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Do want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model and you have no programming skill? Do you want to get started with learning data science? This bundle is going to guide you to the basics and the principles behind machine learning. Machine learning is an active research domain and includes several different approaches. This bundle is going to help you understand the different approaches of machine learning and neural networks. It will guide you through the steps you need to build a machine learning model. Machine learning implies programming. This bundle will teach you Python programming. This bundle does not require any pre-programming skills. It will help to get you started in Python programming, as well as how to use Python libraries to analyze data and apply machine learning. Overall, this bundle is a go-to guide for getting started in machine learning modeling using Python programming. Once you get through the bundle, you will be able to develop your own machine learning models using Python. Through this bundle, you will learn: Principles of machine learning Types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning Advantages of each type of machine learning Principle and types of neural networks Steps to develop and fit artificial neural network model Getting started and installing Python Tools and platforms for Python programming How to use pandas, NumPy and matplotlib Python libraries How to develop a simple linear and logistic machine learning model How to develop and train a multi-layer artificial neural network two ways: from scratch and using the Python libraries When to use each type of machine learning The general concept of artificial neural n
This bundle aims to demystify machine learning and neural networks for beginners with no prior programming experience. It guides readers through the fundamental principles of machine learning, covering supervised, unsupervised, semi-supervised, and reinforcement learning, along with the advantages of each. The material also introduces Python programming and essential libraries like pandas, NumPy, and matplotlib, enabling users to build and fit machine learning models from scratch and using libraries.
This book is structured as a comprehensive guide for individuals new to machine learning and data science, particularly those without a programming background. It focuses on practical application through Python, covering foundational concepts and essential libraries. The content suggests an accessible introduction to building machine learning models.
Page Count:
311
Publication Date:
2019-09-18
Publisher:
Amazon Digital Services LLC - KDP Print US
ISBN-10:
1694100952
ISBN-13:
9781694100955
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