THE TRAINING PROGRAM OF MACHINE LEARNING WITH PYTHON
The Training Program of Machine Learning with Python
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Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

Machine
The Training Program of Machine Learning with Python


Python for Data Analysis

For many people, the Python language is easy to fall in love with. Since its first appearance in 1991, Python has become one of the most popular dynamic, programming languages, along with Perl, Ruby, and others.

Python and Ruby have become especially popular in recent years for building websites using their numerous web frameworks, like Rails (Ruby) and Django (Python). Such languages are often called scripting languages as they can be used to write quick-and-dirty small programs, or scripts.

I don’t like the term “scripting language” as it carries a connotation that they cannot be used for building mission-critical software. Among interpreted languages Python is distinguished by its large and active scientific computing community.

Adoption of Python for scientific computing in both industry applications and academic research has increased significantly since the early 2000s. For data analysis and interactive, exploratory computing and data visualization.

Python will inevitably draw comparisons with the many other domain-specific open source and commercial programming languages and tools in wide use, such as R, MATLAB, SAS, Stata, and others. In recent years, Python’s improved library support (primarily pandas) has made it a strong alternative for data manipulation tasks.

Combined with Python’s strength in general purpose programming, it is an excellent choice as a single language for building data-centric applications.

Python for Machine Learning

Machine learning (ML) teaches machines how to carry out tasks by themselves, it is that simple. The complexity comes with the details, and that is most likely the reason you are reading this book. Maybe you have too much data and too little insight, and you hoped that using machine learning algorithms will help you solve this challenge.

So you started to  dig into random algorithms. But after some time you were puzzled: which of the myriad of algorithms should you actually choose? Or maybe you are broadly interested in machine learning and have been reading  a few blogs and articles about it for some time.

The goal of machine learning is to teach machines to carry out tasks by providing them with a couple of examples (how to do or not do a task). Let us assume that each morning when you turn on your computer, you perform the  same task of moving e-mails around so that only those e-mails belonging to a particular topic end up in the same folder.

After some time, you feel bored and  think of automating this chore. One way would be to start analyzing your brain  and writing down all the rules your brain processes while you are shuffling your e-mails. However, this will be quite cumbersome and always imperfect.

While you will miss some rules, you will over-specify others. A better and more future-proof way would be to automate this process by choosing a set of e-mail meta information and body/folder name pairs and let an algorithm come up with the best rule set.

The pairs would be your training data, and the resulting rule set (also called model) could then be applied to future e-mails that we have not yet seen. This is machine learning in its simplest form. Of course, machine learning (often also referred to as data mining or predictive analysis) is not a brand new field in itself.

Quite the contrary, its success over recent years can be attributed to the pragmatic way of using rock-solid techniques and insights from other successful fields; for example, statistics. There, the purpose is for us humans to get insights into the data by learning more about the underlying patterns and relationships.

As you read more and more about successful applications of machine learning (you have checked out kaggle.com already, haven't you?), you will see that applied statistics is a common field among machine learning experts. As you will see later, the process of coming up with a decent ML approach is never a waterfall-like process.

Instead, you will see yourself going back and forth in your analysis, trying out different versions of your input data on diverse sets of ML algorithms. It is this explorative nature that lends itself perfectly to Python. Being an interpreted high-level programming language, it may seem that Python was designed specifically for the process of trying out different things.

What is more, it does this very fast. Sure enough, it is slower than C or similar statically-typed programming languages; nevertheless, with a myriad of easy-to-use libraries that  are often written in C, you don't have to sacrifice speed for agility.

Table of Content


  • Module 1: The Python Programming Language
  • 1. Introduction
    • videocam
      History of Python language

      11m26s
    • videocam
      Install Python on MacOS

      11m26s
    • videocam
      Install Python on Ubuntu

      11m26s
    • videocam
      Install Python on Windows

      11m26s
  • 2. Python Basic
    • videocam
      Syntax and data types

      11m26s
    • videocam
      Number

      11m26s
    • videocam
      String

      11m26s
    • videocam
      List

      11m26s
    • videocam
      Dictionary

      11m26s
    • videocam
      Tuple

      11m26s
    • videocam
      Control statements in Python

      11m26s
  • 3. String and Text
    • videocam
      Split string

      11m26s
    • videocam
      Regular Expressions

      11m26s
    • videocam
      Unicode string

      11m26s
    • videocam
      Removes characters in the string

      11m26s
    • videocam
      Format text in columns

      11m26s
    • videocam
      Convert strings to bytes

      11m26s
  • 4. Numbers-Dates and Times
    • videocam
      Rounding the number

      11m26s
    • videocam
      Performing Accurate Decimal Calculations

      11m26s
    • videocam
      Formatting Numbers

      11m26s
    • videocam
      Working with Infinity and NaNs

      11m26s
    • videocam
      Calculating with Fractions

      11m26s
    • videocam
      Performing Matrix

      11m26s
    • videocam
      Generate random number

      11m26s
    • videocam
      Converting DateTime

      11m26s
    • videocam
      Manipulating Dates Time Zones

      11m26s
  • 5. File and I/O
    • videocam
      Reading and Writing Text Data

      11m26s
    • videocam
      Reading and Writing Binary Data

      11m26s
    • videocam
      Reading and Writing Compressed File

      11m26s
    • videocam
      Reading Binary Data into a Mutable Buffer

      11m26s
    • videocam
      Memory Mapping Binary Files

      11m26s
    • videocam
      Getting a Directory Listing

      11m26s
    • videocam
      Writing Bytes to a Text File

      11m26s
    • videocam
      Communicating with Serial Ports

      11m26s
    • videocam
      Serializing Python Objects

      11m26s
  • 6. Data Encoding and Processing
    • videocam
      Reading and Writing CSV Data

      11m26s
    • videocam
      Reading and Writing JSON Data

      11m26s
    • videocam
      Parsing Simple XML Data

      11m26s
    • videocam
      Parsing Huge XML Files Incrementally

      11m26s
    • videocam
      Convert a Dictionary into XML

      11m26s
    • videocam
      Parsing XML Document

      11m26s
    • videocam
      Interacting with a Relational Database

      11m26s
    • videocam
      Decoding and Encoding Hexadecimal Digits

      11m26s
    • videocam
      Decoding and Encoding Base64

      11m26s
    • videocam
      Reading and Writing Binary Arrays of Structures

      11m26s
  • 7. Functions Programming
    • videocam
      Writing Functions That Accept Any Number of

      11m26s
    • videocam
      Writing Functions That Only Accept Keyword

      11m26s
    • videocam
      Attaching Informational Metadata to Function

      11m26s
    • videocam
      Returning Multiple Values from a Function

      11m26s
    • videocam
      Defining Functions with Default Arguments

      11m26s
    • videocam
      Defining Anonymous or Inline Functions

      11m26s
    • videocam
      Capturing Variables in Anonymous Functions

      11m26s
    • videocam
      Replacing Single Method Classes with Functions

      11m26s
    • videocam
      Carrying Extra State with Callback Functions

      11m26s
    • videocam
      Inlining Callback Functions

      11m26s
    • videocam
      Accessing Variables Defined Inside a Closure

      11m26s
  • 8. Classes and Objects
    • videocam
      Changing the String Representation of Instances

      11m26s
    • videocam
      Saving Memory When Creating a Large Number of

      11m26s
    • videocam
      Encapsulating Names in a Class

      11m26s
    • videocam
      Inheritance in Python

      11m26s
    • videocam
      Polymorphism in Python

      11m26s
    • videocam
      Creating Managed Attributes

      11m26s
  • 9. GUI Programming
    • videocam
      Creating our first Python GUI

      11m26s
    • videocam
      GUI programming with tkinter

      11m26s
    • videocam
      Widget

      11m26s
    • videocam
      Layout Management

      11m26s
    • videocam
      Controls in GUI

      11m26s
    • videocam
      Matplotlib Charts

      11m26s
  • 10. Network and Web Programming
    • videocam
      HTTP Services As a Client

      11m26s
    • videocam
      Creating a TCP Server

      11m26s
    • videocam
      Creating a UDP Server

      11m26s
    • videocam
      Transfer file with FTP

      11m26s
  • 11. Multitasking in Python
    • videocam
      Starting and Stopping Threads

      11m26s
    • videocam
      Determining If a Thread Has Started

      11m26s
    • videocam
      Communicating Between Threads

      11m26s
    • videocam
      Locking Critical Sections

      11m26s
    • videocam
      Locking with Deadlock Avoidance

      11m26s
    • videocam
      Storing Thread-Specific State

      11m26s
    • videocam
      Creating a Thread Pool

      11m26s
    • videocam
      Performing Simple Parallel Programming

      11m26s
    • videocam
      Dealing with the GIL

      11m26s
    • videocam
      Implementing Publish/Subscribe Messaging

      11m26s
    • videocam
      Polling Multiple Thread Queues

      11m26s
  • 12. Testing - Debugging and Exceptions
    • videocam
      Testing Output Sent to stdout

      11m26s
    • videocam
      Patching Objects in Unit Tests

      11m26s
    • videocam
      Logging Test Output to a File

      11m26s
    • videocam
      Handling Multiple Exceptions

      11m26s
    • videocam
      Catching All Exceptions

      11m26s
    • videocam
      Creating Custom Exceptions

      11m26s
    • videocam
      Debugging Program Crashes

      11m26s
    • videocam
      Profiling and Timing Your Program

      11m26s
    • videocam
      Making Your Programs Run Faster

      11m26s
  • Module 2: Data Analysis and Machine Learning with Python
  • 1. Introduction
    • videocam
      The tasks to do in this course

      11m26s
    • videocam
      Install Development Environment

      11m26s
  • 2. Interactive Computing with IPython
    • videocam
      IPython Basics

      11m26s
    • videocam
      The commands in IPython

      11m26s
    • videocam
      Interacting with the OS

      11m26s
    • videocam
      Debug with pdb

      11m26s
    • videocam
      Advanced IPython Features

      11m26s
  • 3. Arrays and Vectorized Computation
    • videocam
      Introduction to NumPy

      11m26s
    • videocam
      Multidimensional Array Object

      11m26s
    • videocam
      Fast Element-wise Array Functions

      11m26s
    • videocam
      Data Processing Using Arrays

      11m26s
    • videocam
      File Input and Output with Arrays

      11m26s
    • videocam
      Linear Algebra

      11m26s
    • videocam
      Random Number Generation

      11m26s
  • 4. Data Analysis with pandas
    • videocam
      Introduction to pandas Data Structures

      11m26s
    • videocam
      Essential Functionality

      11m26s
    • videocam
      Summarizing and Computing Descriptive Statistics

      11m26s
    • videocam
      Handling Missing Data

      11m26s
    • videocam
      Hierarchical Indexing

      11m26s
    • videocam
      Advanced pandas

      11m26s
  • 5. Data Loading, Storage, and File Formats
    • videocam
      Reading and Writing Data in Text Format

      11m26s
    • videocam
      Binary Data Formats

      11m26s
    • videocam
      Interacting with HTML and Web APIs

      11m26s
    • videocam
      Interacting with Databases

      11m26s
  • 6. Data Wrangling
    • videocam
      Combining and Merging Data Sets

      11m26s
    • videocam
      Reshaping and Pivoting

      11m26s
    • videocam
      Data Transformation

      11m26s
    • videocam
      String Manipulation

      11m26s
  • 7. Plotting and Visualization
    • videocam
      Matplotlib APIs

      11m26s
    • videocam
      Plotting Functions in pandas

      11m26s
    • videocam
      Example Visualizing Earthquake Crisis Data

      11m26s
    • videocam
      Visualization Tool Ecosystem

      11m26s
  • 8. Data Aggregation and Group Operations
    • videocam
      GroupBy Mechanics

