Introduction to Machine Learning with Python: A Guide for Beginners in Data Science PDF
Author(s): Nedal, Daniel;Morgan, Peters
Publisher: Createspace Independent Publishing Platform;AI Sciences LLC, Year: 2018
******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning more about Machine Learning using Python? This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions.
From AI Sciences Publisher Our books may be the best one for beginners; it’s a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations.
Step By Step Guide and Visual Illustrations and Examples This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning. Instead of tough math formulas, this book contains several graphs and images which detail all important Machine Learning concepts and their applications.
Target Users The book designed for a variety of target audiences. The most suitable users would include:
Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field.
Software developers and engineers with a strong programming background but seeking to break into the field of machine learning.
Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird’s eye view of current techniques and approaches.
What’s Inside This Book?
Supervised Learning Algorithms
Unsupervised Learning Algorithms
Semi-supervised Learning Algorithms
Reinforcement Learning Algorithms
Overfitting and underfitting
The Bias-Variance Trade-off
Feature Extraction and Selection
A Regression Example: Predicting Boston Housing Prices
How to forecast and Predict
Popular Classification Algorithms
Introduction to K Nearest Neighbors
Introduction to Support Vector Machine
Example of Clustering
Running K-means with Scikit-Learn
Introduction to Deep Learning using TensorFlow
Deep Learning Compared to Other Machine Learning Approaches
Applications of Deep Learning
How to run the Neural Network using TensorFlow
Cases of Study with Real Data
Sources & References
Frequently Asked Questions
Q: Is this book for me and do I need programming experience? A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you’ll be OK.
Q: Does this book include everything I need to become a Machine Learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning.