Important Note: Please if you want to buy it, go to the bundle with the same price, and get the whole series.

In this part, you will learn the pre-requisites linear algebra that needed for both of machine learning and linear algebra, you will study both of vectors and matrices and the operations that can be use in both of vectors and matrices. For example, addition, summation, subtraction and multiplication, also we will study advanced operations. With the theories we will give you the implementation of all these operations from scratch, to enhance your skills in both of linear algebra and coding too.

You can check the first part A Refresher Guide to Convolution Neural Networks here

About In-weekend Series?

As I have a regular job, I have to go to company and work on solving problem 5 Days in a Week, and as I should increase my knowledge to have a life progress, I need to study and learn more and more, but when I arrive to home everyday, I look like a disaster and I can’t even open my eyes, so the only time I have to study are in the holiday/weekends. In those two days I have to study and have time with my family. So, I got the idea of effectively time management, to manage and keep use of every minute of those two days, in both studying and having family time. So, every week at work I take 10 minute to design and build what I should learn and study on the weekend, and when the weekend comes I start finishing what I have built. And from this the idea came to me to build a series to help you to progress your skill while you have no time to do that.

What to Learn on the Weekend?

• Getting Started with Python,
• Managing Data Frames with Pandas package, 
• Exploratory Data Analysis Checklist, 
• Principles of Analytic Graphics, 
• Exploratory Graphs, 
• Plotting Systems, 
• Graphics Devices, 
• The Base Plotting System,

Table of Content of this Weekend.

• 0. Table of Content
• 1. Stay in Touch!
• 2. Preface
• 2.1 What is Exploratory Data Analysis?
• 2.2 What does this book cover?
• 2.3 What you should expect?
• 2.4 About In-weekend Series?
• 2.5 What to Learn on the Weekend?
• 3. Getting Started with Python
• 3.1 What is Python?
• 3.1 Installation
• 3.1.1 Python 2 vs. Python 3
• 3.1.2 Installation steps   
• 3.1.3 Python packages   
• 3.2 IPython and Jupyter
• 3.2.1 IPython    
• 3.2.2 Jupyter    
• 3.2.3 Installation
• 3.2.4 What is an ipynb File?    
• 4. Managing Data Frames with the Pandas package   
• 4.1 Data Frames   
• 4.2 The Pandas Package
• 4.3 The Pandas Grammar
• 4.4 Installing the Pandas Package
• 4.5 selecting methods    
• 4.6 filter method    
• 4.7 sort_values method
• 4.8 rename method    
• 4.9 assign method    
• 4.10 groupby method  
• 4.11 method chaining
• 4.12 Summary    
• 5. Exploratory Data Analysis Checklist
• 5.1 formulate your question    
• 5.2 reading data    
• 5.3 check the shaping
• 5.4 describe and info
• 5.5 Look at the head and the tail of your data   
• 5.6 Check your “n”s    
• 5.7 Validate with at least one external data source    
• 5.8 Try the easy solution first    
• 5.9 Challenge your solution    
• 5.10 Follow up questions    
• 6. Principles of Analytic Graphics    
• 6.1 Show comparisons    
• 6.2 Show causality, mechanism, explanation and systematic structure    
• 6.3 Show multivariate data    
• 6.4 Integrate evidence    
• 6.5 Describe and document the evidence    
• 6.6 Content, Content, and Content    
• 6.7 References    
• 7. Exploratory Graphs    
• 7.1 Characteristics of exploratory graphs    
• 7.2 Survival rate in Titanic dataset    
• 7.3 Getting the Data    
• 7.4 Simple Summaries: One Dimension    
• 7.5 Five Number Summary    
• 7.6 Boxplot    
• 7.7 Histogram  
• 7.8 Overlaying Features    
• 7.9 Barplot
• 7.10 Simple Summaries: Two Dimensions and Beyond
• 7.11 Multiple Boxplots
• 7.12 Multiple Histograms
• 7.13 Scatterplots
• 7.14 Scatterplot - Another Shape
• 7.15 Multiple Scatterplots
• 7.16 Summary
• 8. About the Author

By reading this book you will learn how to build a machine learning pipeline for a real-life projects, whatever stopped you before from mastering machine learning with python you can easily overcome it with this book, because of easy step-by-step, and example-oriented approach that will help you apply the most straightforward and effective tools to both demonstrative and real-world problems and datasets.

Note: This book is for free and and will always be, so get your copy and we will be glade if you supported us by either with your feedback or some donation.

This book will cover the following:

Part one: Introduction

1. an introduction to what is data science tools and how to setup it.
2. an introduction to data science pipelines and define it and how to scale it.
3. an introduction to machine learning pipelines and how learning is done.
4. building a small project to make sure that you are now understand the meaning of pipelines.

Part two: Data

1. defining data, types of data and levels of data, because it will help us to understand the data.
2. understand and cleaning data process, since it's a very important step in the pipeline
3. resampling data to create train-set and test-set, and splitting techniques.
4. feature engineering and selection, and that's because not all time the needed variable is visible to us.

Part three: supervised leaning

an introduction to machine learning algorithms, how it works, and it's evaluation. And this part will cover the following algorithms:

1. Linear Regression.
2. Logistic Regression.
3. Decision Trees.
4. Support Vector Machines.