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Machine Learning for Absolute Beginners - Level 2

Machine Learning for Absolute Beginners - Level 2

  • 30 Day Money Back Guarantee
  • Completion Certificate
  • 24/7 Technical Support

Highlights

  • On-Demand course

  • 3 hours 59 minutes

  • All levels

Description

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library. The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms. By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects. The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2

What You Will Learn

Understand how to use Jupyter Lab for Jupyter notebooks
Learn Python fundamentals
Load large datasets from files using pandas
Uncover techniques to perform data analysis and exploration
Group, sort, export, select, filter, and clean data
Find out how to preview the data frame

Audience

This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.

Approach

With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.

Key Features

Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms

Github Repo

https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 

About the Author
Idan Gabrieli

Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies. Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers. Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.

Course Outline

1. Getting Started with Level 2!

2. Python Fundamentals for Data Science

3. Introduction to the Pandas Library

4. Loading Data into a DataFrame

5. Data Cleaning and Transformation

6. Course Summary

Course Content

  1. Machine Learning for Absolute Beginners - Level 2

About The Provider

Packt
Packt
Birmingham
Founded in 2004 in Birmingham, UK, Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and i...
Read more about Packt

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