The Tools and Techniques That You Need To
Get Really Good at Applied Machine Learning, Really Fast
Get The 21-book Set
Your progress in applied machine learning is limited by the systematic processes you follow and the tools you choose.
- Linear Algebra for Machine Learning Basics of Linear Algebra for Machine Learning: Discover the Mathematical Language of Data in Python
- Statistical Methods for Machine Learning Statistical Methods for Machine Learning: Discover how to Transform Data into Knowledge with Python
- Probability for Machine Learning Probability for Machine Learning Discover How To Harness Uncertainty With Python
- Optimization for Machine Learning
- Master Machine Learning Algorithms Master Machine Learning Algorithms Discover How They Work and Implement Them From Scratch
- Machine Learning Algorithms From Scratch
- Machine Learning Mastery With Weka
- Machine Learning Mastery With Python Machine Learning Mastery With Python: Understand Your Data, Create Accurate Models and Work Projects End-to-End
- Machine Learning Mastery With R Machine Learning Mastery With R: Get Started, Build Accurate Models And Work Through Projects Step-by-Step + code
- Data Preparation for Machine Learning
- Imbalanced Classification With Python
- Time Series Forecasting With Python
- Deep Learning With Python Deep Learning With Python: Develop Deep Learning Models on Theano and TensorFlow using Keras
- Deep Learning for Computer Vision
- Deep Learning for Natural Language Processing Deep Learning for Natural Language Processing: Develop Deep Learning Models for Natural Language in Python
- Deep Learning for Time Series Forecasting Deep Learning for Time Series Forecasting – Predict the Future with MLPs, CNNs and LSTMs in Python
- Generative Adversarial Networks with Python Generative Adversarial Networks with Python
- LSTM Networks with Python
- Better Deep Learning
- XGBoost With Python XGBoost With Python: Discover The Algorithm That Is Winning Machine Learning Competitions
- Ensemble Learning Algorithms With Python
Statistics is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it.
Cut through the equations, Greek letters, and confusion, and discover the topics in statistics that you need to know.
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.
About the Ebook:
PDF format Ebook.
6 parts, covering the main topics.
29 step-by-step tutorials.
86 Python (.py) code files included.
Basics of Linear Algebra for Machine Learning: Discover the Mathematical Language of Data in Python
Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it.
In this new laser-focused Ebook written in the friendly Machine Learning Mastery style that you’re used to, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know.
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.
Introducing My New Ebook:
“Basics of Linear Algebra for Machine Learning“
Welcome to the “Basics of Linear Algebra for Machine Learning”
I designed this book to teach machine learning practitioners, like you, step-by-step the basics of linear algebra with concrete and executable examples in Python.
Basics of Linear Algebra for Machine Learning
Who Is This Book For?
…so is this book right for YOU?
This book is for developers that may know some applied machine learning.
Maybe you know how to work through a predictive modeling problem end-to-end, or at least most of the main steps, with popular tools.
The lessons in this book do assume a few things about you, such as:
You may know your way around basic Python for programming.
You may know some basic NumPy for array manipulation.
You want to learn linear algebra to deepen your understanding and application of machine learning.
This guide was written in the top-down and results-first machine learning style that you’re used to from Machine Learning Mastery.
What if I Am New to Machine Learning?
This book does not assume you have a background in machine learning.
That being said, I do recommend that you learn how to work through a predictive modeling problem first. It will give you the context for linear algebra. Otherwise the topic will feel too abstract.
What if I Am Just a Developer?
Perfect. I wrote this book for you.
What if My Math is Really Poor?
Maybe you learned linear algebra a long time ago back in school?
Maybe you never covered linear algebra before.
Perfect. This book is for you. I assume you know some basic arithmetic, and even then I give you a refresher.
What if I Am Not a Python Programmer?
You can handle this book if you are a programmer in another language, even if you are not experienced in Python.
Everything is demonstrated with a small code example that you can run directly.
All code is provided for you to play with, modify, and learn from.
I even show you how to manipulate NumPy arrays from first principles, because that is how we do linear algebra in Python.
The book even has an appendix to show you how to set up Python on your workstation.
What if I Am Working Through a Linear Algebra Course at a University?
This book is not a substitute for an undergraduate course in linear algebra or a textbook for such a course, although it is a great complement to such materials.
About Your Outcomes
…so what will YOU know after reading this book?
After reading and working through this book, you will know:
What linear algebra is and why it is relevant and important to machine learning.
How to create, index, and generally manipulate data in NumPy arrays.
What a vector is and how to perform vector arithmetic and calculate vector norms.
What a matrix is and how to perform matrix arithmetic, including matrix multiplication.
A suite of types of matrices, their properties, and advanced operations involving matrices.
What a tensor is and how to perform basic tensor arithmetic.
Matrix factorization methods, including the eigendecomposition and singular-value decomposition.
How to calculate and interpret basic statistics using the tools of linear algebra.
How to implement methods using the tools of linear algebra such as principal component analysis and linear least squares regression.
This new basic understanding of linear algebra will impact your practice of machine learning.
After reading this book, you will be able to:
Read the linear algebra mathematics in machine learning papers.
Implement the linear algebra descriptions of machine learning algorithms.
Describe your machine learning models using the notation and operations of linear algebra.
What Exactly Is in This Book?
This book was designed to be a crash course in linear algebra for machine learning practitioners. Ideally, those with a background as a developer.
This book was designed around major data structures, operations, and techniques in linear algebra that are directly relevant to machine learning algorithms.
There are a lot of things you could learn about linear algebra, from theory to abstract concepts to APIs. My goal is to take you straight to developing an intuition for the elements you must understand with laser-focused tutorials.
I designed the tutorials to focus on how to get things done with linear algebra. They give you the tools to both rapidly understand and apply each technique or operation.
Each tutorial is designed to take you about one hour to read through and complete, excluding the extensions and further reading.
You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book is intended to be read and used, not to sit idle.
I would recommend picking a schedule and sticking to it.
The tutorials are divided into five parts:
Part 1: Foundation. Discover a gentle introduction to the field of linear algebra and the relationship it has with the field of machine learning.
Part 2: NumPy. Discover NumPy tutorials that show you how to create, index, slice, and reshape NumPy arrays, the main data structure used in machine learning and the basis for linear algebra examples in this book.
Part 3: Matrices. Discover the key structures for holding and manipulating data in linear algebra in vectors, matrices, and tensors.
Part 4: Factorization. Discover a suite of methods for decomposing a matrix into its constituent elements in order to make numerical operations more efficient and more stable.
Part 5: Statistics. Discover statistics through the lens of linear algebra and its application to principal component analysis and linear regression.
Probability for Machine Learning: Discover How To Harness Uncertainty With Python
Probability is the bedrock of machine learning. You cannot develop a deep understanding and application of machine learning without it.
Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know.
Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.
Master Machine Learning Algorithms Discover How They Work and Implement Them From Scratch
Finally Pull Back The Curtain And See How They Work With
Clear Descriptions, Step-By-Step Tutorials and Working Examples in Spreadsheeds
You must understand the algorithms to get good (and be recognized as being good) at machine learning.
In this mega Ebook is written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.
Clear Descriptions and Step-By-Step Tutorials
Ebook with 163 pages in PDF format.
10 top algorithms described with clear descriptions.
12 step-by-step tutorials with worked examples.
16 spreadsheets with working implementations.
You Learn Best By Implementing Algorithms From Scratch
…But You Need Help With The First Step: The Math
Developers Learn Fast By Trying Things Out…
I’m a developer and I feel like I don’t really understand something until I can implement it from scratch. I need to understand each piece of it in order to understand the whole. The same thing applies to machine learning algorithms.
If you are anything like me, you will not feel comfortable about machine learning algorithms until you can implement them from scratch, step-by-step.
The Math Can Really Slow You Down (…and Sap Your Motivation)
The problem is, machine learning algorithms are not like other algorithms you may have implemented like sorting. They are always described using complex mathematics with a mixture of probability, statistics and linear algebra.
You need to be able to get past the mathematical descriptions in order to implement the algorithms from scratch, but you don’t have the time to spend 3 years studying mathematics to get there.
You Really Need Clear Worked Examples (…step-by-step with real numbers)
Machine learning algorithms would be much easier to understand if someone simplified the math and gave clear worked examples showing how real numbers get plugged into the equations and what numbers to expect as outputs. With clear inputs and outputs we as developers can reproduce and understand the math.
Even better would be to have worked examples that actually perform all of the calculation required to learn a model from a small sample dataset, and all of the calculations required to make predictions from the learned model.
Master Machine Learning Algorithms is for Developers
….with NO Background in Math
…and LOTS of Interest in Machine Learning
Introducing the “Master Machine Learning Algorithms” Ebook. This Ebook was carefully designed to provide a gentle introduction of the procedures to learn models from data and make predictions from data 10 popular and useful supervised machine learning algorithms used for predictive modeling.
Each algorithm includes a one or more step-by-step tutorials explaining exactly how to plug in numbers into each equation and what numbers to expect as output. These tutorials will guide you step-by-step through the processes for creating models from training data and making predictions.
More than that, each tutorial is designed to be completed in a spreadsheet. Spreadsheets are the simplest way to automate calculations and anyone can use a spreadsheet, from beginners, to professional developers to hard core programmers.
If you can understand how a machine learning algorithm works in a spreadsheet then you really know how it works. You can then implement it in any programming language you wish or use your newfound knowledge and understanding to achieve better performance from the algorithms in practice.
Everything You Need To Know About 10 Top Machine Learning Algorithms
You Will Get:
6 Import Background Lessons
11 Clear Algorithm Descriptions
12 Step-By-Step Algorithm Tutorials
This ebook was written around two themes designed to help you understand machine learning algorithms as quickly as possible.
These two parts are Algorithm Descriptions and Algorithm Tutorials:
Algorithm Descriptions: Discover exactly what each algorithm is and generally how it works from a high-level.
Algorithm Tutorials: Climb inside each machine learning algorithm and work through a case study to see how it learns and makes predictions.
Machine Learning Mastery With Python: Understand Your Data, Create Accurate Models and Work Projects End-to-End
The Python ecosystem with scikit-learn and pandas is required for operational machine learning.
Python is the rising platform for professional machine learning because you can use the same code to explore different models in R&D then deploy it directly to production.
In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply machine learning using the Python ecosystem. You get:
178 Page PDF Ebook.
74 Python Recipes using scikit-learn and Pandas.
16 Step-by-Step Lessons.
3 End-to-End Projects.
Machine Learning Mastery with Python is for Developers
….with a little Background in Machine Learning
…and LOTS of Interest in Making Accurate Predictions and Delivering Results
I have carefully designed this Ebook for developers that already know a little background in machine learning and who are interested in discovering how to make accurate predictions and deliver results with machine learning on the Python ecosystem.
Introducing your guide to applied machine learning with Python.
You will discover the step-by-step process that you can use to get started and become good at machine learning for predictive modeling with the Python ecosystem including:
Python 3.6 (or 2.7)
This book will lead you from being 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.
After reading this ebook you will know…
How to deliver a model that can make accurate predictions on new unseen data.
How to complete all subtasks of a predictive modeling problem with Python.
How to learn new and different techniques in Python and SciPy.
How to work through a small to medium sized dataset end-to-end.
How to get help with Python machine learning.
You will know which Python modules, classes and functions to use for common machine learning tasks.
From here you can start to dive into the specifics of the functions, techniques and algorithms used with the goal of learning how to use them better in order to deliver more accurate predictive models, more reliably in less time.
Harness The Rising Power of Python for Machine Learning
The Python ecosystem is growing and may become the dominant platform for machine learning.
The reason is because Python is a general purpose programming language (unlike R or Matlab). This means that you can use the same code for research and development to figure out what model to run as you can in production.
The cost and maintenance efficiencies and benefits of this fact cannot be understated.
Everything You Need To Know to Apply Machine Learning in Python
You Will Get:
16 Lessons on Python Best Practices for Machine Learning Tasks and
3 Project Tutorials that Tie it All Together
This Ebook was written around two themes designed to get you started and using Python for applied machine learning effectively and quickly.
Machine Learning Mastery With R: Get Started, Build Accurate Models And Work Through Projects Step-by-Step
R has been the gold standard in applied machine learning for a long time. Surveys show that it is the most popular platform used by professional data scientists. It is also preferred by the best data scientists in the world.
In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, learn how to get started, practice and apply machine learning using the R platform.
224 Page PDF Ebook.
14 step-by-step tutorial lessons.
3 end-to-end projects.
85 R scripts.
You Need R to Really Kick Ass at Applied Machine Learning
…But You Don’t Want to Deep-Dive into Theory or Language Syntax
Professional developers can pick-up R fast…
As a developer, you know how to pick up a new programming language quickly. Once you know how to define a function, use some loops and look-up at the API documentation, you’re off.
You have no interest in spending days or weeks of your time learning the intricate syntax of yet another language – especially when that language looks like every other one you’ve ever used.
When you already know some machine learning, R is a super power…
As someone who knows a little machine learning, you know that what matters in applied predictive modeling is working through problems systematically. Through careful trial and error you must discover the data transforms and algorithms that are best for your dataset.
You have no interest in yet another slow and plodding introduction to machine learning.
You really need to know how R maps onto the tasks of a machine learning project…
What you really need is a clear and straight forward presentation of how to complete each step of an applied machine learning project using the best packages and functions on the R platform.
Introducing Machine Learning Mastery With R.
In this new Ebook, Machine Learning Mastery With R will break down exactly what steps you need to do in a predictive modeling machine learning project and walk you through step-by-step exactly how to do it in R.
With the help of 3 larger end-to-end project tutorials and a reusable project template, you will tie all of the steps back together and confidently know how to complete your own machine learning projects. The true fact of the matter is this:
When Machine Learning in R is Done Right,
It Makes Working Through Projects Shockingly… Fast and Fun!
There’s a reason that R is the most popular platform for applied machine learning for professional data scientists. What do you think that reason is?
Why would someone choose to use a language where a strange arrow operator (<-) is used for assignment?
Why would professionals put up with 20 ways to do each task, when other platforms offer just one?
Why would data scientists invest so much time into reading the documentation for third-party R packages when other platforms have much better doco?
Any ideas why?
R is a like a candy shop… for data scientists
For applied machine learning the R platform is like a candy shop with rows and rows of thousands of colorful sweets to try. There are packages and functions for every possible algorithm, statistical method and technique you have heard of (and hundreds you haven’t).
R is the power tool of power tools… for machine learning
But R is also like a massive Tesla coil with huge bolts of electricity arching, bagging and popping above your head, and you’re at the controls. Academics are developing and releasing state-of-the-art machine learning algorithms as R packages all the time. With a few simple lines of code you can download these algorithms first, before any other platform, and run them on your data.
Use machine learning algorithms in the way that the people that thought them up intended. No waiting around for a sleepy development team to wake up, hear about the algorithm and eventually port it across. It’s ready for you to use, right there in your R interactive environment.
Machine Learning Mastery With R Is Designed for Fast Moving
Developers that Already Know a Little Machine Learning Like You…
So what is the missing gap here?
The gap is that you don’t know how to get started with R. You may have tried watching videos. You may have tried a tutorial or two. You may have even tried another book. Everyone has an idea on the parts, but now one is putting it all together…
You need a complete solution… lessons on the parts and end-to-end projects
To bridge the gap between a burning desire to use R for machine learning and actually delivering accurate predictions reliably on project after project you need to stop trying to work from bits and pieces. You need a complete solution.
You need to know what the professionals know. Without investing years of your life figuring it all out.
Everything You Need to Know to Work Through Predictive Modeling Projects in R
You Will Get:
14 Lessons on Machine Learning with R
3 Project Tutorials that Tie it All Together
This ebook was written around two themes designed to get you started and using machine learning with R effectively and quickly.
These two parts are Lessons and Projects:
Lessons: Learn how the sub-tasks of applied machine learning map onto the R and the best practice way of working through each task.
Projects: Tie together all of the knowledge from the lessons by working through case study predictive modeling problems.
Deep Learning for Time Series Forecasting – Predict the Future with MLPs, CNNs and LSTMs in Python
Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.
In this new Ebook written in the friendly Machine Learning Mastery style that you’re used to, skip the math and jump straight to getting results.
With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to devel
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.
In this mega Ebook is written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply deep learning to your own machine learning projects
Deep Learning for Natural Language Processing: Develop Deep Learning Models for Natural Language in Python
Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another.
In this new laser-focused Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math, research papers and patchwork descriptions about natural language processing.
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.
Generative Adversarial Networks with Python
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.
In this new Ebook written in the friendly Machine Learning Mastery style that you’re used to, skip the math and jump straight to getting results.
With clear explanations, standard Python libraries, and step-by-step tutorial lessons, you’ll discover how to develop Generative Adversarial Networks for your own computer vision projects.
XGBoost With Python: Discover The Algorithm That Is Winning Machine Learning Competitions
Why Is XGBoost So Powerful?
… the secret is its “speed” and “model performance”
The Gradient Boosting algorithm has been around since 1999. So why is it so popular right now?
The reason is that we now have machines fast enough and enough data to really make this algorithm shine.
Academics and researchers knew it was a dominant algorithm, more powerful than random forest, but few people in industry knew about it.
This was due to two main reasons:
The implementations of gradient boosting in R and Python were not really developed for performance and hence took a long time to train even modest sized models.
Because of the lack of attention on the algorithm, there were few good heuristics on which parameters to tune and how to tune them.
Naive implementations are slow, because the algorithm requires one tree to be created at a time to attempt to correct the errors of all previous trees in the model.
This sequential procedure results in models with really great predictive capability, but can be very slow to train when hundreds or thousands of trees need to be created from large datasets.
XGBoost Changed Everything
XGBoost was developed by Tianqi Chen and collaborators for speed and performance.
Tianqi is a top machine learning researcher, so he knows deeply how the algorithm works. He is also a very good engineer, so he knows how to build high-quality software.
This combination allowed him to combine his talents and re-frame the interns of the gradient boosting algorithm in such a way that it can exploit the full potential of the memory and CPU cores of your hardware.
In XGBoost, individual trees are created using multiple cores and data is organized to minimize the lookup times, all good computer science tips and tricks.
The result is an implementation of gradient boosting in the XGBoost library that can be configured to squeeze the best performance from your machine, whilst offering all of the knobs and dials to tune the behavior of the algorithm to your specific problem.
This Power Did Not Go Unnoticed
Soon after the release of XGBoost, top machine learning competitors started using it.
More than that, they started winning competitions on sites like Kaggle. And they were not shy about sharing the news about XGBoost.