Watch this Python tutorial for beginners to learn Python programming from scratch π
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Python Tutorial for Beginners (2019) - Learn Python for Machine Learning and Web Development
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TABLE OF CONTENT
00:00:00 Introduction
00:01:49 Installing Python
00:06:10 Your First Python Program
00:08:11 How Python Code Gets Executed
00:11:24 How Long It Takes To Learn Python
00:13:03 Variables
00:18:21 Receiving Input
00:22:16 Python Cheat Sheet
00:22:46 Type Conversion
00:29:31 Strings
00:37:36 Formatted Strings
00:40:50 String Methods
00:48:33 Arithmetic Operations
00:51:33 Operator Precedence
00:55:04 Math Functions
00:58:17 If Statements
01:06:32 Logical Operators
01:11:25 Comparison Operators
01:16:17 Weight Converter Program
01:20:43 While Loops
01:24:07 Building a Guessing Game
01:30:51 Building the Car Game
01:41:48 For Loops
01:47:46 Nested Loops
01:55:50 Lists
02:01:45 2D Lists
02:05:11 My Complete Python Course
02:06:00 List Methods
02:13:25 Tuples
02:15:34 Unpacking
02:18:21 Dictionaries
02:26:21 Emoji Converter
02:30:31 Functions
02:35:21 Parameters
02:39:24 Keyword Arguments
02:44:45 Return Statement
02:48:55 Creating a Reusable Function
02:53:42 Exceptions
02:59:14 Comments
03:01:46 Classes
03:07:46 Constructors
03:14:41 Inheritance
03:19:33 Modules
03:30:12 Packages
03:36:22 Generating Random Values
03:44:37 Working with Directories
03:50:47 Pypi and Pip
03:55:34 Project 1: Automation with Python
04:10:22 Project 2: Machine Learning with Python
04:58:37 Project 3: Building a Website with Django
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Using Scikit-Learn for Machine Learning Application Development in Python - Python is arguably the best programming language for machine learning. However, many aspiring machine learning developers donβt know where to start...
Using Scikit-Learn for Machine Learning Application Development in Python - Python is arguably the best programming language for machine learning. However, many aspiring machine learning developers donβt know where to start...
They should look into the scikit-learn library, which is one of the best for developing machine learning applications. It is free and relatively easy to install and learn.
Why Machine Learning Programmers Should Be Familiar With Scikit-LearnIf you are trying to develop machine learning applications, then you were going to need a robust toolkit. Scikit-learn is just the solution that you need. This library was developed in 2007 as part of a Google project. Three years later, the code was released as hey solution for machine learning algorithms in conjunction with Google and several other major companies.
Scikit-learn is a library that contains several implementations of machine learning algorithms. There are two essential classifiers for developing machine learning applications with this library: a supervised learning model known as an SVM and a Random Forest (RF).
There are numerous reasons that scikit-learn is one of the preferred libraries for developing machine learning solutions. Some of the Premier benefits include:
Scikit-learn has been used in a number of applications by J.P. Morgan, Spotify, Inria, and other major companies. Machine learning applications built with scikit-learn include financial cybersecurity analytics, product development, neuroimaging, barcode scanner development, medical modeling and help with handling Shopify inventory issues.
The wide range of decision modeling features makes scikit-learn. One of the most versatile machine learning environments available in any programming language. Intermediate and advanced Python programmers should be able to master the nuances of this sophisticated library in a matter of hours.
The scikit-learn library is not installed by default. Fortunately, you should be able to set it up quickly. Here are some guidelines for installation and creating the foundation for your first machine learning project.
Installation of Scikit-LearnIf you already have pip installed, it's very easy to install the scikit-learn library. The instructions are available on this page.
Data for AudioThe purpose of using classification is to create a model based on the representation of a phenomenon in vector form (i.e. as a vector) and its corresponding class. This model will then be used to assign a class to an unknown vector. MFCCs can be used for approximations of sound vectors. MFCC provides 13 values per window. One option is to try classifying the class of a sound using those values. However, the sequences of the sound are very important.
This approach resolves some vector problems. The first approach we can follow is to take a segment of MFCCs and average them. Rather than having 13 values for the size of the segment, we end up with thirteen values. Averaging them is very simple, but we can get other statistics, such as: standard deviations and quartiles. This strategy provides statistical representations of all variables.
Loading Data From a CSV FileYou will save your scikit-learn data in CSV files. Each line represents a line and each regular column represents a dimension of the vector. In general, the latter represents the class. Rows are separated by a line break and columns by a column. An illustrative example would be as follows:
#!events
event_1,event_2, event_label
1,2,3
11.1,1221,11341
1322,1422,320
330,222,121
To upload a file you can execute the following code:
import numpy as np
.loadtxt('scikit_1.csv',)
data.shape
At the end of this code, the variable data contains our data. The file scikit_1.csv contains segment data..
Separating Different Data TypesIn order to learn a model, we need to follow the methodology presented at the beginning. We are not going to be able to follow it to the letter, but we are going to do our best to make our model the best. The first step is to hide some examples to consider them as evidence.
scikit learn prefers separate data between dimensions and classes.
Here is the code that accomplishes this step:
[:,:2233]
[:,-3]
The first line brings $2233$ dimensions of our vectors (in this case we are ignoring those derived from these data). The data will be stored in the variable $First_variable$. The variable $Second_variable$ stores the classes (all lines, last column).
Scikit learn contains a function that allows separating the training data from the test data, and this is done automatically and shuffles the data randomly that supports our methodology.
We have four sets; two versions of the dimension data we generally call features and two versions of the classes. One version is for training (train), and another for testing (test). The train versions have half of the original data, while testing the other half.
Learn Python 3 from scratch to become a developer in demand
Description
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*************************** No Prior Coding Experience Needed ***************************
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Each lecture consist of a video screencast and code files
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Basic knowledge
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What will you learn
Understand the concepts of Object Oriented Programming Language
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Use Python Language to develop back-end of Web Applications
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