## Frustrated with one-off articles and too much math?

### Take the *Next Step* and Get Tutorial-Based Playbooks

that will *Guide You* to a *Specific Result*

## Welcome to: the Machine Learning Mastery EBook Catalog

Beginner | Intermediate | Advanced | Bundles | Donate

## Beginner EBooks

### Basics of Linear Algebra for Machine Learning

#### Discover the Mathematical Language of Data in Python

Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more.

GREAT VALUE### Statistical Methods for Machine Learning

#### Discover How to Transform Data into Knowledge with Python

Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, and much more.

### Probability for Machine Learning

#### Discover How To Harness Uncertainty With Python

Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, including Bayes Theorem, Bayesian Optimization, Maximum Likelihood Estimation, Entropy, Probability Distributions, Types of Probability, Naive Classifier Models, and much more.

NEW### Master Machine Learning Algorithms

#### Discover How They Work and Implement Them From Scratch

A gentle introduction to the procedures to learn models from data for 10 popular and useful supervised machine learning algorithms used for predictive modeling. Each algorithm includes one or more step-by-step tutorials explaining exactly how to plug in numbers into each equation and what numbers to expect as output. Each tutorial is designed to be completed in a spreadsheet.

TOP SELLER### Machine Learning Algorithms From Scratch

#### With Python

Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.

## Intermediate EBooks

### Machine Learning Mastery With Weka

#### Analyze Data, Develop Models and Work Through Projects

Discover how to load data, transform data, evaluate machine learning algorithms and work through machine learning projects end-to-end without writing a single line of code using the Weka open source platform. A step-by-step tutorial approach is used throughout the 18 lessons and 3 end-to-end projects, showing you exactly what to click and exactly what results to expect.

### Machine Learning Mastery With Python

#### Understand Your Data, Create Accurate Models and work Projects End-to-End

Discover the process that you can use to get started and get good at applied machine learning for predictive modeling with the Python ecosystem including Pandas and scikit-learn. This book will lead you from a developer who is interested in machine learning with Python to a developer who has the resources and capability to work through a new dataset end-to-end using Python and develop accurate predictive models.

TOP SELLER### Machine Learning Mastery With R

#### Get Started, Build Accurate Models and Work Through Projects Step-by-Step

There’s a reason that R is the most popular platform for applied machine learning for professional data scientists. Discover exactly how to work through a predictive modeling machine learning project step-by-step with R and the widely adopted caret library.

### Introduction to Time Series Forecasting With Python

#### How to Prepare Data and Develop Models to Predict the Future

Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

### Imbalanced Classification with Python

#### Better Metrics, Balance Skewed Classes, Cost-Sensitive Learning

Imbalanced classification are those classification tasks where the distribution of examples across the classes is not equal. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently develop robust models for your own imbalanced classification projects.

### Data Preparation for Machine Learning

#### Data Cleaning, Feature Selection, and Data Transforms in Python

Data preparation involves transforming raw data in to a form that can be modeled using machine learning algorithms. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively prepare your data for predictive modeling with machine learning.

NEW## Advanced EBooks

### Deep Learning With Python

#### Develop Deep Learning Models on Theano and TensorFlow Using Keras

Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. Discover exactly how to get started and apply deep learning to your own machine learning projects.

TOP SELLER### Deep Learning for Computer Vision

#### Image Classification, Object Detection, and Face Recognition in Python

Deep learning methods can achieve state-of-the-art results on challenging computer vision problems such as image classification, object detection, and face recognition. With clear explanations, standard Python libraries, and step-by-step tutorial lessons, you’ll discover how to develop deep learning models for your own computer vision projects.

### Generative Adversarial Networks with Python

#### Deep Generative Models for Image Synthesis and Image Translation

Generative Adversarial Networks are a type of deep learning generative model that can achieve startlingly photorealistic results on a range of image synthesis and image-to-image translation problems. With clear explanations, standard Python libraries, and step-by-step tutorial lessons, you’ll discover how to develop GANs for your own computer vision projects.

NEW### Better Deep Learning

#### Train Faster, Reduce Overfitting, and Make Better Predictions

Deep learning neural networks have become easy to define and fit, but are still hard to configure. Discover exactly how to improve the performance of deep learning neural network models on your predictive modeling projects. Focus on techniques for faster learning including batch normalization, techniques for less overfitting such as weight decay and dropout, and techniques for better prediction such as stacking ensembles.

### Long Short-Term Memory Networks With Python

#### Develop Sequence Prediction Models With Deep Learning

Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what LSTMs are, and how to develop a suite of LSTM models to get the most out of this modern deep learning algorithm on your sequence prediction problems.

### Deep Learning for Time Series Forecasting

#### Predict the Future With MLPs, CNNs, and LSTMs in Python

With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects.

### Deep Learning for Natural Language Processing

#### Develop Deep Learning Models for Natural Language in Python

Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects.

### XGBoost With Python

#### Gradient Boosted Trees With XGboost and scikit-learn

XGBoost is the dominant technique for predictive modeling on tabular data. The gradient boosting algorithm is the top technique on a wide range of predictive modeling problems, and XGBoost is the fastest implementation. When asked, the best machine learning competitors in the world recommend using XGBoost.

## EBook Bundles

### Math Bundle

*(get a massive 29% discount)*

This 3-book set includes:

- Linear Algebra for Machine Learning
- Statistical Methods for Machine Learning
- Probability for Machine Learning

### Beginner Bundle

*(get a massive 34% discount)*

This 2-book set includes:

- Master Machine Learning Algorithms
- Machine Learning Mastery With Weka

### Python Machine Learning Bundle

*(get a massive 32.33% discount)*

This 6-book set includes:

- Machine Learning Algorithms from Scratch With Python
- Machine Learning Mastery With Python
- Data Preparation for Machine Learning
- Imbalanced Classification With Python
- XGBoost With Python
- Time Series Forecasting With Python

### R Bundle

*(get a massive 33% discount)*

This 2-book set includes:

- Master Machine Learning Algorithms
- Machine Learning Mastery With R

### Deep Learning Bundle

*(get a massive 30% discount)*

This 7-book set includes:

- Deep Learning With Python
- Deep Learning for Computer Vision
- Deep Learning for Natural Language Process
- Deep Learning for Time Series Forecasting
- Generative Adversarial Networks with Python
- Long Short-Term Memory Networks
- Better Deep Learning

### Super Bundle

*(get a massive 31.63% discount)*

This 19-book set includes all currently available EBooks!

- Linear Algebra for Machine Learning
- Statistical Methods for Machine Learning
- Probability for Machine Learning
- Master Machine Learning Algorithms
- Machine Learning Algorithms From Scratch
- Machine Learning Mastery With Weka
- Machine Learning Mastery With Python
- Machine Learning Mastery With R
- Data Preparation for Machine Learning
- Imbalanced Classification with Python
- Introduction to Time Series Forecasting With Python
- Deep Learning With Python
- Long Short-Term Memory Networks With Python
- Deep Learning for Natural Language Processing
- Deep Learning for Computer Vision
- Deep Learning for Time Series Forecasting
- Generative Adversarial Networks with Python
- Better Deep Learning
- XGBoost With Python

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