Python Machine Learning: Introduction to Machine Learning with Python

By Frank Millstein

Machine learning is the science of getting machines and computers to act and learn on their own without being programmed explicitly. In just the past decade, this field has given us practical speech recognition, self-driving cars, greatly improved understanding of the overall human genome, effective web search and much more. Therefore, there is no wondering why machine learning is so pervasive today.

In this book, you will learn more about interpreting machine learning techniques using Python. You will also gain practice as you implement the most popular machine learning techniques on some real-world examples and you will learn both about the theoretical and practical machine learning implementation using Python's machine learning libraries.

At the end of the book, you will be able to cope with more complex machine learning issues solving your own problems using Python and its libraries specifically crafted for machine learning.

Here Is A Preview Of What You'll Learn Here…

• Basics behind machine learning techniques
• Different machine learning algorithms
• Fundamental machine learning applications and their importance
• Getting started with machine learning in Python, installing and starting SciPy
• Loading data and importing different libraries
• Data summarization and data visualization
• Evaluation of machine learning models and making predictions
• Most commonly used machine learning algorithms, linear and logistic regression, decision trees support vector machines, k-nearest neighbors, random forests
• Solving multi-clasisfication problems
• Data visualization with Matplotlib and data transformation with Pandas and Scikit-learn
• Solving multi-label classification problems
• And much, much more...

Get this book NOW and learn more about Machine Learning with Python!

• Language: English
• Publisher: Frank Millstein
• Release date: Oct 19, 2019
• ISBN: 9781393909989

Book preview

By Frank Millstein

WHAT IS IN THE BOOK?

INTRODUCTION

BASIC MACHINE LEARNING CONCEPTS

MACHINE LEARNING ALGORITHMS

MACHINE LEARNING APPLICATIONS

EVOLUTION OF MACHINE LEARNING

HOW WE GET MACHINES TO LEARN ON THEIR OWN

THE GROWING IMPORTANCE OF MACHINE LEARNING

MACHINE LEARNING LIMITATIONS AND CHALLENGES

CHAPTER 1: GETTING STARTED WITH MACHINE LEARNING IN PYTHON

INSTALLING AND STARTING PYTHON SCIPY

STARTING PYTHON

LOADING DATA

IMPORTING LIBRARIES

LOADING DATASET

SUMMARIZING THE DATASET

DATA VISUALIZATION

EVALUATING ALGORITHMS

BUILDING MODELS

SELECTING BEST MODELS

MAKING PREDICTIONS

CHAPTER 2: MACHINE LEARNING ALGORITHMS

LINEAR REGRESSION

LOGISTIC REGRESSION

DECISION TREES

SUPPORT VECTOR MACHINES

K-NEAREST NEIGHBORS

RANDOM FORESTS

K-MEAN CLUSTERING

PRINCIPAL COMPONENTS ANALYSIS

CHAPTER 3: SOLVING CLASSIFICATION PROBLEMS

TRAINING TEST DATA

LOADING DATA USING PANDAS

MATPLOTLIB DATA VISUALIZATION

TRANSFORMING DATA WITH SKLEARN AND PANDAS

MODEL TRAINING

PREDICTING USING THE CLASSIFICATION MODEL

EVALUATION OF THE CLASSIFICATION MODEL

CHAPTER 4: SOLVING MULTI-LABEL CLASSIFICATION PROBLEMS

GENERATING MULTI-LABEL DATASETS

PROBLEM TRANSFORMATION

ADAPTED ALGORITHM

ENSEMBLE APPROACHES

LAST WORDS

Copyright © 2018 by Frank Millstein- All rights reserved.

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INTRODUCTION

Machine learning is one of the artificial intelligence applications which provides systems, machines, and computers a method to automatically learn without the need for humans to program them. The method of teaching that they learn from are in the examples which are submitted to them and, in the fact that the systems, machines, and computers can learn and improve from gained experience as they process. Machine learning is mainly focused on the development of various computer programs, which can easily and accurately access any kind of data and use it to learn completely by themselves.

The process of machine learning begins with data, examples, observations from direct experiences, or instructions to look for different patterns in data and make better decisions in the future that are based on the examples humans provide to the machines. The main goal of machine learning is to allow the machines and computers to learn from examples automatically without any human assistance or intervention needed.

In fact, machine learning is the science of getting machines and computers to learn as well as act as humans do. Machine learning is also improving machines’ learning over time in a completely autonomous fashion just by feeding them information, data, and examples that include real-world interactions. As any other computer science concept, machine learning has different definitions, which all arrive at the same conclusion.

Machine learning is purely based on algorithms which can learn new information from data without any need to rely on the traditional, rules-based programming. Therefore, machine learning algorithms can straightforwardly figure out how to perform valuable and important tasks just by observing examples and data.

Therefore, this is the science of getting machines to act as humans do. Machine learning at its most fundamental practice is the science of using models and algorithms to parse data, learn valuable information from it, and