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English | 2021 | ISBN: 9389328977 | 474 pages | True EPUB | 5.46 MB
Hands-On ML problem solving and creating solutions using Python.
KEY FEATURES
Introduction to Python ProgrammingPython for Machine LearningIntroduction to Machine LearningIntroduction to Predictive Modelling, Supervised and Unsupervised AlgorithmsLinear Regression, Logistic Regression and Support Vector Machines
DESCRIPTION
You will learn about the fundamentals of Machine Learning and Python programming post, which you will be introduced to predictive modelling and the different methodologies in predictive modelling. You will be introduced to Supervised Learning algorithms and Unsupervised Learning algorithms and the difference between them.
We will focus on learning supervised machine learning algorithms covering Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Artificial Neural Networks. For each of these algorithms, you will work hands-on with open-source datasets and use python programming to program the machine learning algorithms. You will learn about cleaning the data and optimizing the features to get the best results out of your machine learning model. You will learn about the various parameters that determine the accuracy of your model and how you can tune your model based on the reflection of these parameters.
WHAT WILL YOU LEARN
Get a clear vision of what is Machine Learning and get familiar with the foundation principles of Machine learning.Understand the Python language-specific libraries available for Machine learning and be able to work with those libraries.Explore the different Supervised Learning based algorithms in Machine Learning and know how to implement them when a real-time use case is presented to you.Have hands-on
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