Machine Learning Basics: A Practical and High-Level Introduction to Machine Learning for Beginners
0
0
As an Amazon Associate and affiliate partner, Menrva Books earns from qualifying purchases. Learn more
Author Description
Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. In this step-by-step guide you will learn: How to download free datasets What tools and machine learning libraries you need Data scrubbing techniques, including one-hot encoding, binning, and dealing with missing data Preparing data for analysis, including k-fold Validation Regression analysis to create trend lines Clustering, including k-means clustering, to find new relationships The basics of Neural Networks Bias/Variance to improve your machine learning model Decision Trees to decode classification How to build your first Machine Learning Model to predict house values using Python
Menrva's Summary
THE THESIS
This book introduces the fundamental concepts and practical applications of machine learning for beginners. The author guides readers through the process of acquiring and preparing data, exploring various machine learning techniques such as regression analysis, clustering, and decision trees, and culminates in building a predictive model for house values using Python. It aims to equip individuals with the essential knowledge to leverage machine learning for data analysis and career advancement.
THE SCOPE MAP
- Downloading and utilizing free datasets
- Essential tools and machine learning libraries
- Data scrubbing techniques: one-hot encoding, binning, handling missing data
- Data preparation for analysis, including k-fold validation
- Regression analysis for trend line creation
- Clustering methods, including k-means clustering
- Introduction to Neural Networks
- Understanding Bias/Variance for model improvement
- Decision Trees for classification
- Building a first Machine Learning Model to predict house values using Python
THE AUTHORITY PERSPECTIVE
The book is positioned as a practical, high-level introduction to machine learning, targeting beginners. Its structure suggests a hands-on approach, guiding users through essential steps from data acquisition to model building. The inclusion of specific techniques like one-hot encoding, k-fold validation, and k-means clustering indicates a focus on foundational skills. The practical application of predicting house values with Python serves as a clear objective for the reader's learning.
Page Count:
151
Publication Date:
2021-02-27
Publisher:
Independently Published
ISBN-13:
9798714299582
Science
Non-Fiction
Science & Technology
Data Science
Python
Python Programming
Machine Learning
Community Tags
Similar Books