      11m26s
    • videocam
      Data Aggregation

      11m26s
    • videocam
      Group-wise Operations and Transformations

      11m26s
    • videocam
      Pivot Tables and Cross-Tabulation

      11m26s
  • 9. Time Series
    • videocam
      Date and Time Data Types

      11m26s
    • videocam
      Time Series Basics

      11m26s
    • videocam
      Date Ranges Frequencies and Shifting

      11m26s
    • videocam
      Time Zone Handling

      11m26s
    • videocam
      Periods and Period Arithmetic

      11m26s
    • videocam
      Resampling and Frequency Conversion

      11m26s
    • videocam
      Time Series Plotting

      11m26s
    • videocam
      Moving Window Functions

      11m26s
    • videocam
      Performance and Memory Usage

      11m26s
  • 10. Financial and Economic Data
    • videocam
      Time Series and Cross-Section Alignment

      11m26s
    • videocam
      Operations with Time Series of Different Frequencies

      11m26s
    • videocam
      Time of Day and Data Selection

      11m26s
    • videocam
      Splicing Together Data Sources

      11m26s
    • videocam
      Return Indexes and Cumulative Returns

      11m26s
    • videocam
      Group Transforms and Analysis

      11m26s
  • 11. Advanced NumPy
    • videocam
      ndarray Object Internals

      11m26s
    • videocam
      Advanced Array Manipulation

      11m26s
    • videocam
      Broadcasting

      11m26s
    • videocam
      Structured and Record Arrays

      11m26s
    • videocam
      Sorting

      11m26s
    • videocam
      NumPy Matrix Class

      11m26s
    • videocam
      Advanced Array I/O

      11m26s
  • 12. Big Data with Python
    • videocam
      Introducing big data

      11m26s
    • videocam
      Hadoop for big data

      11m26s
    • videocam
      Apache Hadoop

      11m26s
    • videocam
      Example in Hadoop

      11m26s
    • videocam
      Hadoop for finance

      11m26s
    • videocam
      Introducing NoSQL

      11m26s
    • videocam
      MongoDB and PyMongo

      11m26s
  • 13. Getting Started with Python Machine Learning
    • videocam
      Machine learning and Python

      11m26s
    • videocam
      A simple example machine learning

      11m26s
    • videocam
      Linear regression algorithm

      11m26s
    • videocam
      Training a linear regression model

      11m26s
    • videocam
      Recursive polynomial algorithm

      11m26s
    • videocam
      Training a recursive polynomial model

      11m26s
    • videocam
      Support Vector Machine Regression

      11m26s
    • videocam
      The Decision Tree Algorithm

      11m26s
    • videocam
      Random forest algorithm

      11m26s
  • 14. Classification in Machine Learning
    • videocam
      Logistic Regression

      11m26s
    • videocam
      K-Nearest Neighbor Classifier

      11m26s
    • videocam
      Support Vector Machine

      11m26s
    • videocam
      Kernel Support Vector Machine

      11m26s
    • videocam
      Naive Bayes Classifier

      11m26s
    • videocam
      Tree Based Algorithms

      11m26s
    • videocam
      Random Forest Classifier

      11m26s
    • videocam
      K-means clustering

      11m26s
    • videocam
      Hierarchical Clustering in Python

      11m26s
  • 15. Artificial Neural Networks
    • videocam
      Introduction to ANN

      11m26s
    • videocam
      Mathematical basis of ANN

      11m26s
    • videocam
      Perceptron neural network

      11m26s
    • videocam
      The Backpropagation Algorithm

      11m26s
    • videocam
      Building an ANN

      11m26s
    • videocam
      Training an ANN

      11m26s
  • 16. TensorFlow Framework
    • videocam
      Introduction to TensorFlow

      11m26s
    • videocam
      TensorFlow APIs

      11m26s
    • videocam
      Building an ANN with TensorFlow

      11m26s
    • videocam
      Training an ANN with TensorFlow

      11m26s
  • 17. Practical Projects
    • videocam
      Handwriting Recognition with Python

      11m26s
    • videocam
      Image Recognition with Python

      11m26s
    • videocam
      Natural Language Processing with Python

      11m26s
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