natural language and its use in machine learning for cataloging texts.


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