Introduction To Machine Learning With Python Andreas Mueller Pdf Free Download

Introduction To Machine Learning With Python Andreas Mueller Pdf Free Download is for intermediate Python developers interested in exploring machine learning concepts and pragmatic techniques. introduction to machine learning with python andreas mueller pdf free download will teach you the basics of building machine learning models and runtimes and how to wield and extend off-the-shelf algorithms depending on the task at hand.

The About Introduction To Machine Learning With Python Andreas Mueller Pdf Download should be the first book for anyone who has a bit of programming background and want to overview how machine learning would look like without deep diving into the linear algebra and/or any relevant math.

A practical guide to building machine learning solutions with Python. Discover multiple ways to solve different problems in machine learning. This Book Is For If you are a beginner to data science or are looking to make your way into this field, then this book is ideal for you. No prior experience in Python or machine learning is required, but you should know the basics of programming. What You Will Learn in introduction to machine learning with python andreas mueller pdf free download is understanding supervised vs unsupervised learning Identify different types of neural networks, start with simple examples and work your way up to more complex applications. Get acquainted with popular datasets used for testing algorithms and set up your python environment for data analytics, implementing various algorithms for text,

About the Introduction To Machine Learning With Python Andreas Mueller Pdf Free Download

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this Introduction To Machine Learning With Python Andreas Mueller Pdf Free Download will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

If you are new to the topic of Machine Learning, this is definitely the go-to book. While staying away from detailed mathematics, this book gives a good overview of the most common techniques used in the field.

The Introduction To Machine Learning With Python Andreas Mueller Pdf Free Download uses Python, scikit-learn, bumpy, etc that are well defined and have been widely used, and take examples one by one, but not with serious math or from the scratch but using existing scikit-learn. Probably some people would like to learn all the stuff from the scratch, including how it works and what’s the math behind it, etc etc, but there are other people like me who what to have brief overview of what’s it look like and how it’s going to work first, and then gradually dive into inch by inch.

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:

  • Fundamental concepts and applications of machine learning
  • Advantages and shortcomings of widely used machine learning algorithms
  • How to represent data processed by machine learning, including which data aspects to focus on
  • Advanced methods for model evaluation and parameter tuning
  • The concept of pipelines for chaining models and encapsulating your workflow
  • Methods for working with text data, including text-specific processing techniques
  • Suggestions for improving your machine learning and data science skills

Table of Contents of Introduction To Machine Learning With Python Andreas Mueller Pdf Free Download

Who Should Read This Book
Why We Wrote This Book
Navigating This Book
Online Resources
Conventions Used in This Book
Using Code Examples
O’Reilly Safari
How to Contact Us
From Andreas
From Sarah
1. Introduction
Why Machine Learning?
Problems Machine Learning Can Solve
Knowing Your Task and Knowing Your Data
Why Python?
Installing scikit-learn
Essential Libraries and Tools
Jupyter Notebook
Python 2 Versus Python 3
Versions Used in this Book
A First Application: Classifying Iris Species
Meet the Data
Measuring Success: Training and Testing Data
First Things First: Look at Your Data
Building Your First Model: k-Nearest Neighbors
Making Predictions
Evaluating the Model
Summary and Outlook
2. Supervised Learning
Classification and Regression
Generalization, Overfitting, and Underfitting
Relation of Model Complexity to Dataset Size
Supervised Machine Learning Algorithms
Some Sample Datasets
k-Nearest Neighbors
Linear Models
Naive Bayes Classifiers
Decision Trees
Ensembles of Decision Trees
Kernelized Support Vector Machines
Neural Networks (Deep Learning)
Uncertainty Estimates from Classifiers
The Decision Function
Predicting Probabilities
Uncertainty in Multiclass Classification
Summary and Outlook
3. Unsupervised Learning and Preprocessing
Types of Unsupervised Learning
Challenges in Unsupervised Learning
Preprocessing and Scaling
Different Kinds of Preprocessing
Applying Data Transformations
Scaling Training and Test Data the Same Way
The Effect of Preprocessing on Supervised Learning
Dimensionality Reduction, Feature Extraction, and Manifold Learning
Principal Component Analysis (PCA)
Non-Negative Matrix Factorization (NMF)
Manifold Learning with t-SNE
k-Means Clustering
Agglomerative Clustering
Comparing and Evaluating Clustering Algorithms
Summary of Clustering Methods
Summary and Outlook
4. Representing Data and Engineering Features
Categorical Variables
One-Hot-Encoding (Dummy Variables)
Numbers Can Encode Categoricals
Binning, Discretization, Linear Models, and Trees
Interactions and Polynomials
Univariate Nonlinear Transformations
Automatic Feature Selection
Univariate Statistics
Model-Based Feature Selection
Iterative Feature Selection
Utilizing Expert Knowledge
Summary and Outlook
5. Model Evaluation and Improvement
Cross-Validation in scikit-learn
Benefits of Cross-Validation
Stratified k-Fold Cross-Validation and Other Strategies
Grid Search
Simple Grid Search
The Danger of Overfitting the Parameters and the Validation Set
Grid Search with Cross-Validation
Evaluation Metrics and Scoring
Keep the End Goal in Mind
Metrics for Binary Classification
Metrics for Multiclass Classification
Regression Metrics
Using Evaluation Metrics in Model Selection
Summary and Outlook
6. Algorithm Chains and Pipelines
Parameter Selection with Preprocessing
Building Pipelines
Using Pipelines in Grid Searches
The General Pipeline Interface
Convenient Pipeline Creation with make_pipeline
Accessing Step Attributes
Accessing Attributes in a Pipeline inside GridSearchCV
Grid-Searching Preprocessing Steps and Model Parameters
Grid-Searching Which Model To Use
Summary and Outlook
7. Working with Text Data
Types of Data Represented as Strings
Example Application: Sentiment Analysis of Movie Reviews
Representing Text Data as a Bag of Words
Applying Bag-of-Words to a Toy Dataset
Bag-of-Words for Movie Reviews
Rescaling the Data with tf–idf
Investigating Model Coefficients
Bag-of-Words with More Than One Word (n-Grams)
Advanced Tokenization, Stemming, and Lemmatization
Topic Modeling and Document Clustering
Latent Dirichlet Allocation
Summary and Outlook
8. Wrapping Up
Approaching a Machine Learning Problem
Humans in the Loop
From Prototype to Production
Testing Production Systems
Building Your Own Estimator
Where to Go from Here
Other Machine Learning Frameworks and Packages
Ranking, Recommender Systems, and Other Kinds of Learning
Probabilistic Modeling, Inference, and Probabilistic Programming
Neural Networks
Scaling to Larger Datasets
Honing Your Skills

about the authors of Introduction To Machine Learning With Python Andreas Mueller Pdf Free Download

Andreas Muller received his PhD in machine learning from the University of Bonn. After working as a machine learning researcher on computer vision applications at Amazon for a year, he recently joined the Center for Data Science at the New York University. In the last four years, he has been maintainer and one of the core contributor of scikit-learn, a machine learning toolkit widely used in industry and academia, and author and contributor to several other widely used machine learning packages. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.

Sarah Guido is a data scientist who has spent a lot of time working in start-ups. She loves Python, machine learning, large quantities of data, and the tech world. She is an accomplished conference speaker, currently resides in New York City, and attended the University of Michigan for grad school.

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