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Data Science with Python Complete Course

Data Science with Python Complete Course

With this Powerful All-In-One Python Data Science course, you'll know it all: visualization, stats, machine learning, data mining, and deep learning! Data Science with Python Complete Course

What you'll learn

  • Perform high-level mathematical and technical computing using the NumPy and SciPy packages and data analysis with the Pandas package
  • Gain an in-depth understanding of Data Science processes: data wrangling, data exploration, data visualization, hypothesis building, and testing
  • Master the essential concepts of Python programming, including data types, tuples, lists, dicts, basic operators, and functions.
  • Apply knowledge and actionable insights from data across a broad range of application domains.

Requirements

  • An understanding of the fundamentals of Python programming
  • Basic knowledge of statistics

Description

Today Data Science and Machine Learning are used in almost every industry, including automobiles, banks, health, telecommunications, telecommunications, and more.

As the manager of Data Science and Machine Learning, you will have to research and look beyond common problems, you may need to do a lot of data processing. test data using advanced tools and build amazing business solutions. However, where and how will you learn these skills required in Data Science and Machine Learning?

Science and Mechanical Data require in-depth knowledge on a variety of topics. Scientific data is not limited to knowing specific packages/libraries and learning how to use them. Science and Mechanical Data requires an accurate understanding of the following skills,

  • Understand the complete structure of Science and Mechanical Data
  • Different Types of Data Analytics, Data Design, Scientific Data Transfer Features and Machine Learning Projects
  • Python Programming Skills which is the most popular language in Science and Mechanical Data
  • Machine Learning Mathematics including Linear Algebra, Calculus and how to apply it to Machine Learning Algorithms and Science Data
  • Mathematics and Mathematical Analysis of Data Science
  • Data Science Data Recognition
  • Data processing and deception before installing Learning Machines
  • Machine learning
  • Ridge (L2), Lasso (L1), and Elasticnet Regression / Regularization for Machine Learning
  • Selection and Minimization Feature for Machine Learning Models
  • Selection of Machine Learning Model using Cross Verification and Hyperparameter Tuning
  • Analysis of Machine Learning Materials Groups
  • In-depth learning uses the most popular tools and technologies of today.

This Data Science and Machine Learning course is designed to consider all of the above, True Data Science and Machine Learning A-Z Course. In most Data Science and Machine Learning courses, algorithms are taught without teaching Python or this programming language. However, it is very important to understand language structure in order to apply any discipline including Data Science and Mechanical Learning.

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