Hands-on Supervised Learning with Python: Learn How to Solve Machine Learning Problems with Supervised Learning Algorithms Using Python
Author: Lakshmi T C,Gnana and Shang,Madeleine
Publisher finelybook 出版社: BPB Publications (November 26,2020)
Language 语言: English
Print Length 页数: 384 pages
ISBN-10: 9389328977
ISBN-13: 9789389328974
Book Description
Hands-On ML problem solving and creating solutions using Python.
Key Features
Introduction to Python Programming
Python for Machine Learning
Introduction to Machine Learning
Introduction to Predictive Modelling,Supervised and Unsupervised Algorithms
Linear Regression,Logistic Regression and Support Vector Machines
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 with Data Exploration,Data Cleaning,Data Preprocessing and Model implementation.
Get to know the basics of Deep Learning and some interesting algorithms in this space.
Choose the right model based on your problem statement and work with EDA techniques to get good accuracy on your model
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