Hands-On Supervised Learning with Python: Learn How to Solve Machine Learning Problems with Supervised Learning Algorithms Using Python Contributor(s): Shang, Madeleine (Author), Lakshmi T. C., Gnana (Author) |
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ISBN: 9389328977 ISBN-13: 9789389328974 Publisher: Bpb Publications OUR PRICE: $28.45 Product Type: Paperback Published: November 2020 * Not available - Not in print at this time * |
Additional Information |
BISAC Categories: - Computers | Data Visualization |
Physical Information: 0.79" H x 7.52" W x 9.25" (1.45 lbs) 384 pages |
Descriptions, Reviews, Etc. |
Publisher Description: Hands-On ML problem solving and creating solutions using Python. Key Features 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 Who this book is for This book is for anyone interested in understanding Machine Learning. Beginners, Machine Learning Engineers and Data Scientists who want to get familiar with Supervised Learning algorithms will find this book helpful. Table of Contents 1. Introduction to Python Programming 2. Python for Machine Learning 3. Introduction to Machine Learning 4. Supervised Learning and Unsupervised Learning 5. Linear Regression: A Hands-on guide 6. Logistic Regression - An Introduction 7. A sneak peek into the working of Support Vector machines(SVM) 8. Decision Trees 9. Random Forests 10. Time Series models in Machine Learning 11. Introduction to Neural Networks 12. Recurrent Neural Networks 13. Convolutional Neural Networks 14. Performance Metrics 15. Introduction to Design Thinking 16. Design Thinking Case Study |